Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Inf. Model. 2020, 60, 11, 5580-5594

Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method

Alexander D. Wade

Alexander D. Wade

TCM Group, Cavendish Laboratory, University of Cambridge, 19 J J Thomson Avenue, Cambridge CB3 0HE, United Kingdom

More by Alexander D. Wade

http://orcid.org/0000-0003-1500-3733
and
David J. Huggins*

David J. Huggins

Tri-Institutional Therapeutics Discovery Institute, Belfer Research Building, 413 East 69th Street, 16th Floor, Box 300, New York, United States

Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, New York 10065, United States

*E-mail: [email protected]
More by David J. Huggins

http://orcid.org/0000-0003-1579-2496

Cite this: J. Chem. Inf. Model. 2020, 60, 11, 5580–5594

Publication Date (Web):August 18, 2020

https://doi.org/10.1021/acs.jcim.0c00610

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Abstract

In this work, a novel method to rationally design inhibitors with improved steric contacts and enhanced binding free energies is presented. This new method uses alchemical single step perturbation calculations to rapidly optimize the van der Waals interactions of a small molecule in a protein–ligand complex in order to maximize its binding affinity. The results of the optimizer are used to predict beneficial growth vectors on the ligand, and good agreement is found between the predictions from the optimizer and a more rigorous free energy calculation, with a Spearman’s rank order correlation of 0.59. The advantage of the method presented here is the significant speed up of over 10-fold compared to traditional free energy calculations and sublinear scaling with the number of growth vectors assessed. Where experimental data were available, mutations from hydrogen to a methyl group at sites highlighted by the optimizer were calculated with MBAR, and the mean unsigned error between experimental and calculated values of the binding free energy was 0.83 kcal/mol.

SPECIAL ISSUE

This article is part of the Novel Directions in Free Energy Methods and Applications special issue.

Introduction

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Free energy methods have become a valuable tool in rational drug design. (1) With the increased computational power from hardware accelerated computer resources, (2−4) particularly GPUs, (5−7) allowing for longer simulation times and the advances in biological force field accuracy, (8−11) it is now practical to make accurate predictions of binding free energy changes. (1,12−18) However, protein–ligand binding free energy is a complex balance of enthalpic and entropic terms in both the bound and unbound systems. (19) Designing molecules to effectively tip this balance is extremely challenging to achieve by eye, and intuition about what changes to the ligand might improve the binding free energy is often incorrect. (19) The task of finding beneficial mutations can be made more systematic by employing methods such as pharmacophore modeling, which have become more accurate with the inclusion of dynamic trajectories. (20) The great advantage of free energy methods, however, is their ability to evaluate the balance of enthalpic and entropic terms quantitatively and provide reliable information for what changes to a ligand might improve the binding affinity experimentally.

A significant part of a drug design campaign is ideation. This is the process of generating ideas which may lead to improved properties, for example, binding affinity (21) or selectivity. (22) Free energy methods can be a useful tool to predict whether an ideated drug does have an improved binding affinity, and free energy methods have been widely applied to study ideated compounds. (21−25) However, the application of free energy methods to idea generation has seen much less attention. While it is possible to exhaustively test a large set of ligands around a starting hit molecule as part of an ideation campaign these searches are not directed, instead relying on trial and error. This wastes a lot of time testing “bad” ideas, similar to the inefficiencies of experimental campaigns. Since free energy methods are computational, there exists flexibility in how to approach the problem of finding ligands with improved properties. (26) Here, we consider a reframing of the problem as a numerical optimization.

The binding free energy that is calculated for a ligand is a function of the parameters of the system, such as atomic partial charge. Tidor et al. used this idea in previous work (27,28) to perform charge optimizations of ligands, that is, finding a set of atomic partial charges which minimize the binding free energy. These optimizations used Poisson–Boltzmann calculations in the bound and unbound ligand systems to calculate an optimal set of charges which improve binding affinity. (29) More recently, the authors of this paper have extended these methods including explicit water models and single step perturbation (SSP). (30) SSP is a type of free energy perturbation (FEP) and exists among a myriad of other free energy methods. (31−36) FEP methods are based on the Zwanzig equation, as shown in eq 1:

(1)

with k as Boltzmann’s constant, T as temperature, E₁ and E₂ as the potential energies of the system calculated using the Hamiltonian of states 1 and 2, and ⟨...⟩₁ as a state average over system 1. It is typical in free-energy calculations (34,35) to sample from many intermediate states between the two end states. This is done to increase the sampling overlap between adjacent states and improve convergence. However, this adds computational time as molecular dynamics (MD) simulations must be performed for every state sampled. If the perturbation between end states remains small enough, then eq 1 can be applied effectively without any intermediate states. Using sampling from only one end state is referred to as a unidirectional transformation, and this is the defining characteristic of the SSP method. Numerous studies have used SSP (37−40) with applications of this method made to relative free energy calculations (30,41−43) and demonstrations made that it can be significantly faster than standard FEP approaches. (30,43,44) While not explored in this work it is worth mentioning that in the literature there exist methods to improve the performance of SSP methods. These involve sampling of an unphysical central reference state which has improved overlap to all end states. (45−49)

Another important free energy method used in this work is the multistate Bennett acceptance ratio (MBAR) estimator. (35) MBAR is a generalization of the Bennett acceptance ratio BAR (34) method to multiple intermediate alchemical states. Both SSP and MBAR are used in this work to calculate relative binding free energies. SSP will be applied to calculations where the end states are relatively close in parameter space and MBAR applied when these end states are further apart. In general, to reduce the size of the perturbation needed to calculate a ligand–protein binding free energy, it is common to use a relative binding free energy (1,24,25) calculation as opposed to an absolute one, (50) where a relative calculation is the change in binding free energy between two different ligands in the same protein. Tools such as replica exchange (51,52) can be used to improve the convergence properties of these calculations, and accuracy with respect to the experiment can be improved using approaches that, for example, consider buried water molecules (53−55) or using multiscale modeling. (56,57)

Method

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The goal of this work is to optimize the steric parameters of a ligand in a protein–ligand complex and use the results of this optimization to predict beneficial growth vectors on the ligand more efficiently than existing FEP methods. To this end, experimental data have been collected to retrospectively validate this method from the following systems: androgen receptor (AR), (58,59) renin, (60) menin, (61) thrombin, (62) and SARS PL protease. (63,64) These systems were chosen as they contain data for free energy differences for changes from hydrogens to methyl groups. Two calculations were performed with menin using two different ligands. These are termed menin A and B. Four calculations were performed for the thrombin systems using four different ligands. These are termed thrombin A, B, C, and D. All ligands used in this work are shown in Table 1.

Table 1. Ligands Used in Androgen Receptor, SARS PL Protease, Renin, Menin, and Thrombin Systems

System Setup

Protein structures are taken from the PDB IDs given in Table 2. The common steps in preparation of these structures using Schrödinger’s Preparation Wizard (65) are as follows: replace selenomethionines with methionines, add missing side chains, add all hydrogens, assign tautomers, and assign the protonation state of all ionizable residues. All buffer solvents and ions were removed. Force field parameters and partial charges were assigned from the AMBER ff14SB force field. (10) Since we are using an AMBER force field, the Lorenz–Berholet combination rules will be used where relevant. Parameters for the inhibitors were generated using Antechamber (66) with AMBER GAFF 2 (67) and AM1-BCC. (68) Water was modeled with the SPC/E water model. A salt concentration of 150 mM and any required counterions were added using sodium and chloride ions. In every case, the edge of the solvation box was set to be 10 Å from any atom of the receptor or ligand. The SI contains detailed information on the preparation specific to each protein.

Table 2. PDB IDs for Each System Used in This Work

system	PDB ID
androgen receptor	2NW4 (59)
SARS PL Pro	3E9S (64)
renin	3OOT (60)
menin	4OG6 (61)
thrombin	2ZNK (62)

Molecular Dynamics

All simulations were performed with OpenMM 7.3.0 as follows. First, OpenMM’s default minimizer was used to minimize all structures. Equilibration was then performed in the NPT ensemble at 300 K and 1 atm using a Langevin integrator and Monte Carlo barostat for at least 200 ps. MD simulations were performed in the NPT ensemble using a time step of 2 fs; van der Waals interactions were truncated at 10.0 Å. Electrostatics were modeled using the particle mesh Ewald method with a cutoff of 10.0 Å. Snapshots were collected every 5 ps, and all simulations were performed with constrained hydrogens. Since all hydrogens are constrained, we assume that hydrogen bond oscillations contribute negligibly to any calculated free energy changes. This protocol was used throughout this work; please see the Supporting Information (SI) for MD settings not provided here.

Workflow

In this work, several free energy methods were used in different contexts. For clarity, we will now outline these methods for future reference. After models for the protein ligand system were built, we performed an optimization of the van der Waals parameters of an inhibitor in the protein ligand complex. This is done using a gradient descent (GD) algorithm with a line search. In order to perform this optimization, two calculations are needed: one for the objective and one for the gradient. The objective was calculated with MBAR. We therefore name this the MBAR objective, and the gradient was calculated with SSP. We therefore name this the SSP gradient. The details of how these calculations are performed can be found in the Optimization, SSP Gradient, and MBAR Objective sections below. Once this optimization is converged, the output is analyzed to determine growth vectors which should be beneficial. We then calculate the relative binding free energy of adding methyl groups at the locations specified by the optimizer. This calculation is performed using MBAR with Hamiltonian replica exchange. We name these calculations FEP scans, with more details in the FEP Scans section below. Figure 1 illustrates how each of these calculations are combined in the full workflow.

Optimization

Optimizations were performed using the Ligand-Optimiser code, which is available on Github at https://github.com/adw62/Ligand-Optimiser. The optimizations were applied to the σ parameter of the Lennard-Jones (LJ) potential shown in eq 2.

(2)

Here, r is the distance between two atoms, ε is the depth of the potential, and σ is the distance between atoms at which the potential is zero. Note that this method is agnostic to the exact form of the LJ potential used and the exact meaning of σ in that form. The objective function for the optimization performed in this work is defined in eq 3.

(3)

ΔG_binding(σ_i) is the binding free energy difference between the bound and unbound states of the ligand and receptor for a ligand with i atoms using σ_i parameters. ΔG_original is the binding free energy for the ligand using its original parameters. This is a constant for each given system.

To perform this optimization, an implementation of the gradient descent algorithm with a line search was used. The line search used a step size of 0.6 nm and a convergence tolerance of 0.15 nm in σ. The objective function in eq 3 can be considered as the difference in binding free energies between two ligands with binding free energy ΔG_binding(σ_i) and ΔG_original. This difference is a relative binding free energy ΔΔG_binding. This ΔΔG_binding can be constructed equivalently as the difference of ΔΔG_binding in the bound and unbound states or the difference of ΔG_mutation in the bound and unbound states, where ΔG_mutation is defined as the free energy difference of mutating one ligand into the other. We used the latter approach as is standard best practice in relative binding free-energy calculations; (14) see Figure S13 for a graphical depiction of this cycle.

MBAR Objective

To evaluate the objective, we used an MBAR method to calculate ΔΔG_binding. We term this evaluation of ΔΔG_binding as ΔΔG_opt. The objective was calculated by simulating 24 alchemical windows. The two end states were (a) the system with the Hamiltonian using the sigmas for the current optimization step n and (b) the system with the Hamiltonian using the sigmas from optimization step n + 1. The intermediate states are created by linearly interpolating the sigma parameters only. Charge, bond, angle, and torsion parameters do not change. The sigmas for the optimization step n + 1 were determined with the standard GD algorithm using a maximum step size of 0.6 nm. Sampling from all windows was collected, and the potentials evaluated from this sampling were combined using the MBAR estimator giving ΔΔG_mutation^opt. Combining ΔΔG_mutation^opt from the bound and unbound states gave ΔΔG_opt across all lambda windows. The lambda windows of the free energy calculation were treated as a line search in sigma space, and therefore the lambda window with the minimum in ΔΔG_opt was selected and the sigma parameters corresponding to that lambda window became the accepted parameters for the next step of optimization. If the minimum was found to be at the last lambda value, then the line search was extended by another 0.6 nm without recalculating the gradient. Each evaluation of the objective used 6 ns of sampling in AR, SARS PLPro, and menin A systems. Renin, menin B, and the four thrombin systems used 12 ns, as these optimizations were observed taking steps which correspond to larger values of ΔΔG_opt. Figure 2 shows an example for how ΔΔG_opt varies with sampling.

Figure 2. Convergence for a calculation of the objective in the androgen receptor test case. ΔΔG_opt is reported as mean of three replicates with shaded area showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees of freedom, and SEM is the standard error of the mean computed from the sample standard deviation of the three independent replicate predictions.

SSP Gradient

Methods exist in the literature which detail the calculation for the analytic gradient of the potential (69) or ensemble properties (70,71) of a system with respect to some argument of the potential. In this work, however a numerical finite difference will be used to calculate the gradient. The gradient of the objective with respect to sigma was calculated as shown in eq 4.

(4)

where h is a finite difference of 0.00015 nm. The numerator on the RHS of eq 4 is a ΔΔG_binding and can be calculated using an SSP approach, using sampling only from a central state containing the ligand using σ_i values. This calculation of the gradient shows the advantage of SSP. As numerous (tens to hundreds) evaluations of the RHS of eq 4 are required, one for each dimension in σ space, they can all be calculated from the sampling of one central state. The number of dimensions in σ space comes from the number of atoms in the molecule. See Figure 3 where each named hydrogen on the molecule is optimized. Figure 3 shows the ligand from the androgen receptor test case where there are 10 named hydrogens and so a 10-dimensional σ space.

Figure 3. 3D structure for the androgen receptor ligand with all the optimized hydrogens explicitly labeled.

The finite difference in eq 4 is between molecules that are extremely similar, differing only by 0.00015 nm in one atom’s σ. There is therefore likely to be a large sampling overlap between these states allowing SSP to be applied. Throughout this work, the gradient was calculated from 5 ns of sampling in the central reference state. Figure 4 shows an example for how this gradient varies with sampling for the androgen receptor ligand.

Figure 4. Convergence for a calculation of the gradient in the androgen receptor test case. ΔΔG_grag/h is reported as the mean of three replicates with the shaded area showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees. Atom names in the legend can be seen in Figure 3.

Optimization Validation

To test the reproducibility of the optimization, we examined how the optimized values for the sigma of each hydrogen varied across repeats. Three repeats of an optimization for the androgen receptor were made, and the averaged set of optimized sigmas are presented in Table 3. We can see from Table 3 that the variance in the optimized σ is small relative to the difference between original and optimized σ values.

Table 3. Original and Optimized Sigmas for Every Atom in the Androgen Receptor Ligand Alongside the Variance^a

hydrogen name	original σ [nm]	average optimized σ [nm]	variance optimized σ [nm²]
H5	0.263	0.154	0.006
H7	0.263	0.301	0.000
H15	0.242	0.032	0.001
H17	0.242	0.245	0.000
H191	0.260	0.285	0.001
H192	0.260	0.427	0.002
H221	0.242	0.233	0.000
H222	0.242	0.296	0.000
H3	0.263	0.381	0.002
H2	0.263	0.264	0.001

^{^a}

Averages and variances are taken from three repeats. All hydrogen names are given in Figure 3.

To ensure each optimization is converged, the cumulative ΔΔG_opt is plotted over optimization steps. These convergence data are presented in Figure 5, where it can be seen that the optimizations in each case are well converged.

Figure 5. Cumulative ΔΔG_opt calculated by summing the MBAR objective for each step of the optimization plotted against optimization step for every system.

Additional calculations were made to verify the value of the cumulative ΔΔG_opt. This involved calculating the relative binding free energy between the original and optimized parameters of the inhibitor with MBAR. The mean absolute error between the values of ΔΔG_opt and the verification calculation was 1.42 kcal/mol. Details and results of this calculation can be seen in the SI (Figures S1–S3 and Table S2).

FEP Scans

The results of the optimization were analyzed, and the best growth vectors on the ligand were determined. The best growth vectors were tested using the following protocol named FEP scans which used the MBAR method. The binding free energy associated with these FEP scans will be termed ΔΔG_scan. ΔΔG_scan values were calculated using OpenMMTools (72) to collect sampling using Hamiltonian replica exchange. Free energy changes were calculated from this sampling using the MBAR estimator. Each Hamiltonian in the replica exchange was an intermediate state in the alchemical transformation using a hybrid topology. For these intermediate states, the charge, van der Waals, bond, angle, and torsion parameters are all interpolated between the end states. A soft core potential is used to interpolate the LJ potential for these FEP scan calculations. In this work, we focus on transforming hydrogens to methyls. This makes the exploration of the chemical space tractable. Transformations to methyl at all possible sites allow for a thorough validation of the optimizations because a more complete comparison can be made between the ranking of growth vectors by the optimization and the FEP scans. Exhaustive testing of all methyls is done here for the purpose of validation. If this method is applied in a drug discovery effort, only the top growth vectors ranked highly by the optimizer would need to be tested using FEP scans.

To make a hydrogen disappear and a methyl appear, each FEP scan used 33 ns of sampling split across 22 alchemical windows, except renin, which used 55 ns, as this was observed to require more sampling to converge. Hamiltonian swapping was performed every 5 ps. All calculations were performed in triplicate. In Figure 6, we show the convergence for making all possible hydrogen to methyl mutations on the androgen receptor ligand. All remaining convergence graphs for the FEP scan calculations can be seen in Figures S4–S11.

Figure 6. ΔΔG_scan for each methylation in the androgen receptor system as the amount of sampling is increased. ΔΔG_scan are reported as mean of three replicates with shaded area showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees of freedom, and SEM is the standard error of the mean computed from the sample standard deviation of the three independent replicate predictions. All hydrogen names are taken from Figure 3.

When trying to calculate all hydrogen to methyl mutations, we encountered some methyls which introduced numerical instability into the simulation. The causes of these instabilities in the SARS PLPro, menin, and thrombin test cases were in very close contact between an existing part of the ligand and the added methyl. In the androgen receptor test case, the cause was close contact between the protein and the added methyl. We show the androgen receptors case in Figure S12. Any ΔΔG_scan that could not be calculated due to numerical instability is given an NA value in the results.

Summary of Methods

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To summarize, in this work, an optimization of the sigma parameters of several ligands was performed. The objective of this optimization was to find a set of sigmas to minimize the relative binding free energy of the ligand to a protein. This objective, ΔΔG_opt, was evaluated using FEP calculations and the MBAR estimator. To calculate the gradient, ΔΔG_grad, SSP calculations were used. The results of the optimizer are used to predict beneficial growth vectors on the ligand. To validate these predicted growths, hydrogen to methyl mutations were computed, ΔΔG_scan, using FEP and the MBAR estimator. For the readers’ reference, Table 4 contains all the abbreviated terms used and a brief description of the calculation.

Table 4. Abbreviated Terms Used to Reference Various Relative Binding Free Energies Calculated in This Work with a Brief Description of the Calculation for the Readers Reference^a

free energy value	description
ΔΔG_opt	the value for the objective, calculated between the original and optimized sigmas with MBAR
ΔΔG_grad	the value for the gradient, calculated between many highly related ligands using SSP
ΔΔG_scan	the value for one or many hydrogen to methyl mutations calculated with MBAR
ΔΔG_exp	the value for one or many hydrogen to methyl mutations determined experimentally

^{^a}

Both ΔΔG_opt and ΔΔG_grad are calculated with fixed C–H bond lengths due to the difficulty in implementing alchemical changes to OpenMM constraints. However, ΔΔG_scan values are calculated with correct bond lengths at both end states.

Results

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The results from the optimizations performed in this work will be considered one system at a time. First, looking at the androgen receptor, each optimization produces a set of optimized σ’s, which minimizes the binding free energy. We show this result with figures made such that any optimized hydrogens are sized in proportion to their calculated Δσ, where Δσ is the difference between the atoms optimized and original σ. One reason we create these figures is to allow the continuous values of σ in the optimization to be converted by the eye into the discrete values of σ associated with adding any atom(s). In the following calculations, we denote symmetry related positions with an obelisk symbol (†). For optimization calculations, no atoms are considered symmetric. This is because the optimizer assigns different sigmas to symmetric atoms, and this breaks any symmetry. In the context of these simulations, symmetric methyl hydrogens rapidly interconvert due to rotation, but symmetric hydrogens on aromatic rings do not. Thus, hydrogens on methyls are considered symmetric for the ΔΔG_scan calculations, but hydrogens on aromatic rings are not.

Figure 7 shows the relative size of the optimized hydrogens in the androgen receptor test case. The largest hydrogens are H192 and H3, with H3 as the position chosen in previous experimental work to be methylated. The values of Δσ from the optimization are tabulated alongside calculated values for ΔΔG_scan, and available experimental free energies are reported as ΔΔG_exp.

Figure 7. Androgen receptor ligand with all optimized hydrogens sized in proportion to their calculated Δσ.

The results in Table 5 show that, for the AR test case, the optimization and FEP scan rank the experimentally verified methylation, H3, second and first, respectively. The overall agreement in the ranking between computational methods can be calculated with the Spearman’s Rank-Order Correlation, ρ. Here, we calculated ρ between the ranking from the optimization and FEP Scan (any hydrogens without calculated value for ΔΔG_scan are ranked last), and for the AR test case, ρ was calculated as 0.7. The agreement between the experimental ΔΔG_exp and computational ΔΔG_scan is not good in this case, differing by more than 1 kcal/mol. Looking at the outlying data in Table 5, it can be seen that while the optimization ranks H2 as not beneficial, the FEP Scan calculated it to be a beneficial position for a methyl. We speculate that this is because during the optimization all σ values are changed simultaneously in the same system. This means that if both H2 and H3 grow simultaneously, they will see each other with increased radius in the simulation. It may be possible that some growths which are close in proximity can interfere with each other such that only one position grows to maximize the binding affinity. This effect would not be seen for the FEP Scan calculation as each methylation is a separate calculation and as such the methylation at H2 and H3 do not need to be accommodated simultaneously.

Table 5. Comparison of Δσ,ΔΔG_scan for Mutating a Hydrogen to Methyl for the Androgen Receptor Test Case Alongside Experimental Values ΔΔG_exp^a

hydrogen name	Δσ [nm]	ΔΔG_scan [kcal/mol]	ΔΔG_exp [kcal/mol]
H192	0.148	–2.32 [−3.14, −1.5]
H3	0.122	–3.78 [−3.91, −3.64]	–0.83
H191	0.041	–1.0 [−1.29, −0.7]
H222	0.041	–1.8 [−2.15, −1.45]
H7	0.038	–0.79 [−1.6, 0.02]
H17	–0.004	1.45 [0.56, 2.34]
H221	–0.004	–0.95 [−1.09, −0.82]
H2	–0.015	–2.28 [−2.5, −2.05]
H5	–0.187	–0.62 [−2.95, 1.71]
H15	–0.237	NA

^{^a}

ΔΔG_scan values are reported as the mean of three replicates with bracketed values showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees of freedom, and SEM is the standard error of the mean computed from the sample standard deviation of the three independent replicate predictions.

Figure 8 provides the result from the optimizer for the SARS PLPro system and shows that three growth vectors have been highlighted. Two of these growths, H4 and H9, are adjacent to each other pointing in approximately the same direction; the other, H10, is separately located on the ortho position of a phenyl. It is this ortho position that was the experimentally chosen position in previous work.

Figure 8. SARS PLPro ligand with all optimized hydrogens sized in proportion to their calculated Δσ.

Looking more closely at the SARS PLPro result in Table 6, it can be seen that the experimentally chosen hydrogen, H10, is ranked second by the optimizer and first by the FEP scan. H4, the position most favored by the optimizer, is confirmed to be a beneficial position for methylation by the FEP scan with a ΔΔG_scan of −0.45 [−1.09, 0.19]. For the SARS PLPro test case, the correlation in the ranking of growth vectors by the optimizer and FEP scan is good with ρ calculated to be 0.8. The experimental data which exist for the SARS PLPro systems is for ligands with methyls at positions H10/H13, H17/H19, and H21, but no reference data exist for the ligand with a hydrogen at all of these sites. To compare to the experiment, we therefore use the ΔΔG_scan values in Table 6 to calculate the relative binding free energy change for permuting the methyl on sites H10/H13, H17/H19, and H21 and present this result in Table 7.

Table 6. Comparison of Δσ, ΔΔG_scan for Mutating a Hydrogen to Methyl for the SARS PLPro Test Case^a

hydrogen name	Δσ [nm]	ΔΔG_scan [kcal/mol]	experimental rank
H4	0.270	–0.45 [−1.09, 0.19]
H10	0.115	–1.70 [−2.35, −1.05]	1
H9	0.115	–0.73 [−0.94, −0.53]
H18	0.047	–0.16 [−0.46, 0.13]
H19	0.028	–0.26 [−0.65, 0.14]	2
H22	0.013	–0.34 [−0.75, 0.07]
H21	0.002	–0.10 [−0.27, 0.08]	3
H12	–0.004	–0.18 [−0.67, 0.31]
H20	–0.007	–0.58 [−0.79, −0.36]
H13	–0.011	–1.14 [−1.17, −1.11]
H01	–0.020	0.34 [−0.02, 0.7]†
H013	–0.021	0.34 [−0.02, 0.7]†
H012	–0.023	0.34 [−0.02, 0.7]†
H14	–0.080	NA
H6	–0.103	NA
H17	–0.106	1.05 [0.89, 1.21]

^{^a}

ΔΔG_scan are reported as the mean of three replicates with bracketed values showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees of freedom, and SEM is the standard error of the mean computed from the sample standard deviation of the three independent replicate predictions. Symmetry related positions are indicated by a † symbol.

Table 7. ΔΔG_scan for Mutating a Methyl from One Position to Another on the Ligand^a

methyl mutation	ΔΔG_scan [kcal/mol]	ΔΔG_exp [kcal/mol]
H10 to H19	1.44 [0.68, 2.20]	0.32
H10 to H21	1.60 [0.93, 2.27]	0.72
H19 to H21	0.16 [−0.27, 0.59]	0.40

^{^a}

Experimental values ΔΔG_exp for methyl mutations in the SARS PLPro test case. ΔΔG_scan are reported as mean of three replicates with bracketed values showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees of freedom, and SEM is the standard error of the mean computed from the sample standard deviation of the three independent replicate predictions.

Table 7 shows that the ΔΔG_scan for moving the methyl from H19 to H21 is in good agreement with the experiment, within 1 kcal/mol. For the H10 to H19 and H10 to H21 transformations, disagreement with the experiment is greater than 1 kcal/mol. However, the ranking in Table 6, is much more accurate with the experimentally preferred methylation ranked first and second by the FEP scan and optimization, respectively.

The data for the renin system are detailed in Figure 9 and Table 8; from these data it can be seen that there is one clear growth vector highlighted by the optimizer H52. This is not in the direction chosen experimentally (H50/H54). There is, however, agreement between the optimizer and the FEP scan, which also ranked H52 first with a ΔΔG_scan of −1.56 [−1.85, −1.27]. This is an example where the ranking between the computational and experimental methods is not in agreement, but the rankings between computational methods is in agreement.

Figure 9. Renin ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

Table 8. Comparison for Δσ, ΔΔG_scan for Mutating a Hydrogen to Methyl and Any Experimental Values ΔΔG_exp for the Renin Test Case^a

hydrogen name	Δσ [nm]	ΔΔG_scan [kcal/mol]	ΔΔG_exp [kcal/mol]
H52	0.256	–1.56 [−1.85, −1.27]
H38	0.158	–1.05 [−1.28, −0.82]
H221	0.158	1.69 [1.01, 2.36]
H39	0.088	–0.79 [−1.46, −0.13]
H31	0.084	–0.36 [−0.76, 0.04]
H6	0.080	–0.31 [−0.54, −0.08]
H54	0.080	–0.28 [−0.49, −0.07]	–1.75
H222	0.077	0.67 [−0.56, 1.91]
H50	0.069	0.68 [0.26, 1.11]
H37	0.052	–0.94 [−1.14, −0.74]
H36	0.037	0.46 [0.07, 0.86]
H99	0.017	0.17 [−0.54, 0.87]
H40	–0.006	0.34 [−0.92, 1.59]
H191	–0.060	6.38 [3.89, 8.88]
H202	–0.065	1.65 [−0.33, 3.63]
H192	–0.067	5.8 [5.1, 6.51]
H201	–0.070	9.14 [8.35, 9.93]
H1	–0.089	1.42 [1.15, 1.7]
H53	–0.099	0.46 [0.08, 0.85]
H231	–0.102	1.78 [−3.86, 7.42]
H232	–0.176	0.7 [−2.3, 3.7]

^{^a}

The result for H221 in Table 8 is outlying, and there is not good agreement between the optimizer and the FEP scan. We speculate that this disagreement is caused by the disruption of a favorable charged interaction between the protein and the amine group adjacent to H221. These interactions may be disrupted by the methyl group added in the FEP scan but not by the small change in sigma seen during the optimization. Overall, the correlation in the ranking between the computational methods in the renin system is high with a calculated ρ of 0.7. The difference between the calculated and experimental binding affinity is between 1 and 2 kcal/mol.

The result for the menin A system can be seen in Figure 10 and Table 9. These results show that three growth vectors are highlighted: HAL, HAU2, and HAO2. Our method agrees with the experimental result that HBF is not a position which can beneficially accommodate a methyl group; this can be seen because the Δσ for HBF is not a large positive.

Figure 10. Menin A ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

Table 9. Comparison for Δσ, ΔΔG_scan for Mutating a Hydrogen to Methyl and Any Experimental Values ΔΔG_exp for the Menin A Test Case^a

hydrogen name	Δσ [nm]	ΔΔG_scan [kcal/mol]	ΔΔG_exp [kcal/mol]
HAU2	0.167	0.46 [−0.27, 1.2]
HAL	0.111	–1.57 [−2.24, −0.9]
HAO2	0.094	–0.49 [−1.19, 0.2]
HAV1	0.059	0.76 [−0.4, 1.92]
HAV2	0.051	0.78 [−0.66, 2.23]
HAW1	0.051	0.35 [−0.96, 1.65]
HAP1	0.033	1.62 [0.69, 2.54]
HAU1	0.021	1.95 [1.07, 2.84]
HAS1	0.009	–0.05 [−0.61, 0.52]
HAJ	0.006	0.72 [−0.58, 2.01]
HAP2	0.003	0.75 [0.3, 1.2]
HAS2	0.000	0.28 [−0.58, 1.14]
HBF	–0.001	–0.22 [−0.78, 0.34]	0.14
HAG	–0.001	–0.09 [−0.22, 0.05]
HAF	–0.003	0.16 [−0.55, 0.86]
HAH	–0.010	1.42 [1.0, 1.84]
H3	–0.017	0.83 [0.28, 1.38]
HAW2	–0.020	0.32 [−0.22, 0.85]
HAT2	–0.062	0.03 [−0.35, 0.41]
HAT1	–0.083	0.29 [0.23, 0.35]
HAI	–0.112	NA
HAK	–0.130	0.99 [−0.19, 2.17]
HAO1	–0.158	1.92 [0.72, 3.12]
HBD	–0.196	NA
HAE	–0.207	0.29 [−0.78, 1.36]

^{^a}

ΔΔG_scan are reported as mean of three replicates with bracketed values showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees of freedom, and SEM is the standard error of the mean computed from the sample standard deviation of the three independent replicate predictions.

It is worth noting that the HBF site is beneficial for groups larger than methyl such as isopropyl or cyclohexyl with a ΔΔG_exp of −2.41 or −2.94 kcal/mol, respectively. In its current form the optimization cannot provide information that this is a beneficial spot for larger mutations. The highlighted positions, HAL and HAO2, are in good agreement between the optimization and FEP Scan, ranked second and third by the optimizer and first and second by the FEP Scan. HAU2 is not agreed between the two computational methods. The overall rank correlation σ for the menin A system was 0.3. The experimental value for adding a methyl at HBF is in reasonable agreement with the experiment, with an experimental value of 0.14 kcal/mol compared to the ΔΔG_scan value of −0.22 [−0.78, 0.34]

As mentioned, the site at HBF in the test case menin A will beneficially accommodate mutations larger than a methyl and this will now be investigated with system menin B. The menin B ligand is the same as a menin A with a methyl added at HBF. When ligand menin B is optimized, the newly added hydrogens H55 and H54 are now found to be the most beneficial by the optimizer. This can be seen in Figure 11 where H55 and H44 are ranked first and second, see Table S3 for all values for optimized σ. The experimental data for growths of ligand menin B correspond to adding methyls to H54 and H55 simultaneously. Therefore, the ΔΔG_scan values calculated are now also for adding two methyls, one at H54 and one at H55. These ΔΔG_scan calculations for two methyls give a value of −3.11 [−4.02, −2.2] kcal/mol, which agrees well with the experimental value −2.55 kcal/mol.

Figure 11. Menin B ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

The final test case to consider is thrombin. In this test case, several mutations are strung together and the optimization method applied iteratively to build a more tightly binding inhibitor. We perform this test case as we imagine this method might be applied in a drug discovery setting. This means that not all methylations will be tested by computing ΔΔG_scan, in every round of optimization, only those which would contribute new information to the study. The goal here was to use information from the optimizer to move from thrombin A shown in Table 1, which has an experimental binding free energy of −7.1 kcal/mol, to a ligand with improved binding free energy.

Considering the result of the thrombin A optimization in Figure 12 and Table 10, it can be seen that H44 is ranked first by the optimization, and H27/H45/H48 are ranked first by the FEP Scan. The ranking of H17 is not in good agreement between the optimizer and the FEP scan. This may be a similar effect to that seen for the outlier in the renin test case, where perhaps in the FEP scan the added methyl is disrupting the charged interaction between the protein and the amine groups on the ligand near H17. Another possible cause for the disagreement might be explained by looking at H22. In the original thrombin A ligand, H17 is symmetric to H22, and the optimizer and FEP scan are in agreement that H22 is very unfavorable. Therefore, it may also be possible that in the FEP scan simulation H17 and H22 are adequately interconverting to converge at the solution that H17/H22 is an unfavorable position, but this interconversion is not occurring sufficiently for the optimization simulations, which are shorter. Overall, the ρ calculated between the ranking from the optimization and FEP Scan for the thrombin A system was 0.7. On the basis of the results of Figure 12 and Table 10, either H44 or H27/H45/H48 could be chosen for methylation, and here we show the result for methylating H44. The thrombin A ligand (Table 1) with a methyl added at H44 is the ligand thrombin B in Table 1, and so ligand thrombin B was then considered for an additional round of optimization. With the retrospective knowledge that the H44 is a beneficial location for an amine group the next round of optimization is performed for the partial charges of ligand thrombin B. The charge optimization used was identical to the steric optimization with the exception that the parameters of the optimization were the partial charges of the atoms instead of the atoms σ. The methodology to perform optimization has been explored in previous work. (30)

Figure 12. Thrombin A ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

Table 10. Comparison for Δσ, ΔΔG_scan for Mutating a Hydrogen to Methyl and Any Experimental Values ΔΔG_exp in the Thrombin A Test Case^a

hydrogen name	Δσ [nm]	ΔΔG_scan [kcal/mol]	ΔΔG_exp [kcal/mol]
H44	0.232	–0.57 [−0.89, −0.24]
H32	0.056	–0.07 [−0.36, 0.21]
H27	0.046	–1.75 [−2.66, −0.83]†
H25	0.045	–0.24 [−0.49, 0.02]	–0.24
H48	0.040	–1.75 [−2.66, −0.83]†
H33	0.033	0.34 [−0.65, 1.33]
H17	0.028	5.11 [3.78, 6.44]
H45	0.018	–1.75 [−2.66, −0.83]†
H16	0.012	–0.8 [−1.05, −0.55]
H24	0.003	NA
H26	–0.001	–0.76 [−1.21, −0.31]	–0.24
H31	–0.010	1.08 [0.79, 1.37]
H29	–0.032	2.7 [1.79, 3.6]
H23	–0.035	1.1 [0.86, 1.34]
H36	–0.075	5.45 [5.03, 5.87]
H34	–0.086	1.56 [1.47, 1.66]
H28	–0.136	2.94 [1.82, 4.06]
H30	–0.144	2.46 [1.49, 3.44]
H37	–0.153	3.56 [−0.66, 7.78]
H22	–0.333	6.09 [3.53, 8.65]

^{^a}

Figure 13 details the result of the charge optimization and shows that hydrogens H49 and H50 can be made more positive to improve the binding free energy. The total change in charge for H49, H50, and H51 made by the optimizer was +0.3e, with all changes in partial charge shown in Table S4. Adding a charged amine group at this position is known experimentally to be beneficial, giving a change in binding affinity of −2.06 kcal/mol. We therefore chose to add an amide group to ligand thrombin B (Table 1), which gave ligand thrombin C (Table 1). We then continued to optimize with another round for the thrombin C ligand. We are now once again optimizing the sigma parameters of ligand thrombin C, and the results of this optimization are presented in Figure 14 and Table 11.

Figure 13. Thrombin B ligand with all optimized hydrogens colored in proportion to their calculated Δq. Red is more positive and blue more negative. See all named hydrogens in Table S1.

Figure 14. Thrombin C ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

Table 11. Comparison for Δσ, ΔΔG_scan for Mutating a Hydrogen to Methyl and Any Experimental Values ΔΔG_exp in the Thrombin C Test Case^a

hydrogen name	Δσ [nm]	ΔΔG_scan [kcal/mol]	ΔΔG_exp [kcal/mol]
H45	0.305	–1.68 [−2.27, −1.09]†
H16	0.228	–0.54 [−0.87, −0.22]
H26	0.196	–0.52 [−1.11, 0.07]	–0.60
H24	0.138
H32	0.079
H33	0.038
H23	0.028
H27	0.007	–1.68 [−2.27, −1.09]†
H48	–0.001	–1.68 [−2.27, −1.09]†
H31	–0.003
H29	–0.027
H25	–0.029	–0.29 [−0.93, 0.34]	–0.60
H34	–0.040
H37	–0.098
H28	–0.099
H22	–0.109
H17	–0.153
H30	–0.159
H36	–0.180

^{^a}

In Table 11, only a subset of all possible ΔΔG_scan values is calculated; the calculated values of ΔΔG_scan are chosen in the areas where growth was made to the ligand experimentally. Looking at Figure 14 and Table 11, three growth vectors are highlighted: H45, H16, and H26, which are all confirmed to be beneficial by the FEP scan. The ρ calculated between the ranking from the optimization and FEP Scan for the thrombin C system was 0.6. The growth vector ranked first by the optimizer and the methylation scan was H45 with a ΔΔG_scan of −1.68 [−2.27, −1.09] kcal/mol. Since the optimizer and FEP scan both rank H45, first it was selected to be mutated to a methyl. Adding a methyl at H45 of ligand thrombin C (Table 1) gives ligand thrombin D (Table 1); we therefore performed a final round of optimization on ligand thrombin D.

Again, only a subset of all possible methylations are calculated in Table 12. With the calculated values of ΔΔG_scan chosen in the area, growths were made to the ligand experimentally. For the thrombin D test case, Figure 15 and Table 12 show that the optimizer ranks H16 and H26 as the first and second best growth vectors. The FEP scan ranks H26 as the best growth vector. The ρ calculated between the ranking from the optimization and FEP Scan for the thrombin C system was 0.4. On the basis of the information from the optimizer and FEP scan, H26 was selected to be methylated. To compare to the experiment, the ΔΔG_scan’s from thrombin C and thrombin D were combined. The ΔΔG_scan for H45 from thrombin C was combined with the ΔΔG_scan for methylations in thrombin D, which gave the free energy change for adding a methyl at both sites simultaneously; these calculations are presented in Table 13.

Figure 15. Thrombin D ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

Table 12. Comparison for Δσ, ΔΔG_scan for Mutating a Hydrogen to Methyl in the Thrombin D Test Case^a

hydrogen name	Δσ [nm]	ΔΔG_scan [kcal/mol]
H16	0.177
H26	0.145	–1.35 [−1.76, −0.95]
H24	0.098	1.57 [0.26, 2.87]
H53	0.089	–1.02 [−1.32, −0.72]†
H52	0.075	–1.02 [−1.32, −0.72]†
H23	0.019
H48	0.015	–0.64 [−1.13, −0.14]
H31	0.012
H54	0.008	–1.02 [−1.32, −0.72]†
H27	0.008	–0.67 [−0.74, −0.6]
H33	0.005
H25	0.000	–0.27 [−1.13, 0.6]
H32	–0.004
H29	–0.038
H34	–0.065
H22	–0.079
H28	–0.126
H17	–0.133
H37	–0.156
H30	–0.156
H36	–0.164

^{^a}

Table 13. Comparison for Δσ, ΔΔG_scan for Mutating Thrombin C H45 to a Methyl and One Other Named Hydrogen in Thrombin D to a Methyl and Any Experimental Values ΔΔG_exp for the Thrombin D Test Case^a

hydrogen name	ΔΔG_scan [kcal/mol]	ΔΔG_exp [kcal/mol]
H26	–3.03 [−3.74, −2.32]
H52	–2.7 [−3.36, −2.04]†
H53	–2.7 [−3.36, −2.04]†
H54	–2.7 [−3.36, −2.04]†
H27	–2.35 [−2.94, −1.76]	–1.20
H48	–2.32 [−3.09, −1.55]	–1.20
H25	–1.95 [−3.0, −0.9]
H24	–0.11 [−1.54, 1.32]

^{^a}

H27 and H48 are the positions methylated in the experimental work, and it can be seen in Table 13 that computationally these positions are also beneficial to adding a methyl. However, H27 and H48 are not ranked highly; instead H26 was ranked first. Comparing the ΔΔG_scan for H27 or H48 to the experimental values gives reasonable agreement with the experiment, within 1 kcal/mol for both H27 and H48.

Finally, when all the mutations selected in the thrombin optimization iterations are combined, the final ligand is presented in Figure 16. Figure 16 additionally presents an experimentally optimized ligand also originating from the ligand thrombin A in Table 1. It can be seen in Figure 16 that our optimized ligand is very similar to a ligand determined experimentally, differing only in the placement of one methyl group. The binding free energy of the experimental ligand is −10.4 kcal/mol, (62) we can use this value in combination with our calculation for removing a methyl at H27 and adding a methyl at H26 (Table 12) to estimate the binding free energy of our computationally optimized ligand. The binding free energy of our optimized ligand is calculated to be −11.1 [−10.7, −11.5] significantly better than the starting ligand (thrombin A), which had an experimental binding free energy of −7.1 kcal/mol.

Figure 16. Ligand with improved binding free energy found experimentally and final computationally optimized ligand built by choosing perturbations highlighted by our optimization methodology.

Conclusion

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In this work, a novel method to optimize the van der Waals interactions of small molecule inhibitors to maximize the binding affinity to a receptor has been developed. This method combines a rapid single step perturbation calculation with MBAR calculations to calculate the gradient and objective, respectively, allowing optimized inhibitors to be found quickly. This new method was applied to nine inhibitors across five diverse test systems: androgen receptor, SARS PL protease, renin, menin and thrombin. Good agreement was found between the beneficial growth vectors identified by the optimization and the results of full FEP calculations to exhaustively calculate methylations, with a Spearman’s rank order correlation of 0.59. The method developed in this work is found to be approximately 10 times faster than testing all possible growth vectors with FEP. Using an Nvidia P100 GPU, an optimization for the androgen receptor test case (containing around 4000 atoms in the ligand-protein simulation) takes approximately 13 h of wall time. This is compared to 15 h per growth vector, totaling 150 h to test all growth vectors with full FEP. Additionally, the scaling of the optimization compute time with the number of growth vectors is sublinear compared to linear scaling in the full FEP scan case. The sublinear scaling is a result of using single step perturbation theory in the optimization calculations that allowed any number of growth vectors to be assessed with one molecular dynamics simulation. Where experimental data were available mutations highlighted by the optimizer were tested with full FEP, and the mean unsigned error between experimental and calculated values of the binding free energy was 0.83 kcal/mol. We suggest that optimization methods such as this will be useful in a drug discovery setting to identify beneficial growth vectors during the hit-to-lead process, reducing the need for costly trial and error in both computational and experimental campaigns.

This work could be extended by investigating the information that could be extracted by optimizing any other parameters of the system. A natural example might be the epsilon parameter in the LJ potential, which may provide more information as to what groups might be beneficially accommodated beyond methyl groups. In theory, any parameter of the system could be optimized, and looking at the bond, angle or torsion parameters using the methods developed here may also be interesting.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.0c00610.

Details of protein preparation specific to each system; all ligand structures in full 3D with all optimized hydrogens labeled explicitly (Table S1); calculations for validation of free energies obtained from final optimized sigmas (Figures S1–S3 and Table S2); all convergence graphs not presented in the main text for ΔΔG_scan calculations (Figures S4–S11); diagram for steric clashes in androgen receptor (Figure S12); all optimized sigmas for the menin B ligand (Table S3); all optimized charges for the thrombin B ligand (Table S4); Figure S13 depicting the thermodynamic cycle used for relative binding free energy calculations in this work (PDF)

ci0c00610_si_001.pdf (1.72 MB)

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Author Information

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Corresponding Author
- David J. Huggins - Tri-Institutional Therapeutics Discovery Institute, Belfer Research Building, 413 East 69th Street, 16th Floor, Box 300, New York, United States; Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, New York 10065, United States; http://orcid.org/0000-0003-1579-2496; Email: [email protected]
Author
- Alexander D. Wade - TCM Group, Cavendish Laboratory, University of Cambridge, 19 J J Thomson Avenue, Cambridge CB3 0HE, United Kingdom; http://orcid.org/0000-0003-1500-3733
Notes
The authors declare no competing financial interest.

Acknowledgments

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A.D.W. would like to acknowledge the EPSRC Centre for Doctoral Training in Computational Methods for Materials Science for funding under grant number EP/L015552/1. All calculations were performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/) and were funded by the EPSRC under grant EP/P020259/1. The authors gratefully acknowledge the support to the project generously provided by the Tri-Institutional Therapeutics Discovery Institute (TDI), a 501(c) (3) organization. TDI receives financial support from Takeda Pharmaceutical Company, TDI’s parent institutes (Memorial Sloan Kettering Cancer Center, The Rockefeller University and Weill Cornell Medicine) and from a generous contribution from Mr. Lewis Sanders and other philanthropic sources.

References

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This article references 72 other publications.

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Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M. K.; Greenwood, J.; Romero, D. L.; Masse, C.; Knight, J. L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D. L.; Jorgensen, W. L.; Berne, B. J.; Friesner, R. A.; Abel, R. Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field. J. Am. Chem. Soc. 2015, 137, 2695– 2703, DOI: 10.1021/ja512751q

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Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field

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Designing tight-binding ligands is a primary objective of small-mol. drug discovery. Over the past few decades, free-energy calcns. have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread com. application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the tech. challenges traditionally assocd. with running these types of calcns. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chem. perturbations, many of which involve significant changes in ligand chem. structures. In addn., we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compds. synthesized that have been predicted to be potent. Compds. predicted to be potent by this approach have a substantial redn. in false positives relative to compds. synthesized on the basis of other computational or medicinal chem. approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

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Lindorff-Larsen, K.; Maragakis, P.; Piana, S.; Shaw, D. E. Picosecond to Millisecond Structural Dynamics in Human Ubiquitin. J. Phys. Chem. B 2016, 120 (33), 8313– 8320, DOI: 10.1021/acs.jpcb.6b02024

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Picosecond to Millisecond Structural Dynamics in Human Ubiquitin

Lindorff-Larsen, Kresten; Maragakis, Paul; Piana, Stefano; Shaw, David E.

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Human ubiquitin has been extensively characterized using a variety of exptl. and computational methods and has become an important model for studying protein dynamics. Nevertheless, it has proven difficult to characterize the microsecond time scale dynamics of this protein with atomistic resoln. Here we use an unbiased computer simulation to describe the structural dynamics of ubiquitin on the picosecond to millisecond time scale. In the simulation, ubiquitin interconverts between a small no. of distinct states on the microsecond to millisecond time scale. We find that the conformations visited by free ubiquitin in soln. are very similar to those found various crystal structures of ubiquitin in complex with other proteins, a finding in line with previous exptl. studies. We also observe weak but statistically significant correlated motions throughout the protein, including long-range concerted movement across the entire β sheet, consistent with recent exptl. observations. We expect that the detailed atomistic description of ubiquitin dynamics provided by this unbiased simulation may be useful in interpreting current and future expts. on this protein.

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Pierce, L. C. T.; Salomon-Ferrer, R.; Augusto F de Oliveira, C.; McCammon, J. A.; Walker, R. C. Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics. J. Chem. Theory Comput. 2012, 8 (9), 2997– 3002, DOI: 10.1021/ct300284c

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Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics

Pierce, Levi C. T.; Salomon-Ferrer, Romelia; Augusto F. de Oliveira, Cesar; McCammon, J. Andrew; Walker, Ross C.

Journal of Chemical Theory and Computation (2012), 8 (9), 2997-3002CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

In this work, we critically assess the ability of the all-atom enhanced sampling method accelerated mol. dynamics (aMD) to investigate conformational changes in proteins that typically occur on the millisecond time scale. We combine aMD with the inherent power of graphics processor units (GPUs) and apply the implementation to the bovine pancreatic trypsin inhibitor (BPTI). A 500 ns aMD simulation is compared to a previous millisecond unbiased brute force MD simulation carried out on BPTI, showing that the same conformational space is sampled by both approaches. To our knowledge, this represents the first implementation of aMD on GPUs and also the longest aMD simulation of a biomol. run to date. Our implementation is available to the community in the latest release of the Amber software suite (v12), providing routine access to millisecond events sampled from dynamics simulations using off the shelf hardware.

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Salomon-Ferrer, R.; Götz, A. W.; Poole, D.; Le Grand, S.; Walker, R. C. Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. J. Chem. Theory Comput. 2013, 9 (9), 3878– 3888, DOI: 10.1021/ct400314y

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Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald

Salomon-Ferrer, Romelia; Gotz, Andreas W.; Poole, Duncan; Le Grand, Scott; Walker, Ross C.

Journal of Chemical Theory and Computation (2013), 9 (9), 3878-3888CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

We present an implementation of explicit solvent all atom classical mol. dynamics (MD) within the AMBER program package that runs entirely on CUDA-enabled GPUs. First released publicly in Apr. 2010 as part of version 11 of the AMBER MD package and further improved and optimized over the last two years, this implementation supports the three most widely used statistical mech. ensembles (NVE, NVT, and NPT), uses particle mesh Ewald (PME) for the long-range electrostatics, and runs entirely on CUDA-enabled NVIDIA graphics processing units (GPUs), providing results that are statistically indistinguishable from the traditional CPU version of the software and with performance that exceeds that achievable by the CPU version of AMBER software running on all conventional CPU-based clusters and supercomputers. We briefly discuss three different precision models developed specifically for this work (SPDP, SPFP, and DPDP) and highlight the tech. details of the approach as it extends beyond previously reported work [Goetz et al., J. Chem. Theory Comput.2012, DOI: 10.1021/ct200909j; Le Grand et al., Comp. Phys. Comm.2013, DOI: 10.1016/j.cpc.2012.09.022].We highlight the substantial improvements in performance that are seen over traditional CPU-only machines and provide validation of our implementation and precision models. We also provide evidence supporting our decision to deprecate the previously described fully single precision (SPSP) model from the latest release of the AMBER software package.

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Eastman, P.; Swails, J.; Chodera, J. D.; McGibbon, R. T.; Zhao, Y.; Beauchamp, K. A.; Wang, L.-P.; Simmonett, A. C.; Harrigan, M. P.; Stern, C. D.; Wiewiora, R. P.; Brooks, B. R.; Pande, V. S. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLoS Comput. Biol. 2017, 13, e1005659, DOI: 10.1371/journal.pcbi.1005659

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OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

Eastman, Peter; Swails, Jason; Chodera, John D.; McGibbon, Robert T.; Zhao, Yutong; Beauchamp, Kyle A.; Wang, Lee-Ping; Simmonett, Andrew C.; Harrigan, Matthew P.; Stern, Chaya D.; Wiewiora, Rafal P.; Brooks, Bernard R.; Pande, Vijay S.

PLoS Computational Biology (2017), 13 (7), e1005659/1-e1005659/17CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)

OpenMM is a mol. dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a math. description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.

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Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J. Y.; Wang, L.; Lupyan, D.; Dahlgren, M. K.; Knight, J. L.; Kaus, J. W.; Cerutti, D. S.; Krilov, G.; Jorgensen, W. L.; Abel, R.; Friesner, R. A. OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins. J. Chem. Theory Comput. 2016, 12 (1), 281– 296, DOI: 10.1021/acs.jctc.5b00864

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OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins

Harder, Edward; Damm, Wolfgang; Maple, Jon; Wu, Chuanjie; Reboul, Mark; Xiang, Jin Yu; Wang, Lingle; Lupyan, Dmitry; Dahlgren, Markus K.; Knight, Jennifer L.; Kaus, Joseph W.; Cerutti, David S.; Krilov, Goran; Jorgensen, William L.; Abel, Robert; Friesner, Richard A.

Journal of Chemical Theory and Computation (2016), 12 (1), 281-296CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

The parametrization and validation of the OPLS3 force field for small mols. and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addn. of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chem. space, OPLS3 employs over an order of magnitude more ref. data and assocd. parameter types relative to other commonly used small mol. force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small mol. conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a no. of proteins. Together, the improvements made to both the small mol. and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

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Roos, K.; Wu, C.; Damm, W.; Reboul, M.; Stevenson, J. M.; Lu, C.; Dahlgren, M. K.; Mondal, S.; Chen, W.; Wang, L.; Abel, R.; Friesner, R. A.; Harder, E. D. OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules. J. Chem. Theory Comput. 2019, 15, 1863, DOI: 10.1021/acs.jctc.8b01026

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OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules

Roos, Katarina; Wu, Chuanjie; Damm, Wolfgang; Reboul, Mark; Stevenson, James M.; Lu, Chao; Dahlgren, Markus K.; Mondal, Sayan; Chen, Wei; Wang, Lingle; Abel, Robert; Friesner, Richard A.; Harder, Edward D.

Journal of Chemical Theory and Computation (2019), 15 (3), 1863-1874CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

Building upon the OPLS3 force field we report on an enhanced model, OPLS3e, that further extends its coverage of medicinally relevant chem. space by addressing limitations in chemotype transferability. OPLS3e accomplishes this by incorporating new parameter types that recognize moieties with greater chem. specificity and integrating an on-the-fly parametrization approach to the assignment of partial charges. As a consequence, OPLS3e leads to greater accuracy against performance benchmarks that assess small mol. conformational propensities, solvation, and protein-ligand binding.

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Maier, J. A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K. E.; Simmerling, C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015, 11 (8), 3696– 3713, DOI: 10.1021/acs.jctc.5b00255

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ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB

Maier, James A.; Martinez, Carmenza; Kasavajhala, Koushik; Wickstrom, Lauren; Hauser, Kevin E.; Simmerling, Carlos

Journal of Chemical Theory and Computation (2015), 11 (8), 3696-3713CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

Mol. mechanics is powerful for its speed in atomistic simulations, but an accurate force field is required. The Amber ff99SB force field improved protein secondary structure balance and dynamics from earlier force fields like ff99, but weaknesses in side chain rotamer and backbone secondary structure preferences have been identified. Here, we performed a complete refit of all amino acid side chain dihedral parameters, which had been carried over from ff94. The training set of conformations included multidimensional dihedral scans designed to improve transferability of the parameters. Improvement in all amino acids was obtained as compared to ff99SB. Parameters were also generated for alternate protonation states of ionizable side chains. Av. errors in relative energies of pairs of conformations were under 1.0 kcal/mol as compared to QM, reduced 35% from ff99SB. We also took the opportunity to make empirical adjustments to the protein backbone dihedral parameters as compared to ff99SB. Multiple small adjustments of φ and ψ parameters were tested against NMR scalar coupling data and secondary structure content for short peptides. The best results were obtained from a phys. motivated adjustment to the φ rotational profile that compensates for lack of ff99SB QM training data in the β-ppII transition region. Together, these backbone and side chain modifications (hereafter called ff14SB) not only better reproduced their benchmarks, but also improved secondary structure content in small peptides and reprodn. of NMR χ1 scalar coupling measurements for proteins in soln. We also discuss the Amber ff12SB parameter set, a preliminary version of ff14SB that includes most of its improvements.

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Vanommeslaeghe, K.; Hatcher, E.; Acharya, C.; Kundu, S.; Zhong, S.; Shim, J.; Darian, E.; Guvench, O.; Lopes, P.; Vorobyov, I.; Mackerell, A. D. CHARMM General Force Field: A Force Field for Drug-like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Fields. J. Comput. Chem. 2009, 31 (4), 671– 690, DOI: 10.1002/jcc.21367

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Abel, R.; Wang, L.; Harder, E. D.; Berne, B. J.; Friesner, R. A. Advancing Drug Discovery through Enhanced Free Energy Calculations. Acc. Chem. Res. 2017, 50 (7), 1625– 1632, DOI: 10.1021/acs.accounts.7b00083

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Advancing Drug Discovery through Enhanced Free Energy Calculations

Abel, Robert; Wang, Lingle; Harder, Edward D.; Berne, B. J.; Friesner, Richard A.

Accounts of Chemical Research (2017), 50 (7), 1625-1632CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)

A review. A principal goal of drug discovery project is to design mols. that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chem. and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup, have enabled accurate and reliable calcns. of protein-ligands binding free energies, and position free energy calcns. to play a guiding role in small mol. drug discovery. In this accounts, the authors outline the relevant methodol. advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with conventional FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force fields, and the advanced simulation set up that constitute the authors' FEP+ approach, followed by the presentation of extensive comparisons with expt., demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodol. development plans to address those limitations. The authors then report results from a recent drug discovery project, in which several thousand FEP+ calcns. were successfully deployed to simultaneously optimize potency, selectivity, and soly., illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calcns. to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compds., in various dimensions, for a wide range of targets. More effective integration of FEP+ calcns. into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compds. entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approxns. The authors conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theor. methods and models.

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Chodera, J. D.; Mobley, D. L.; Shirts, M. R.; Dixon, R. W.; Branson, K.; Pande, V. S. Alchemical Free Energy Methods for Drug Discovery: Progress and Challenges. Curr. Opin. Struct. Biol. 2011, 21 (2), 150– 160, DOI: 10.1016/j.sbi.2011.01.011

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Alchemical free energy methods for drug discovery: Progress and challenges

Chodera, John D.; Mobley, David L.; Shirts, Michael R.; Dixon, Richard W.; Branson, Kim; Pande, Vijay S.

Current Opinion in Structural Biology (2011), 21 (2), 150-160CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)

A review. Improved rational drug design methods are needed to lower the cost and increase the success rate of drug discovery and development. Alchem. binding free energy calcns., one potential tool for rational design, have progressed rapidly over the past decade, but still fall short of providing robust tools for pharmaceutical engineering. Recent studies, esp. on model receptor systems, have clarified many of the challenges that must be overcome for robust predictions of binding affinity to be useful in rational design. In this review, inspired by a recent joint academic/industry meeting organized by the authors, we discuss these challenges and suggest a no. of promising approaches for overcoming them.

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Cournia, Z.; Allen, B.; Sherman, W. Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations. J. Chem. Inf. Model. 2017, 57 (12), 2911– 2937, DOI: 10.1021/acs.jcim.7b00564

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Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

Cournia, Zoe; Allen, Bryce; Sherman, Woody

Journal of Chemical Information and Modeling (2017), 57 (12), 2911-2937CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

A review. Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, tech., and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calcns., which rely on physics-based mol. simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, the authors present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. The authors focus specifically on relative binding free energies because the calcns. are less computationally intensive than abs. binding free energy (ABFE) calcns. and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a ref. mol. and new ideas (virtual mols.) can be used to prioritize mols. for synthesis. The authors describe the crit. aspects of running RBFE calcns., from both theor. and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, tech., and practical issues assocd. with running relative binding free energy simulations, with a focus on real-world drug discovery applications. The authors offer guidelines for improving the accuracy of RBFE simulations, esp. for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

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van Vlijmen, H.; Desjarlais, R. L.; Mirzadegan, T. Computational Chemistry at Janssen. J. Comput.-Aided Mol. Des. 2017, 31 (3), 267– 273, DOI: 10.1007/s10822-016-9998-9

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Computational chemistry at Janssen

van Vlijmen, Herman; Desjarlais, Renee L.; Mirzadegan, Tara

Journal of Computer-Aided Molecular Design (2017), 31 (3), 267-273CODEN: JCADEQ; ISSN:0920-654X. (Springer)

Computer-aided drug discovery activities at Janssen are carried out by scientists in the Computational Chem. group of the Discovery Sciences organization. This perspective gives an overview of the organizational and operational structure, the science, internal and external collaborations, and the impact of the group on Drug Discovery at Janssen.

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Cole, D. J.; Janecek, M.; Stokes, J. E.; Rossmann, M.; Faver, J. C.; McKenzie, G. J.; Venkitaraman, A. R.; Hyvönen, M.; Spring, D. R.; Huggins, D. J.; Jorgensen, W. L. Computationally-Guided Optimization of Small-Molecule Inhibitors of the Aurora A kinase–TPX2 Protein–protein Interaction. Chem. Commun. 2017, 53, 9372– 9375, DOI: 10.1039/C7CC05379G

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Computationally-guided optimization of small-molecule inhibitors of the Aurora A kinase-TPX2 protein-protein interaction

Cole, Daniel J.; Janecek, Matej; Stokes, Jamie E.; Rossmann, Maxim; Faver, John C.; McKenzie, Grahame J.; Venkitaraman, Ashok R.; Hyvonen, Marko; Spring, David R.; Huggins, David J.; Jorgensen, William L.

Chemical Communications (Cambridge, United Kingdom) (2017), 53 (67), 9372-9375CODEN: CHCOFS; ISSN:1359-7345. (Royal Society of Chemistry)

Free energy perturbation theory, in combination with enhanced sampling of protein-ligand binding modes, was evaluated in the context of fragment-based drug design, and used to design 2 new small-mol. inhibitors of the human Aurora A kinase-TPX2 protein-protein interaction.

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Kuhn, M.; Firth-Clark, S.; Tosco, P.; Mey, A. S. J. S.; Mackey, M. D.; Michel, J. Assessment of Binding Affinity via Alchemical Free Energy Calculations. J. Chem. Inf. Model. 2020, 60, 3120, DOI: 10.1021/acs.jcim.0c00165

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Assessment of Binding Affinity via Alchemical Free-Energy Calculations

Kuhn, Maximilian; Firth-Clark, Stuart; Tosco, Paolo; Mey, Antonia S. J. S.; Mackey, Mark; Michel, Julien

Journal of Chemical Information and Modeling (2020), 60 (6), 3120-3130CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

Free-energy calcns. have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calcns. and subsequent anal. of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free-energy calcns. is presented. Several sanity checks for assessing the reliability of the calcns. were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace, and OpenMM and uses the AMBER 14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrodinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with exptl. values. For the larger dataset, the av. correlation coeff. Rp was 0.70 ± 0.05 and av. Kendall's τ was 0.53 ± 0.05, which are broadly comparable to or better than previously reported results using other methods.

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Riniker, S.; Christ, C. D.; Hansen, H. S.; Hünenberger, P. H.; Oostenbrink, C.; Steiner, D.; van Gunsteren, W. F. Calculation of Relative Free Energies for Ligand-Protein Binding, Solvation, and Conformational Transitions Using the GROMOS Software. J. Phys. Chem. B 2011, 115 (46), 13570– 13577, DOI: 10.1021/jp204303a

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Calculation of Relative Free Energies for Ligand-Protein Binding, Solvation, and Conformational Transitions Using the GROMOS Software

Riniker, Sereina; Christ, Clara D.; Hansen, Halvor S.; Hunenberger, Philippe H.; Oostenbrink, Chris; Steiner, Denise; van Gunsteren, Wilfred F.

Journal of Physical Chemistry B (2011), 115 (46), 13570-13577CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)

The calcn. of the relative free energies of ligand-protein binding, of solvation for different compds., and of different conformational states of a polypeptide is of considerable interest in the design or selection of potential enzyme inhibitors. Since such processes in aq. soln. generally comprise energetic and entropic contributions from many mol. configurations, adequate sampling of the relevant parts of configurational space is required and can be achieved through mol. dynamics simulations. Various techniques to obtain converged ensemble avs. and their implementation in the GROMOS software for biomol. simulation are discussed, and examples of their application to biomols. in aq. soln. are given.

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Bissantz, C.; Kuhn, B.; Stahl, M. A Medicinal Chemist’s Guide to Molecular Interactions. J. Med. Chem. 2010, 53 (14), 5061– 5084, DOI: 10.1021/jm100112j

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A Medicinal Chemist's Guide to Molecular Interactions

Bissantz, Caterina; Kuhn, Bernd; Stahl, Martin

Journal of Medicinal Chemistry (2010), 53 (14), 5061-5084CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)

A review.

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Wieder, M.; Garon, A.; Perricone, U.; Boresch, S.; Seidel, T.; Almerico, A. M.; Langer, T. Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations. J. Chem. Inf. Model. 2017, 57 (2), 365– 385, DOI: 10.1021/acs.jcim.6b00674

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Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations

Wieder, Marcus; Garon, Arthur; Perricone, Ugo; Boresch, Stefan; Seidel, Thomas; Almerico, Anna Maria; Langer, Thierry

Journal of Chemical Information and Modeling (2017), 57 (2), 365-385CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

The authors present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high no. of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and re-scored to generate a single hit-list. The score for a particular mol. is calcd. based on the no. of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. The authors test the performance of CHA for virtual screening using screening databases with active and inactive compds. for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of a protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the exptl. structure in the PDB and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availability of diverse sets of different pharmacophore models is utilized to analyze some addnl. questions of interest in 3D pharmacophore based virtual screening.

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Newton, A. S.; Faver, J. C.; Micevic, G.; Muthusamy, V.; Kudalkar, S. N.; Bertoletti, N.; Anderson, K. S.; Bosenberg, M. W.; Jorgensen, W. L. Structure-Guided Identification of DNMT3B Inhibitors. ACS Med. Chem. Lett. 2020, 11 (5), 971– 976, DOI: 10.1021/acsmedchemlett.0c00011

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Structure-Guided Identification of DNMT3B Inhibitors

Newton, Ana S.; Faver, John C.; Micevic, Goran; Muthusamy, Viswanathan; Kudalkar, Shalley N.; Bertoletti, Nicole; Anderson, Karen S.; Bosenberg, Marcus W.; Jorgensen, William L.

ACS Medicinal Chemistry Letters (2020), 11 (5), 971-976CODEN: AMCLCT; ISSN:1948-5875. (American Chemical Society)

Methyltransferase 3 beta (DNMT3B) inhibitors that interfere with cancer growth are emerging possibilities for treatment of melanoma. Herein we identify small mol. inhibitors of DNMT3B starting from a homol. model based on a DNMT3A crystal structure. Virtual screening by docking led to purchase of 15 compds., among which 5 were found to inhibit the activity of DNMT3B with IC50 values of 13-72μM in a fluorogenic assay. Eight analogs of 7, 10, and 12 were purchased to provide 2 more active compds. Compd. 11 is particularly notable as it shows good selectivity with no inhibition of DNMT1 and 22μM potency toward DNMT3B. Following addnl. de novo design, exploratory synthesis of 17 analogs of 11 delivered 5 addnl. inhibitors of DNMT3B with the most potent being 33h with an IC50 of 8.0μM. This result was well confirmed in an ultrahigh-performance liq. chromatog. (UHPLC)-based anal. assay, which yielded an IC50 of 4.8μM. Structure-activity data are rationalized based on computed structures for DNMT3B complexes.

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Liosi, M.-E.; Krimmer, S. G.; Newton, A. S.; Dawson, T. K.; Puleo, D. E.; Cutrona, K. J.; Suzuki, Y.; Schlessinger, J.; Jorgensen, W. L. Selective Janus Kinase 2 (JAK2) Pseudokinase Ligands with a Diaminotriazole Core. J. Med. Chem. 2020, 63 (10), 5324– 5340, DOI: 10.1021/acs.jmedchem.0c00192

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Selective Janus Kinase 2 (JAK2) Pseudokinase Ligands with a Diaminotriazole Core

Liosi Maria-Elena; Krimmer Stefan G; Newton Ana S; Dawson Thomas K; Cutrona Kara J; Jorgensen William L; Puleo David E; Suzuki Yoshihisa; Schlessinger Joseph

Journal of medicinal chemistry (2020), 63 (10), 5324-5340 ISSN:.

Janus kinases (JAKs) are non-receptor tyrosine kinases that are essential components of the JAK-STAT signaling pathway. Associated aberrant signaling is responsible for many forms of cancer and disorders of the immune system. The present focus is on the discovery of molecules that may regulate the activity of JAK2 by selective binding to the JAK2 pseudokinase domain, JH2. Specifically, the Val617Phe mutation in JH2 stimulates the activity of the adjacent kinase domain (JH1) resulting in myeloproliferative disorders. Starting from a non-selective screening hit, we have achieved the goal of discovering molecules that preferentially bind to the ATP binding site in JH2 instead of JH1. We report the design and synthesis of the compounds and binding results for the JH1, JH2, and JH2 V617F domains, as well as five crystal structures for JH2 complexes. Testing with a selective and non-selective JH2 binder on the autophosphorylation of wild-type and V617F JAK2 is also contrasted.

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Schindler, C.; Rippmann, F.; Kuhn, D. Relative Binding Affinity Prediction of Farnesoid X Receptor in the D3R Grand Challenge 2 Using FEP+. J. Comput.-Aided Mol. Des. 2018, 32 (1), 265– 272, DOI: 10.1007/s10822-017-0064-z

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Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+

Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

Journal of Computer-Aided Molecular Design (2018), 32 (1), 265-272CODEN: JCADEQ; ISSN:0920-654X. (Springer)

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the com. free energy calcn. protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two exptl. data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for mol. modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future inhouse drug design projects.

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Keränen, H.; Pérez-Benito, L.; Ciordia, M.; Delgado, F.; Steinbrecher, T. B.; Oehlrich, D.; van Vlijmen, H. W. T.; Trabanco, A. A.; Tresadern, G. Acylguanidine Beta Secretase 1 Inhibitors: A Combined Experimental and Free Energy Perturbation Study. J. Chem. Theory Comput. 2017, 13 (3), 1439– 1453, DOI: 10.1021/acs.jctc.6b01141

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Acylguanidine Beta Secretase 1 Inhibitors: A Combined Experimental and Free Energy Perturbation Study

Keranen, Henrik; Perez-Benito, Laura; Ciordia, Myriam; Delgado, Francisca; Steinbrecher, Thomas B.; Oehlrich, Daniel; van Vlijmen, Herman W. T.; Trabanco, Andres A.; Tresadern, Gary

Journal of Chemical Theory and Computation (2017), 13 (3), 1439-1453CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

A series of acylguanidine beta secretase 1 (BACE1) inhibitors with modified scaffold and P3 pocket substituent was synthesized and studied with Free Energy Perturbation (FEP) calcns. The resulting mols. showed potencies in enzymic BACE1 inhibition assays up to 1 nM. The correlation between the predicted activity from the FEP calcns. and the exptl. activity was good for the P3 pocket substituents. The av. mean unsigned error (MUE) between prediction and expt. was 0.68±0.17 kcal/mol for the default 5 ns lambda window simulation time improving to 0.35±0.13 kcal/mol for 40 ns. FEP calcns. for the P2' pocket substituents on the same acylguanidine scaffold also showed good agreement with expt. and the results remained stable with repeated simulations and increased simulation time. It proved more difficult to use FEP calcns. to study the scaffold modification from increasing 5 to 6 and 7 membered-rings. Although prediction and expt. were in agreement for short 2 ns simulations, as the simulation time increased the results diverged. This was improved by the use of a newly developed 'Core Hopping FEP+' approach, which also showed improved stability in repeat calcns. The origins of these differences along with the value of repeat and longer simulation times are discussed. This work provides a further example of the use of FEP as a computational tool for mol. design.

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Song, L. F.; Lee, T.-S.; Zhu, C.; York, D. M.; Merz, K. M., Jr. Using AMBER18 for Relative Free Energy Calculations. J. Chem. Inf. Model. 2019, 59 (7), 3128– 3135, DOI: 10.1021/acs.jcim.9b00105

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Using AMBER18 for Relative Free Energy Calculations

Song, Lin Frank; Lee, Tai-Sung; Zhu, Chun; York, Darrin M.; Merz, Kenneth M.

Journal of Chemical Information and Modeling (2019), 59 (7), 3128-3135CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

With renewed interest in free energy methods in contemporary structure-based need to validate against multiple targets and force fields to assess the overall ability of these methods to accurately predict relative binding free energies. We computed relative binding free energies using GPU accelerated Thermodn. Integration (GPU-TI) on a dataset originally assembled by Schrodinger, Inc.. Using their GPU free energy code (FEP+) and the OPLS2.1 force field combined with the REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol. In our study using GPU-TI from AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 perturbations contained in this data set. A more detailed analyses of our results suggested that the obsd. differences between the two studies arise from differences in sampling protocols along with differences in the force fields employed. Future work should address the problem of establishing benchmark quality results with robust statistical error bars obtained through multiple independent runs and enhanced sampling, which is possible with the GPU-accelerated features in AMBER.

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Irwin, B. W. J.; Huggins, D. J. Estimating Atomic Contributions to Hydration and Binding Using Free Energy Perturbation. J. Chem. Theory Comput. 2018, 14 (6), 3218– 3227, DOI: 10.1021/acs.jctc.8b00027

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Estimating Atomic Contributions to Hydration and Binding Using Free Energy Perturbation

Irwin, Benedict W. J.; Huggins, David J.

Journal of Chemical Theory and Computation (2018), 14 (6), 3218-3227CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

We present a general method called atom-wise free energy perturbation (AFEP), which extends a conventional mol. dynamics free energy perturbation (FEP) simulation to give the contribution to a free energy change from each atom. AFEP is derived from an expansion of the Zwanzig equation used in the exponential averaging method by defining that the system total energy can be partitioned into contributions from each atom. A partitioning method is assumed and used to group terms in the expansion to correspond to individual atoms. AFEP is applied to six example free energy changes to demonstrate the method. Firstly, the hydration free energies of methane, methanol, methylamine, methanethiol, and caffeine in water. AFEP highlights the atoms in the mols. that interact favorably or unfavorably with water. Finally AFEP is applied to the binding free energy of human immunodeficiency virus type 1 protease to lopinavir, and AFEP reveals the contribution of each atom to the binding free energy, indicating candidate areas of the mol. to improve to produce a more strongly binding inhibitor. FEP gives a single value for the free energy change and is already a very useful method. AFEP gives a free energy change for each "part" of the system being simulated, where part can mean individual atoms, chem. groups, amino acids, or larger partitions depending on what the user is trying to measure. This method should have various applications in mol. dynamics studies of phys., chem., or biochem. phenomena, specifically in the field of computational drug discovery.

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Lee, L.-P.; Tidor, B. Optimization of Electrostatic Binding Free Energy. J. Chem. Phys. 1997, 106 (21), 8681– 8690, DOI: 10.1063/1.473929

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Optimization of electrostatic binding free energy

Lee, Lee-Peng; Tidor, Bruce

Journal of Chemical Physics (1997), 106 (21), 8681-8690CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)

An analytic result is derived that defines the charge distribution of the tightest-binding ligand given a receptor charge distribution and spherical geometries. Using the framework of continuum electrostatics, the optimal distribution is expressed as a set of multipoles detd. by minimizing the electrostatic free energy of binding. Results for two simple receptor systems are presented to illustrate applications of the theory.

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Kangas, E.; Tidor, B. Electrostatic Complementarity at Ligand Binding Sites: Application to Chorismate Mutase. J. Phys. Chem. B 2001, 105 (4), 880– 888, DOI: 10.1021/jp003449n

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Electrostatic Complementarity at Ligand Binding Sites: Application to Chorismate Mutase

Kangas, Erik; Tidor, Bruce

Journal of Physical Chemistry B (2001), 105 (4), 880-888CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)

Charge optimization methods facilitate examn. and, potentially, improvement of electrostatic interactions between binding partners. Here charge optimization was applied to the chorismate mutase from Bacillus subtilis binding an endo-oxabicyclic transition-state analog. Electrostatically optimized templates based on calcns. using the x-ray crystal structure were used to define regions of the transition-state analog whose electrostatic properties are sub-optimal for binding. Variants of the analog that could exhibit improved electrostatic affinity for the enzyme were considered that more closely mimicked the optimal charge distributions. Results indicate that the transition-state analog is remarkably complementary to the enzyme active site in terms of electrostatics throughout much of the binding site. Particularly good electrostatic complementarity is exhibited for most of the groups on the analog that make hydrogen bonds with the enzyme. While some small potential opportunities for improvement exist throughout the ligand, one significant under-compensated interaction stands out. The C10 carboxylate pays substantial desolvation penalty upon binding but does not recover strong hydrogen-bonded interactions with the enzyme in the available crystal structure. Calcns. suggest that replacement with a virtually isosteric nitro group may improve the electrostatic contribution to the binding free energy by 2-3 kcal/mol. The principal mechanism is a decrease in the desolvation penalty of the ligand with a smaller loss in ligand-enzyme interactions. This work shows that charge optimization techniques are capable of identifying chem. reasonable substitutions to known ligands that produce substantial improvements in computed binding affinity through electrostatic enhancement. Comparison of the results across twelve independently refined active sites confirms that the approach is not overly sensitive to subtle structural variation.

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Radhakrishnan, M. L. Theor. Chem. Acc. 2012, 131, 1252, DOI: 10.1007/s00214-012-1252-5

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Wade, A. D.; Huggins, D. J. Optimization of Protein–Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method. J. Chem. Theory Comput. 2019, 15 (11), 6504– 6512, DOI: 10.1021/acs.jctc.9b00976

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Optimization of Protein-Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method

Wade, Alexander D.; Huggins, David J.

Journal of Chemical Theory and Computation (2019), 15 (11), 6504-6512CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

The authors present an explicit solvent alchem. free-energy method for optimizing the partial charges of a ligand to maximize the binding affinity with a receptor. This methodol. can be applied to known ligand-protein complexes to det. an optimized set of ligand partial at. changes. Three protein-ligand complexes have been optimized in this work: FXa, P38 and the androgen receptor. The sets of optimized charges can be used to identify design principles for chem. changes to the ligands which improve the binding affinity for all three systems. In this work, beneficial chem. mutations are generated from these principles and the resulting mols. tested using free-energy perturbation calcns. The authors show that three quarters of the chem. changes are predicted to improve the binding affinity, with an av. improvement for the beneficial mutations of approx. 1 kcal/mol. In the cases where exptl. data is available, the agreement between prediction and expt. is also good. The results demonstrate that charge optimization in explicit solvent is a useful tool for predicting beneficial chem. changes such as pyridinations, fluorinations, and oxygen to sulfur mutations.

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Wan, S.; Bhati, A. P.; Zasada, S. J.; Wall, I.; Green, D.; Bamborough, P.; Coveney, P. V. Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study. J. Chem. Theory Comput. 2017, 13 (2), 784– 795, DOI: 10.1021/acs.jctc.6b00794

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Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study

Wan, Shunzhou; Bhati, Agastya P.; Zasada, Stefan J.; Wall, Ian; Green, Darren; Bamborough, Paul; Coveney, Peter V.

Journal of Chemical Theory and Computation (2017), 13 (2), 784-795CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

Binding free energies of bromodomain inhibitors are calcd. with recently formulated approaches, namely ESMACS (enhanced sampling of mol. dynamics with approxn. of continuum solvent) and TIES (thermodn. integration with enhanced sampling). A set of compds. is provided by GlaxoSmithKline, which represents a range of chem. functionality and binding affinities. The predicted binding free energies exhibit a good Spearman correlation of 0.78 with the exptl. data from the 3-trajectory ESMACS, and an excellent correlation of 0.92 from the TIES approach where applicable. Given access to suitable high end computing resources and a high degree of automation, the authors can compute individual binding affinities in a few hours with precisions no greater than 0.2 kcal/mol for TIES, and no larger than 0.34 kcal/mol and 1.71 kcal/mol for the 1- and 3-trajectory ESMACS approaches.

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Lawrenz, M.; Baron, R.; McCammon, J. A. Independent-Trajectories Thermodynamic-Integration Free-Energy Changes for Biomolecular Systems: Determinants of H5N1 Avian Influenza Virus Neuraminidase Inhibition by Peramivir. J. Chem. Theory Comput. 2009, 5 (4), 1106– 1116, DOI: 10.1021/ct800559d

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Independent-Trajectories Thermodynamic-Integration Free-Energy Changes for Biomolecular Systems: Determinants of H5N1 Avian Influenza Virus Neuraminidase Inhibition by Peramivir

Lawrenz, Morgan; Baron, Riccardo; McCammon, J. Andrew

Journal of Chemical Theory and Computation (2009), 5 (4), 1106-1116CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

Free-energy changes are essential physicochem. quantities for understanding most biochem. processes. Yet, the application of accurate thermodn.-integration (TI) computation to biol. and macromol. systems is limited by finite-sampling artifacts. In this paper, the authors employ independent-trajectories thermodn.-integration (IT-TI) computation to est. improved free-energy changes and their uncertainties for (bio)mol. systems. IT-TI aids sampling statistics of the thermodn. macrostates for flexible assocg. partners by ensemble averaging of multiple, independent simulation trajectories. The authors study peramivir (PVR) inhibition of the H5N1 avian influenza virus neuraminidase flexible receptor (N1). Binding site loops 150 and 119 are highly mobile, as revealed by N1-PVR 20-ns mol. dynamics. Due to such heterogeneous sampling, std. TI binding free-energy ests. span a rather large free-energy range, from a 19% underestimation to a 29% overestimation of the exptl. ref. value (-62.2±1.8 kJ mol-1). Remarkably, the authors' IT-TI binding free-energy est. (-61.1±5.4 kJ mol-1) agrees with a 2% relative difference. In addn., IT-TI runs provide a statistics-based free-energy uncertainty for the process of interest. Using ∼800 ns of overall sampling, the authors investigate N1-PVR binding determinants by IT-TI alchem. modifications of PVR moieties. These results emphasize the dominant electrostatic contribution, particularly through the N1 E277-PVR guanidinium interaction. Future drug development may be also guided by properly tuning ligand flexibility and hydrophobicity. IT-TI will allow estn. of relative free energies for systems of increasing size, with improved reliability by employing large-scale distributed computing.

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Vilseck, J. Z.; Armacost, K. A.; Hayes, R. L.; Goh, G. B.; Brooks, C. L., 3rd Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics. J. Phys. Chem. Lett. 2018, 9 (12), 3328– 3332, DOI: 10.1021/acs.jpclett.8b01284

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Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics

Vilseck, Jonah Z.; Armacost, Kira A.; Hayes, Ryan L.; Goh, Garrett B.; Brooks, Charles L.

Journal of Physical Chemistry Letters (2018), 9 (12), 3328-3332CODEN: JPCLCD; ISSN:1948-7185. (American Chemical Society)

In this study, we demonstrate the extensive scalability of the biasing potential replica exchange multisite λ dynamics (BP-REX MSλD) free energy method by calcg. binding affinities for 512 inhibitors to HIV Reverse Transcriptase (HIV-RT). This is the largest exploration of chem. space using free energy methods known to date, requires only a few simulations, and identifies 55 new inhibitor designs against HIV-RT predicted to be at least as potent as a tight binding ref. compd. (i.e., as potent as 56 nM). We highlight that BP-REX MSλD requires an order of magnitude less computational resources than conventional free energy methods while maintaining a similar level of precision, overcomes the inherent poor scalability of conventional free energy methods, and enables the exploration of combinatorially large chem. spaces in the context of in silico drug discovery.

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Bennett, C. H. Efficient Estimation of Free Energy Differences from Monte Carlo Data. J. Comput. Phys. 1976, 22 (2), 245– 268, DOI: 10.1016/0021-9991(76)90078-4

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Shirts, M. R.; Chodera, J. D. Statistically Optimal Analysis of Samples from Multiple Equilibrium States. J. Chem. Phys. 2008, 129 (12), 124105, DOI: 10.1063/1.2978177

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Statistically optimal analysis of samples from multiple equilibrium states

Shirts, Michael R.; Chodera, John D.

Journal of Chemical Physics (2008), 129 (12), 124105/1-124105/10CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)

We present a new estimator for computing free energy differences and thermodn. expectations as well as their uncertainties from samples obtained from multiple equil. states via either simulation or expt. The estimator, which we call the multistate Bennett acceptance ratio estimator (MBAR) because it reduces to the Bennett acceptance ratio estimator (BAR) when only two states are considered, has significant advantages over multiple histogram reweighting methods for combining data from multiple states. It does not require the sampled energy range to be discretized to produce histograms, eliminating bias due to energy binning and significantly reducing the time complexity of computing a soln. to the estg. equations in many cases. Addnl., an est. of the statistical uncertainty is provided for all estd. quantities. In the large sample limit, MBAR is unbiased and has the lowest variance of any known estimator for making use of equil. data collected from multiple states. We illustrate this method by producing a highly precise est. of the potential of mean force for a DNA hairpin system, combining data from multiple optical tweezer measurements under const. force bias. (c) 2008 American Institute of Physics.

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Riniker, S.; Christ, C. D.; Hansen, N.; Mark, A. E.; Nair, P. C.; van Gunsteren, W. F. Comparison of Enveloping Distribution Sampling and Thermodynamic Integration to Calculate Binding Free Energies of Phenylethanolamine N-Methyltransferase Inhibitors. J. Chem. Phys. 2011, 135 (2), 024105, DOI: 10.1063/1.3604534

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Comparison of enveloping distribution sampling and thermodynamic integration to calculate binding free energies of phenylethanolamine N-methyltransferase inhibitors

Riniker, Sereina; Christ, Clara D.; Hansen, Niels; Mark, Alan E.; Nair, Pramod C.; van Gunsteren, Wilfred F.

Journal of Chemical Physics (2011), 135 (2), 024105/1-024105/13CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)

The relative binding free energy between two ligands to a specific protein can be obtained using various computational methods. The more accurate and also computationally more demanding techniques are the so-called free energy methods which use conformational sampling from mol. dynamics or Monte Carlo simulations to generate thermodn. avs. Two such widely applied methods are the thermodn. integration (TI) and the recently introduced enveloping distribution sampling (EDS) methods. In both cases relative binding free energies are obtained through the alchem. perturbations of one ligand into another in water and inside the binding pocket of the protein. TI requires many sep. simulations and the specification of a pathway along which the system is perturbed from one ligand to another. Using the EDS approach, only a single automatically derived ref. state enveloping both end states needs to be sampled. In addn., the choice of an optimal pathway in TI calcns. is not trivial and a poor choice may lead to poor convergence along the pathway. Given this, EDS is expected to be a valuable and computationally efficient alternative to TI. In this study, the performances of these two methods are compared using the binding of ten tetrahydroisoquinoline derivs. to phenylethanolamine N-transferase as an example. The ligands involve a diverse set of functional groups leading to a wide range of free energy differences. In addn., two different schemes to det. automatically the EDS ref. state parameters and two different topol. approaches are compared. (c) 2011 American Institute of Physics.

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Liu, H.; Mark, A. E.; van Gunsteren, W. F. Estimating the Relative Free Energy of Different Molecular States with Respect to a Single Reference State. J. Phys. Chem. 1996, 100 (22), 9485– 9494, DOI: 10.1021/jp9605212

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Estimating the Relative Free Energy of Different Molecular States with Respect to a Single Reference State

Liu, Haiyan; Mark, Alan E.; van Gunsteren, Wilfred F.

Journal of Physical Chemistry (1996), 100 (22), 9485-9494CODEN: JPCHAX; ISSN:0022-3654. (American Chemical Society)

The authors have investigated the feasibility of predicting free energy differences between a manifold of mol. states from a single simulation or ensemble representing one ref. state. Two formulas that are based on the so-called λ- coupling parameter approach are analyzed and compared: (i) expansion of the free energy F(λ) into a Taylor series around a ref. state (λ = 0), and (ii) the so-called free energy perturbation formula. The results obtained by these extrapolation methods are compared to exact (target) values calcd. by thermodn. integration for mutations in two mol. systems: a model dipolar diat. mol. in water, and a series of para-substituted phenols in water. For moderate charge redistribution (≈0.5 e), both extrapolation methods reproduce the exact free energy differences. For free energy changes due to a change of atom type or size, the Taylor expansion method fails completely, while the perturbation formula yields moderately accurate predictions. Both extrapolation methods fail when a mutation involves the creation or deletion of atoms, due to the poor sampling in the ref. state simulation of the configurations that are important in the end states of interest. To overcome this sampling difficulty, a procedure based on the perturbation formula and on biasing the sampling in the ref. state is proposed, in which soft-core interaction sites are incorporated into the Hamiltonian of the ref. state at positions where atoms are to be created or deleted. For mutations going from p-methylphenol to the other five differently para-substituted phenols, the differences in free energy are correctly predicted using extrapolation based on a single simulation of a biased, non-phys. ref. state. Since a large no. of mutations can be investigated using a recorded trajectory of a single simulation, the proposed method is potentially viable in practical applications such as drug design.

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Mordasini, T. Z.; McCammon, J. A. Calculations of Relative Hydration Free Energies: A Comparative Study Using Thermodynamic Integration and an Extrapolation Method Based on a Single Reference State. J. Phys. Chem. B 2000, 104, 360– 367, DOI: 10.1021/jp993102o

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Calculations of Relative Hydration Free Energies: A Comparative Study Using Thermodynamic Integration and an Extrapolation Method Based on a Single Reference State

Mordasini, Tiziana Z.; McCammon, J. Andrew

Journal of Physical Chemistry B (2000), 104 (2), 360-367CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)

The relative hydration free energies of a series of org. mols. were calcd. from mol. dynamics (MD) simulations using an extrapolation method in combination with soft-core potential scaling. This technique consists in first generating one single long trajectory using an unphys. ref. state and in using afterward this trajectory for the estn. of the free energy difference between several mols. First, we investigated the accuracy of the method for the deletion of small functional groups. A trajectory from an MD simulation of pyrogallol (1,2,3-trihydroxybenzene) was used to calc. by extrapolation the free energy changes for the mutations of pyrogallol, catechol, and phenol to benzene in water and in vacuo. The results were compared to those obtained by thermodn. integration, to exptl. data, and to values calcd. using a semiempirical method. In a second step, increasingly larger mutations were studied in order to investigate the limitations of the method. The influence of various simulation parameters (choice of the unphys. ref. state, simulation length, soft-core scaling parameter α) on the final free energy values was examd. Benzene derivs. with hydroxyalkyl and/or bulky functional groups of increasing sizes were mutated into benzene. The results for simulations both in water and in vacuo were compared to the free energy results obtained by thermodn. integration and to the exptl. values of similar mols. The results showed that for small mutations (deletion of functional groups with up to three atoms) the extrapolation method is reliable. However, the free energies calcd. for the deletion of larger functional groups showed different accuracy levels depending on the chosen simulation parameters. For the largest mutations the thermodn. integration method also showed convergence problems. This study therefore demonstrated the usefulness of the extrapolation method for mols. of similar size, but showed the difficulties of obtaining reliable results for mols. that substantially differ from each other.

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Raman, E. P.; Vanommeslaeghe, K.; MacKerell, A. D. Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations. J. Chem. Theory Comput. 2012, 8, 3513– 3525, DOI: 10.1021/ct300088r

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Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations

Raman, E. Prabhu; Vanommeslaeghe, Kenno; MacKerell, Alexander D., Jr.

Journal of Chemical Theory and Computation (2012), 8 (10), 3513-3525CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

The in silico Site Identification by Ligand Competitive Satn. (SILCS) approach identifies the binding sites of representative chem. entities on the entire protein surface, information that can be applied for computational fragment-based drug design. The authors report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single-step free energy perturbation (SSFEP) calcns. to identify site-specific favorable chem. modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the exptl. trends in relative hydration free energies of benzene analogs and for two data sets of exptl. relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins are first analyzed to identify favorable benzene binding sites, following which an ensemble of benzene-protein conformations for that site was obtained. The SSFEP protocol applied to that ensemble results in good reprodn. of exptl. free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and anal. of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.

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Stroet, M.; Koziara, K. B.; Malde, A. K.; Mark, A. E. Optimization of Empirical Force Fields by Parameter Space Mapping: A Single-Step Perturbation Approach. J. Chem. Theory Comput. 2017, 13, 6201– 6212, DOI: 10.1021/acs.jctc.7b00800

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Optimization of Empirical Force Fields by Parameter Space Mapping: A Single-Step Perturbation Approach

Stroet, Martin; Koziara, Katarzyna B.; Malde, Alpeshkumar K.; Mark, Alan E.

Journal of Chemical Theory and Computation (2017), 13 (12), 6201-6212CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

A general method for parametrizing at. interaction functions is presented. The method is based on an anal. of surfaces corresponding to the difference between calcd. and target data as a function of alternative combinations of parameters (parameter space mapping). The consideration of surfaces in parameter space as opposed to local values or gradients leads to a better understanding of the relationships between the parameters being optimized and a given set of target data. This in turn enables for a range of target data from multiple mols. to be combined in a robust manner and for the optimal region of parameter space to be trivially identified. The effectiveness of the approach is illustrated by using the method to refine the chlorine 6-12 Lennard-Jones parameters against exptl. solvation free enthalpies in water and hexane as well as the d. and heat of vaporization of the liq. at atm. pressure for a set of 10 arom.-chloro compds. simultaneously. Single-step perturbation is used to efficiently calc. solvation free enthalpies for a wide range of parameter combinations. The capacity of this approach to parametrize accurate and transferrable force fields is discussed.

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Oostenbrink, C.; Van Gunsteren, W. F. Single-Step Perturbations to Calculate Free Energy Differences from Unphysical Reference States: Limits on Size, Flexibility, and Character. J. Comput. Chem. 2003, 24, 1730– 1739, DOI: 10.1002/jcc.10304

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Single-step perturbations to calculate free energy differences from unphysical reference states: Limits on size, flexibility, and character

Oostenbrink, Chris; Van Gunsteren, Wilfred F.

Journal of Computational Chemistry (2003), 24 (14), 1730-1739CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

Relative free energies for a series of not too different compds. can be estd. accurately from a single simulation of an unphys. ref. state that encompasses the characteristic mol. features of the compds. Previously, this method has been applied to the calcn. of free energies of solvation and of ligand binding for small mols. In the present study we investigate the limits to the accuracy of the method by applying it to a realistic model of the binding of a set of rather large ligands to the protein factor Xa, a key protein in current efforts to design anticoagulation drugs. The evaluation of the binding free energies and conformations of nine derivs. of a biphenylamidino inhibitor leads to insights regarding the effect of the size, flexibility, and character of the unphys. part of the ligand in the ref. state on the accuracy of the predicted binding free energies.

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Oostenbrink, C.; van Gunsteren, W. F. Free Energies of Ligand Binding for Structurally Diverse Compounds. Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (19), 6750– 6754, DOI: 10.1073/pnas.0407404102

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Free energies of ligand binding for structurally diverse compounds

Oostenbrink, Chris; van Gunsteren, Wilfred F.

Proceedings of the National Academy of Sciences of the United States of America (2005), 102 (19), 6750-6754CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)

The one-step perturbation approach is an efficient means to calc. many relative free energies from a common ref. compd. Combining lessons learned in previous studies, an application of the method is presented that allows for the calcn. of relative binding free energies for structurally rather diverse compds. from only a few simulations. Based on the well known statistical-mech. perturbation formula, the results do not require any empirical parameters, or training sets, only limited knowledge of the binding characteristics of the ligands suffices to design appropriate ref. compds. Depending on the choice of ref. compd., relative free energies of binding rigid ligands to the ligand-binding domain of the estrogen receptor can be obtained that show good agreement with the exptl. values. The approach presented here can easily be applied to many rigid ligands, and it should be relatively easy to extend the method to account for ligand flexibility. The free-energy calcns. can be straightforwardly parallelized, allowing for an efficient means to understand and predict relative binding free energies.

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Wade, A. D.; Rizzi, A.; Wang, Y.; Huggins, D. J. Computational Fluorine Scanning Using Free-Energy Perturbation. J. Chem. Inf. Model. 2019, 59, 2776, DOI: 10.1021/acs.jcim.9b00228

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Computational Fluorine Scanning Using Free-Energy Perturbation

Wade, Alexander D.; Rizzi, Andrea; Wang, Yuanqing; Huggins, David J.

Journal of Chemical Information and Modeling (2019), 59 (6), 2776-2784CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

We present perturbative fluorine scanning, a computational fluorine scanning approach using free-energy perturbation. This method can be applied to mol. dynamics simulations of a single compd. and make predictions for the best binders out of numerous fluorinated analogs. We tested the method on nine test systems: renin, DPP4, menin, P38, factor Xa, CDK2, AKT, JAK2, and androgen receptor. The predictions were in excellent agreement with more rigorous alchem. free-energy calcns. and in good agreement with exptl. data for most of the test systems. However, the agreement with expt. was very poor in some of the test systems, and this highlights the need for improved force fields in addn. to accurate treatment of tautomeric and protonation states. The method is of particular interest due to the wide use of fluorine in medicinal chem. to improve binding affinity and ADME properties. The promising results on this test case suggest that perturbative fluorine scanning will be a useful addn. to the available arsenal of free-energy methods.

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Raman, E. P.; Lakkaraju, S. K.; Denny, R. A.; MacKerell, A. D. Estimation of Relative Free Energies of Binding Using Pre-Computed Ensembles Based on the Single-Step Free Energy Perturbation and the Site-Identification by Ligand Competitive Saturation Approaches. J. Comput. Chem. 2017, 38, 1238– 1251, DOI: 10.1002/jcc.24522

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Estimation of relative free energies of binding using pre-computed ensembles based on the single-step free energy perturbation and the site-identification by Ligand competitive saturation approaches

Raman, E. Prabhu; Lakkaraju, Sirish Kaushik; Denny, Rajiah Aldrin; MacKerell, Alexander D. Jr

Journal of Computational Chemistry (2017), 38 (15), 1238-1251CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

Accurate and rapid estn. of relative binding affinities of ligand-protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design. In the present study relative free energies of binding are estd. for one or two nonhydrogen atom changes in compds. targeting the proteins ACK1 and p38 MAP kinase using three methods. The methods include std. FEP, single-step free energy perturbation (SSFEP) and the site-identification by ligand competitive satn. (SILCS) ligand grid free energy (LGFE) approach. Results show the SSFEP and SILCS LGFE methods to be competitive with or better than the FEP results for the studied systems, with SILCS LGFE giving the best agreement with exptl. results. This is supported by addnl. comparisons with published FEP data on p38 MAP kinase inhibitors. While both the SSFEP and SILCS LGFE approaches require a significant upfront computational investment, they offer a 1000-fold computational savings over FEP for calcg. the relative affinities of ligand modifications once those precomputations are complete. An illustrative example of the potential application of these methods in the context of screening large nos. of transformations is presented. Thus, the SSFEP and SILCS LGFE approaches represent viable alternatives for actively driving ligand design during drug discovery and development. © 2016 Wiley Periodicals, Inc.

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Oostenbrink, C. Free Energy Calculations from One-Step Perturbations. Methods Mol. Biol. 2012, 819, 487– 499, DOI: 10.1007/978-1-61779-465-0_28

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Free energy calculations from one-step perturbations

Oostenbrink, Chris

Methods in Molecular Biology (New York, NY, United States) (2012), 819 (Computational Drug Discovery and Design), 487-499CODEN: MMBIED; ISSN:1064-3745. (Springer)

The one-step perturbation approach offers an efficient means to est. free energy differences. It may be applied to est. solvation free energies, conformational preferences or relative free energies of binding of series of compds. to a common receptor. Applicability of the method depends on the possibility to define a proper ref. state which may in itself be an unphys. mol. Here, we describe practical considerations and explicit guidelines to define a proper ref. state, and to efficiently calc. relative free energies. The strengths and limitations of the method are highlighted and special considerations are noted. The method may be applied using many different simulation programs. Here, analyses are exemplified at the hand of the GROMOS simulation package.

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Baron, R. Computational Drug Discovery and Design; Baron, R., Ed.; Springer: New York, 2012.

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47
Graf, M. M. H.; Zhixiong, L.; Bren, U.; Haltrich, D.; van Gunsteren, W. F.; Oostenbrink, C. Pyranose Dehydrogenase Ligand Promiscuity: A Generalized Approach to Simulate Monosaccharide Solvation, Binding, and Product Formation. PLoS Comput. Biol. 2014, 10 (12), e1003995 DOI: 10.1371/journal.pcbi.1003995

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Pyranose dehydrogenase ligand promiscuity: a generalized approach to simulate monosaccharide solvation, binding, and product formation

Graf, Michael M. H.; Lin, Zhixiong; Bren, Urban; Haltrich, Dietmar; van Gunsteren, Wilfred F.; Oostenbrink, Chris

PLoS Computational Biology (2014), 10 (12), e1003995/1-e1003995/13, 13 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)

The flavoenzyme pyranose dehydrogenase (PDH) from the litter decompg. fungus Agaricus meleagris oxidizes many different carbohydrates occurring during lignin degrdn. This promiscuous substrate specificity makes PDH a promising catalyst for bioelectrochem. applications. A generalized approach to simulate all 32 possible aldohexopyranoses in the course of one or a few mol. dynamics (MD) simulations is reported. Free energy calcns. according to the one-step perturbation (OSP) method revealed the solvation free energies (ΔGsolv) of all 32 aldohexopyranoses in water, which have not yet been reported in the literature. The free energy difference between β- and α-anomers (ΔGβ-α) of all D-stereoisomers in water were compared to exptl. values with a good agreement. Moreover, the free-energy differences (ΔG) of the 32 stereoisomers bound to PDH in two different poses were calcd. from MD simulations. The relative binding free energies (ΔΔGbind) were calcd. and, where available, compared to exptl. values, approximated from Km values. The agreement was very good for one of the poses, in which the sugars are positioned in the active site for oxidn. at C1 or C2. Distance anal. between hydrogens of the monosaccharide and the reactive N5-atom of the FAD revealed that oxidn. is possible at HC1 or HC2 for pose A, and at HC3 or HC4 for pose B. Exptl. detected oxidn. products could be rationalized for the majority of monosaccharides by combining ΔΔGbind and a reweighted distance anal. Furthermore, several oxidn. products were predicted for sugars that have not yet been tested exptl., directing further analyses. This study rationalizes the relationship between binding free energies and substrate promiscuity in PDH, providing novel insights for its applicability in bioelectrochem. The results suggest that a similar approach could be applied to study promiscuity of other enzymes.

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Perthold, J. W.; Petrov, D.; Oostenbrink, C. Towards Automated Free Energy Calculation with Accelerated Enveloping Distribution Sampling (A-EDS). J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00456 .

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Perthold, J. W.; Oostenbrink, C. Accelerated Enveloping Distribution Sampling: Enabling Sampling of Multiple End States While Preserving Local Energy Minima. J. Phys. Chem. B 2018, 122 (19), 5030– 5037, DOI: 10.1021/acs.jpcb.8b02725

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Accelerated Enveloping Distribution Sampling: Enabling Sampling of Multiple End States while Preserving Local Energy Minima

Perthold, Jan Walther; Oostenbrink, Chris

Journal of Physical Chemistry B (2018), 122 (19), 5030-5037CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)

Enveloping distribution sampling (EDS) is an efficient approach to calc. multiple free-energy differences from a single mol. dynamics (MD) simulation. However, the construction of an appropriate ref.-state Hamiltonian that samples all states efficiently is not straightforward. We propose a novel approach for the construction of the EDS ref.-state Hamiltonian, related to a previously described procedure to smoothen energy landscapes. In contrast to previously suggested EDS approaches, our ref.-state Hamiltonian preserves local energy min. of the combined end-states. Moreover, we propose an intuitive, robust and efficient parameter optimization scheme to tune EDS Hamiltonian parameters. We demonstrate the proposed method with established and novel test systems and conclude that our approach allows for the automated calcn. of multiple free-energy differences from a single simulation. Accelerated EDS promises to be a robust and user-friendly method to compute free-energy differences based on solid statistical mechanics.

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Aldeghi, M.; Heifetz, A.; Bodkin, M. J.; Knapp, S.; Biggin, P. C. Accurate Calculation of the Absolute Free Energy of Binding for Drug Molecules. Chem. Sci. 2016, 7 (1), 207– 218, DOI: 10.1039/C5SC02678D

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Accurate calculation of the absolute free energy of binding for drug molecules

Aldeghi, Matteo; Heifetz, Alexander; Bodkin, Michael J.; Knapp, Stefan; Biggin, Philip C.

Chemical Science (2016), 7 (1), 207-218CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)

Accurate prediction of binding affinities has been a central goal of computational chem. for decades, yet remains elusive. Despite good progress, the required accuracy for use in a drug-discovery context has not been consistently achieved for drug-like mols. Here, we perform abs. free energy calcns. based on a thermodn. cycle for a set of diverse inhibitors binding to bromodomain-contg. protein 4 (BRD4) and demonstrate that a mean abs. error of 0.6 kcal mol-1 can be achieved. We also show a similar level of accuracy (1.0 kcal mol-1) can be achieved in pseudo prospective approach. Bromodomains are epigenetic mark readers that recognize acetylation motifs and regulate gene transcription, and are currently being investigated as therapeutic targets for cancer and inflammation. The unprecedented accuracy offers the exciting prospect that the binding free energy of drug-like compds. can be predicted for pharmacol. relevant targets.

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Woods, C. J.; Essex, J. W.; King, M. A. The Development of Replica-Exchange-Based Free-Energy Methods. J. Phys. Chem. B 2003, 107 (49), 13703– 13710, DOI: 10.1021/jp0356620

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The Development of Replica-Exchange-Based Free-Energy Methods

Woods, Christopher J.; Essex, Jonathan W.; King, Michael A.

Journal of Physical Chemistry B (2003), 107 (49), 13703-13710CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)

The calcn. of relative free energies that involve large reorganizations of the environment is one of the great challenges of condensed-phase simulation. Such calcns. are of particular importance in protein-ligand free-energy calcns. To meet this challenge, we have developed new free-energy techniques that combine the advantages of the replica-exchange method with free-energy perturbation (FEP) and finite-difference thermodn. integration (FDTI). These new techniques are tested and compared with FEP, FDTI, and the adaptive umbrella weighted histogram anal. method (AdUmWHAM) on the challenging calcn. of the relative hydration free energy of methane and water. This calcn. involves a large solvent configurational change. Through the use of replica-exchange moves along the λ-coordinate, the configurations sampled along λ are allowed to mix, which leads to dramatic improvements in solvent configurational sampling, an efficient redn. of random sampling error, and a redn. of general simulation error. This is achieved at effectively no extra computational cost, relative to std. FEP or FDTI.

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Chodera, J. D.; Shirts, M. R. Replica Exchange and Expanded Ensemble Simulations as Gibbs Sampling: Simple Improvements for Enhanced Mixing. J. Chem. Phys. 2011, 135 (19), 194110, DOI: 10.1063/1.3660669

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Replica exchange and expanded ensemble simulations as Gibbs sampling: Simple improvements for enhanced mixing

Chodera, John D.; Shirts, Michael R.

Journal of Chemical Physics (2011), 135 (19), 194110/1-194110/15CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)

The widespread popularity of replica exchange and expanded ensemble algorithms for simulating complex mol. systems in chem. and biophysics has generated much interest in discovering new ways to enhance the phase space mixing of these protocols in order to improve sampling of uncorrelated configurations. Here, we demonstrate how both of these classes of algorithms can be considered as special cases of Gibbs sampling within a Markov chain Monte Carlo framework. Gibbs sampling is a well-studied scheme in the field of statistical inference in which different random variables are alternately updated from conditional distributions. While the update of the conformational degrees of freedom by Metropolis Monte Carlo or mol. dynamics unavoidably generates correlated samples, we show how judicious updating of the thermodn. state indexes-corresponding to thermodn. parameters such as temp. or alchem. coupling variables-can substantially increase mixing while still sampling from the desired distributions. We show how state update methods in common use can lead to suboptimal mixing, and present some simple, inexpensive alternatives that can increase mixing of the overall Markov chain, reducing simulation times necessary to obtain ests. of the desired precision. These improved schemes are demonstrated for several common applications, including an alchem. expanded ensemble simulation, parallel tempering, and multidimensional replica exchange umbrella sampling. (c) 2011 American Institute of Physics.

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Macdonald, H. B.; Cave-Ayland, C.; Essex, J. Predicting Water Networks and Ligand Binding Free Energies in Proteins Using Grand Canonical Monte Carlo. In Abstracts of Papers of the American Chemical Society; American Chemical Society: Washington, DC, 2018; Vol. 255.
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Ross, G. A.; Bruce Macdonald, H. E.; Cave-Ayland, C.; Cabedo Martinez, A. I.; Essex, J. W. Replica-Exchange and Standard State Binding Free Energies with Grand Canonical Monte Carlo. J. Chem. Theory Comput. 2017, 13 (12), 6373– 6381, DOI: 10.1021/acs.jctc.7b00738

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Replica-Exchange and Standard State Binding Free Energies with Grand Canonical Monte Carlo

Ross, Gregory A.; Bruce Macdonald, Hannah E.; Cave-Ayland, Christopher; Cabedo Martinez, Ana I.; Essex, Jonathan W.

Journal of Chemical Theory and Computation (2017), 13 (12), 6373-6381CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

The ability of grand canonical Monte Carlo (GCMC) to create and annihilate mols. in a given region greatly aids the identification of water sites and water binding free energies in protein cavities. However, acceptance rates without the application of biased moves can be low, resulting in large variations in the obsd. water occupancies. Here, we show that replica exchange of the chem. potential significantly reduces the variance of the GCMC data. This improvement comes at a negligible increase in computational expense when simulations comprise of runs at different chem. potentials. Replica exchange GCMC is also found to substantially increase of the precision of std. state water binding free energies as calcd. with grand canonical integration, which has allowed us to address a missing std. state correction.

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Ben-Shalom, I. Y.; Lin, C.; Kurtzman, T.; Walker, R.; Gilson, M. K. Equilibration of Buried Water Molecules to Enhance Protein-Ligand Binding Free Energy Calculations. Biophys. J. 2020, 118 (3), 144a, DOI: 10.1016/j.bpj.2019.11.908

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Jagger, B. R.; Kochanek, S. E.; Haldar, S.; Amaro, R. E.; Mulholland, A. J. Multiscale Simulation Approaches to Modeling Drug–protein Binding. Curr. Opin. Struct. Biol. 2020, 61, 213– 221, DOI: 10.1016/j.sbi.2020.01.014

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Multiscale simulation approaches to modeling drug-protein binding

Jagger, Benjamin R.; Kochanek, Sarah E.; Haldar, Susanta; Amaro, Rommie E.; Mulholland, Adrian J.

Current Opinion in Structural Biology (2020), 61 (), 213-221CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)

A review. Simulations can provide detailed insight into the mol. processes involved in drug action, such as protein-ligand binding, and can therefore be a valuable tool for drug design and development. Processes with a large range of length and timescales may be involved, and understanding these different scales typically requires different types of simulation methodol. Ideally, simulations should be able to connect across scales, to analyze and predict how changes at one scale can influence another. Multiscale simulation methods, which combine different levels of treatment, are an emerging frontier with great potential in this area. Here we review multiscale frameworks of various types, and selected applications to biomol. systems with a focus on drug-ligand binding.

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Haldar, S.; Comitani, F.; Saladino, G.; Woods, C.; van der Kamp, M. W.; Mulholland, A. J.; Gervasio, F. L. A Multiscale Simulation Approach to Modeling Drug–Protein Binding Kinetics. J. Chem. Theory Comput. 2018, 14 (11), 6093– 6101, DOI: 10.1021/acs.jctc.8b00687

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A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics

Haldar, Susanta; Comitani, Federico; Saladino, Giorgio; Woods, Christopher; van der Kamp, Marc W.; Mulholland, Adrian J.; Gervasio, Francesco Luigi

Journal of Chemical Theory and Computation (2018), 14 (11), 6093-6101CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

Drug-target binding kinetics has recently emerged as a sometimes crit. determinant of in vivo efficacy and toxicity. Its rational optimization to improve potency or reduce side effects of drugs is, however, extremely difficult. Mol. simulations can play a crucial role in identifying features and properties of small ligands and their protein targets affecting the binding kinetics, but significant challenges include the long timescales involved in (un)binding events and the limited accuracy of empirical atomistic force-fields (lacking e.g. changes in electronic polarization). In an effort to overcome these hurdles, we propose a method that combines state-of-the-art enhanced sampling simulations and quantum mechanics/mol. mechanics (QM/MM) calcns. at the BLYP/VDZ level to compute assocn. free energy profiles and characterize the binding kinetics in terms of structure and dynamics of the transition state ensemble. We test our combined approach on the binding of the anticancer drug imatinib to Src kinase, a well-characterized target for cancer therapy with a complex binding mechanism involving significant conformational changes. The results indicate significant changes in polarization along the binding pathways, which affect the predicted binding kinetics. This is likely to be of widespread importance in ligand-target binding.

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Hamann, L. G.; Manfredi, M. C.; Sun, C.; Krystek, S. R.; Huang, Y.; Bi, Y.; Augeri, D. J.; Wang, T.; Zou, Y.; Betebenner, D. A.; Fura, A.; Seethala, R.; Golla, R.; Kuhns, J. E.; Lupisella, J. A.; Darienzo, C. J.; Custer, L. L.; Price, J. L.; Johnson, J. M.; Biller, S. A.; Zahler, R.; Ostrowski, J. Tandem Optimization of Target Activity and Elimination of Mutagenic Potential in a Potent Series of N-Aryl Bicyclic Hydantoin-Based Selective Androgen Receptor Modulators. Bioorg. Med. Chem. Lett. 2007, 17 (7), 1860– 1864, DOI: 10.1016/j.bmcl.2007.01.076

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Tandem optimization of target activity and elimination of mutagenic potential in a potent series of N-aryl bicyclic hydantoin-based selective androgen receptor modulators

Hamann, Lawrence G.; Manfredi, Mark C.; Sun, Chongqing; Krystek, Stanley R.; Huang, Yanting; Bi, Yingzhi; Augeri, David J.; Wang, Tammy; Zou, Yan; Betebenner, David A.; Fura, Aberra; Seethala, Ramakrishna; Golla, Rajasree; Kuhns, Joyce E.; Lupisella, John A.; Darienzo, Celia J.; Custer, Laura L.; Price, Jennifer L.; Johnson, James M.; Biller, Scott A.; Zahler, Robert; Ostrowski, Jacek

Bioorganic & Medicinal Chemistry Letters (2007), 17 (7), 1860-1864CODEN: BMCLE8; ISSN:0960-894X. (Elsevier Ltd.)

Pharmacokinetic studies in cynomolgus monkeys with a novel prototype selective androgen receptor modulator revealed trace amts. of an aniline fragment released through hydrolytic metab. This aniline fragment was detd. to be mutagenic in an Ames assay. Subsequent concurrent optimization for target activity and avoidance of mutagenicity led to the identification of a pharmacol. superior clin. candidate without mutagenic potential.

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Ostrowski, J.; Kuhns, J. E.; Lupisella, J. A.; Manfredi, M. C.; Beehler, B. C.; Krystek, S. R., Jr; Bi, Y.; Sun, C.; Seethala, R.; Golla, R.; Sleph, P. G.; Fura, A.; An, Y.; Kish, K. F.; Sack, J. S.; Mookhtiar, K. A.; Grover, G. J.; Hamann, L. G. Pharmacological and X-Ray Structural Characterization of a Novel Selective Androgen Receptor Modulator: Potent Hyperanabolic Stimulation of Skeletal Muscle with Hypostimulation of Prostate in Rats. Endocrinology 2007, 148 (1), 4– 12, DOI: 10.1210/en.2006-0843

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Pharmacological and X-ray structural characterization of a novel selective androgen receptor modulator: potent hyperanabolic stimulation of skeletal muscle with hypostimulation of prostate in rats

Ostrowski, Jacek; Kuhns, Joyce E.; Lupisella, John A.; Manfredi, Mark C.; Beehler, Blake C.; Krystek, Stanley R., Jr.; Bi, Yingzhi; Sun, Chongqing; Seethala, Ramakrishna; Golla, Rajasree; Sleph, Paul G.; Fura, Aberra; An, Yongmi; Kish, Kevin F.; Sack, John S.; Mookhtiar, Kasim A.; Grover, Gary J.; Hamann, Lawrence G.

Endocrinology (2007), 148 (1), 4-12CODEN: ENDOAO; ISSN:0013-7227. (Endocrine Society)

A novel, highly potent, orally active, nonsteroidal tissue selective androgen receptor (AR) modulator (BMS-564929) has been identified, and this compd. has been advanced to clin. trials for the treatment of age-related functional decline. BMS-564929 is a subnanomolar AR agonist in vitro, is highly selective for the AR vs. other steroid hormone receptors, and exhibits no significant interactions with SHBG or aromatase. Dose response studies in castrated male rats show that BMS-564929 is substantially more potent than testosterone (T) in stimulating the growth of the levator ani muscle, and unlike T, highly selective for muscle vs. prostate. Key differences in the binding interactions of BMS-564929 with the AR relative to the native hormones were revealed through x-ray crystallog., including several unique contacts located in specific helixes of the ligand binding domain important for coregulatory protein recruitment. Results from addnl. pharmacol. studies effectively exclude alternative mechanistic contributions to the obsd. tissue selectivity of this unique, orally active androgen. Because concerns regarding the potential hyperstimulatory effects on prostate and an inconvenient route of administration are major drawbacks that limit the clin. use of T, the potent oral activity and tissue selectivity exhibited by BMS-564929 are expected to yield a clin. profile that provides the demonstrated beneficial effects of T in muscle and other tissues with a more favorable safety window.

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Matter, H.; Scheiper, B.; Steinhagen, H.; Böcskei, Z.; Fleury, V.; McCort, G. Structure-Based Design and Optimization of Potent Renin Inhibitors on 5- or 7-Azaindole-Scaffolds. Bioorg. Med. Chem. Lett. 2011, 21 (18), 5487– 5492, DOI: 10.1016/j.bmcl.2011.06.112

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Structure-based design and optimization of potent renin inhibitors on 5- or 7-azaindole-scaffolds

Matter, Hans; Scheiper, Bodo; Steinhagen, Henning; Boecskei, Zsolt; Fleury, Valerie; McCort, Gary

Bioorganic & Medicinal Chemistry Letters (2011), 21 (18), 5487-5492CODEN: BMCLE8; ISSN:0960-894X. (Elsevier B.V.)

The selective inhibition of the aspartyl protease renin is of high interest to control hypertension and assocd. cardiovascular risk factors. Following on preceding contributions, we report herein on the optimization of two series of azaindoles to arrive at potent and non-chiral renin inhibitors. The previously discovered azaindole scaffold was further explored by structure-based drug design in combination with parallel synthesis. This results in the identification of novel 5- or 7-azaindole derivs. with remarkable potency for renin inhibition. The best compds. on both series show IC50 values between 3 and 8 nM.

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He, S.; Senter, T. J.; Pollock, J.; Han, C.; Upadhyay, S. K.; Purohit, T.; Gogliotti, R. D.; Lindsley, C. W.; Cierpicki, T.; Stauffer, S. R.; Grembecka, J. High-Affinity Small-Molecule Inhibitors of the Menin-Mixed Lineage Leukemia (MLL) Interaction Closely Mimic a Natural Protein–Protein Interaction. J. Med. Chem. 2014, 57 (4), 1543– 1556, DOI: 10.1021/jm401868d

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High-Affinity Small-Molecule Inhibitors of the Menin-Mixed Lineage Leukemia (MLL) Interaction Closely Mimic a Natural Protein-Protein Interaction

He, Shihan; Senter, Timothy J.; Pollock, Jonathan; Han, Changho; Upadhyay, Sunil Kumar; Purohit, Trupta; Gogliotti, Rocco D.; Lindsley, Craig W.; Cierpicki, Tomasz; Stauffer, Shaun R.; Grembecka, Jolanta

Journal of Medicinal Chemistry (2014), 57 (4), 1543-1556CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)

The protein-protein interaction (PPI) between menin and mixed lineage leukemia (MLL) plays a crit. role in acute leukemias, and inhibition of this interaction represents a new potential therapeutic strategy for MLL leukemias. We report development of a novel class of small-mol. inhibitors of the menin-MLL interaction, the hydroxy- and aminomethylpiperidine compds., which originated from HTS of ∼288000 small mols. We detd. menin-inhibitor co-crystal structures and found that these compds. closely mimic all key interactions of MLL with menin. Extensive crystallog. studies combined with structure-based design were applied for optimization of these compds., resulting in MIV-6R, which inhibits the menin-MLL interaction with IC50 = 56 nM. Treatment with MIV-6 demonstrated strong and selective effects in MLL leukemia cells, validating specific mechanism of action. Our studies provide novel and attractive scaffold as a new potential therapeutic approach for MLL leukemias and demonstrate an example of PPI amenable to inhibition by small mols.

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Baum, B.; Muley, L.; Smolinski, M.; Heine, A.; Hangauer, D.; Klebe, G. Non-Additivity of Functional Group Contributions in Protein–Ligand Binding: A Comprehensive Study by Crystallography and Isothermal Titration Calorimetry. J. Mol. Biol. 2010, 397 (4), 1042– 1054, DOI: 10.1016/j.jmb.2010.02.007

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Non-additivity of Functional Group Contributions in Protein-Ligand Binding: A Comprehensive Study by Crystallography and Isothermal Titration Calorimetry

Baum, Bernhard; Muley, Laveena; Smolinski, Michael; Heine, Andreas; Hangauer, David; Klebe, Gerhard

Journal of Molecular Biology (2010), 397 (4), 1042-1054CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)

Additivity of functional group contributions to protein-ligand binding is a very popular concept in medicinal chem. as the basis of rational design and optimized lead structures. Most of the currently applied scoring functions for docking build on such additivity models. Even though the limitation of this concept is well known, case studies examg. in detail why additivity fails at the mol. level are still very scarce. The present study shows, by use of crystal structure anal. and isothermal titrn. calorimetry for a congeneric series of thrombin inhibitors, that extensive cooperative effects between hydrophobic contacts and hydrogen bond formation are intimately coupled via dynamic properties of the formed complexes. The formation of optimal lipophilic contacts with the surface of the thrombin S3 pocket and the full desolvation of this pocket can conflict with the formation of an optimal hydrogen bond between ligand and protein. The mutual contributions of the competing interactions depend on the size of the ligand hydrophobic substituent and influence the residual mobility of ligand portions at the binding site. Anal. of the individual crystal structures and factorizing the free energy into enthalpy and entropy demonstrates that binding affinity of the ligands results from a mixt. of enthalpic contributions from hydrogen bonding and hydrophobic contacts, and entropic considerations involving an increasing loss of residual mobility of the bound ligands. This complex picture of mutually competing and partially compensating enthalpic and entropic effects dets. the non-additivity of free energy contributions to ligand binding at the mol. level.

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Ghosh, A. K.; Takayama, J.; Aubin, Y.; Ratia, K.; Chaudhuri, R.; Baez, Y.; Sleeman, K.; Coughlin, M.; Nichols, D. B.; Mulhearn, D. C.; Prabhakar, B. S.; Baker, S. C.; Johnson, M. E.; Mesecar, A. D. Structure-Based Design, Synthesis, and Biological Evaluation of a Series of Novel and Reversible Inhibitors for the Severe Acute Respiratory Syndrome- Coronavirus Papain-Like Protease. J. Med. Chem. 2009, 52 (16), 5228– 5240, DOI: 10.1021/jm900611t

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Structure-Based Design, Synthesis, and Biological Evaluation of a Series of Novel and Reversible Inhibitors for the Severe Acute Respiratory Syndrome-Coronavirus Papain-Like Protease

Ghosh, Arun K.; Takayama, Jun; Aubin, Yoann; Ratia, Kiira; Chaudhuri, Rima; Baez, Yahira; Sleeman, Katrina; Coughlin, Melissa; Nichols, Daniel B.; Mulhearn, Debbie C.; Prabhakar, Bellur S.; Baker, Susan C.; Johnson, Michael E.; Mesecar, Andrew D.

Journal of Medicinal Chemistry (2009), 52 (16), 5228-5240CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)

We describe here the design, synthesis, mol. modeling, and biol. evaluation of a series of small mol., nonpeptide inhibitors of SARS-CoV PLpro. Our initial lead compd. was identified via high-throughput screening of a diverse chem. library. We subsequently carried out structure-activity relationship studies and optimized the lead structure to potent inhibitors that have shown antiviral activity against SARS-CoV infected Vero E6 cells. Upon the basis of the X-ray crystal structure of inhibitor (III)-bound to SARS-CoV PLpro, a drug design template was created. Our structure-based modification led to the design of a more potent inhibitor, (I) (enzyme IC50 = 0.46 μM; antiviral EC50 = 6 μM). Interestingly, its methylamine deriv., (II), displayed good enzyme inhibitory potency (IC50 = 1.3 μM) and the most potent SARS antiviral activity (EC50 = 5.2 μM) in the series. We have carried out computational docking studies and generated a predictive 3D-QSAR model for SARS-CoV PLpro inhibitors.

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Ratia, K.; Pegan, S.; Takayama, J.; Sleeman, K.; Coughlin, M.; Baliji, S.; Chaudhuri, R.; Fu, W.; Prabhakar, B. S.; Johnson, M. E.; Baker, S. C.; Ghosh, A. K.; Mesecar, A. D. A Noncovalent Class of Papain-like Protease/deubiquitinase Inhibitors Blocks SARS Virus Replication. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (42), 16119– 16124, DOI: 10.1073/pnas.0805240105

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A noncovalent class of papain-like protease/deubiquitinase inhibitors blocks SARS virus replication

Ratia, Kiira; Pegan, Scott; Takayama, Jun; Sleeman, Katrina; Coughlin, Melissa; Baliji, Surendranath; Chaudhuri, Rima; Fu, Wentao; Prabhakar, Bellur S.; Johnson, Michael E.; Baker, Susan C.; Ghosh, Arun K.; Mesecar, Andrew D.

Proceedings of the National Academy of Sciences of the United States of America (2008), 105 (42), 16119-16124CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)

We report the discovery and optimization of a potent inhibitor against the papain-like protease (PLpro) from the coronavirus that causes severe acute respiratory syndrome (SARS-CoV). This unique protease is not only responsible for processing the viral polyprotein into its functional units but is also capable of cleaving ubiquitin and ISG15 conjugates and plays a significant role in helping SARS-CoV evade the human immune system. We screened a structurally diverse library of 50,080 compds. for inhibitors of PLpro and discovered a noncovalent lead inhibitor with an IC50 value of 20 μM, which was improved to 600 nM via synthetic optimization. The resulting compd., GRL0617, inhibited SARS-CoV viral replication in Vero E6 cells with an EC50 of 15 μM and had no assocd. cytotoxicity. The X-ray structure of PLpro in complex with GRL0617 indicates that the compd. has a unique mode of inhibition whereby it binds within the 54-53 subsites of the enzyme and induces a loop closure that shuts down catalysis at the active site. These findings provide proof-of-principle that PLpro is a viable target for development of antivirals directed against SARS-CoV, and that potent noncovalent cysteine protease inhibitors can be developed with specificity directed toward pathogenic deubiquitinating enzymes without inhibiting host DUBs.

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Maestro, version 9.1; Schrödinger, LLC: New York, 2010.
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Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Antechamber: An Accessory Software Package for Molecular Mechanical Calculations. J. Am. Chem. Soc. 2001, 222, U403
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Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and Testing of a General Amber Force Field. J. Comput. Chem. 2004, 25 (9), 1157– 1174, DOI: 10.1002/jcc.20035

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Development and testing of a general Amber force field

Wang, Junmei; Wolf, Romain M.; Caldwell, James W.; Kollman, Peter A.; Case, David A.

Journal of Computational Chemistry (2004), 25 (9), 1157-1174CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

We describe here a general Amber force field (GAFF) for org. mols. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most org. and pharmaceutical mols. that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited no. of atom types, but incorporates both empirical and heuristic models to est. force consts. and partial at. charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallog. structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 Å, which is comparable to that of the Tripos 5.2 force field (0.25 Å) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 Å, resp.). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermol. energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 Å and 1.2 kcal/mol, resp. These data are comparable to results from Parm99/RESP (0.16 Å and 1.18 kcal/mol, resp.), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to expt.) is about 0.5 kcal/mol. GAFF can be applied to wide range of mols. in an automatic fashion, making it suitable for rational drug design and database searching.

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Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: II. Parameterization and Validation. J. Comput. Chem. 2002, 23 (16), 1623– 1641, DOI: 10.1002/jcc.10128

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Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. parameterization and validation

Jakalian, Araz; Jack, David B.; Bayly, Christopher I.

Journal of Computational Chemistry (2002), 23 (16), 1623-1641CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

We present the first global parameterization and validation of a novel charge model, called AM1-BCC, which quickly and efficiently generates high-quality at. charges for computer simulations of org. mols. in polar media. The goal of the charge model is to produce at. charges that emulate the HF/6-31G* electrostatic potential (ESP) of a mol. Underlying electronic structure features, including formal charge and electron delocalization, are first captured by AM1 population charges; simple additive bond charge corrections (BCCs) are then applied to these AM1 at. charges to produce the AM1-BCC charges. The parameterization of BCCs was carried out by fitting to the HF/6-31G* ESP of a training set of >2700 mols. Most org. functional groups and their combinations were sampled, as well as an extensive variety of cyclic and fused bicyclic heteroaryl systems. The resulting BCC parameters allow the AM1-BCC charging scheme to handle virtually all types of org. compds. listed in The Merck Index and the NCI Database. Validation of the model was done through comparisons of hydrogen-bonded dimer energies and relative free energies of solvation using AM1-BCC charges in conjunction with the 1994 Cornell et al. forcefield for AMBER. Homo-dimer and hetero-dimer hydrogen-bond energies of a diverse set of org. mols. were reproduced to within 0.95 kcal/mol RMS deviation from the ab initio values, and for DNA dimers the energies were within 0.9 kcal/mol RMS deviation from ab initio values. The calcd. relative free energies of solvation for a diverse set of monofunctional isosteres were reproduced to within 0.69 kcal/mol of expt. In all these validation tests, AMBER with the AM1-BCC charge model maintained a correlation coeff. above 0.96. Thus, the parameters presented here for use with the AM1-BCC method present a fast, accurate, and robust alternative to HF/6-31G* ESP-fit charges for general use with the AMBER force field in computer simulations involving org. small mols.

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Naden, L. N.; Shirts, M. R. Linear Basis Function Approach to Efficient Alchemical Free Energy Calculations. 2. Inserting and Deleting Particles with Coulombic Interactions. J. Chem. Theory Comput. 2015, 11 (6), 2536– 2549, DOI: 10.1021/ct501047e

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Linear Basis Function Approach to Efficient Alchemical Free Energy Calculations. 2. Inserting and Deleting Particles with Coulombic Interactions

Naden, Levi N.; Shirts, Michael R.

Journal of Chemical Theory and Computation (2015), 11 (6), 2536-2549CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

We extend our previous linear basis function approach for alchem. free energy calcns. to the insertion and deletion of charged particles in dense fluids. We compute a near optimal statistical path to introduce Coulombic interactions into various mols. in soln. and find that this near optimal path is only marginally more efficient than simple linear coupling of electrostatics in all cases where a repulsive core is already present. We also explore the order in which nonbonded forces are coupled to the environment in alchem. transformations. We test two sets of Lennard-Jones basis functions, a Weeks-Chandler-Andersen (WCA) and a 12-6 decompn. of the repulsive and attractive forces turned on in sequence along with changes in charge, to det. a statistically optimized order in which forces should be coupled. The WCA decompn. has lower statistical uncertainty as coupling the attractive r-6 basis function contributes non-negligible statistical error. In all cases, the charge should be coupled only after the repulsive core is fully coupled, and the WCA attractive portion can be coupled at any stage without significantly changing the efficiency. The statistical uncertainty of two of the basis function approaches with charged particles is nearly identical to the soft core approach for decoupling electrostatics, though the correlation times for sampling are often longer for a soft core electrostatics approach than the basis function approach. The basis function approach for introducing or removing mols. or functional groups thus represents a useful alternative to the soft core approach with a no. of clear computational advantages.

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Pechlaner, M.; Reif, M. M.; Oostenbrink, C. Reparametrisation of United-Atom Amine Solvation in the GROMOS Force Field. Mol. Phys. 2017, 115 (9–12), 1144– 1154, DOI: 10.1080/00268976.2016.1255797

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Reparametrisation of united-atom amine solvation in the GROMOS force field

Pechlaner, Maria; Reif, Maria M.; Oostenbrink, Chris

Molecular Physics (2017), 115 (9-12), 1144-1154CODEN: MOPHAM; ISSN:0026-8976. (Taylor & Francis Ltd.)

A review. New parameters for ammonia, mono-, di- and trimethylated amine compatible with GROMOS force fields are presented. A directed search in parameter space by steepest descent minimisation led to an optimized charge set with good agreement to exptl. data on abs. and relative free energies of solvation in water, chloroform and carbon tetrachloride. The final model is characterised in terms of structural and dynamic properties.

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Diem, M.; Oostenbrink, C. Hamiltonian Reweighing To Refine Protein Backbone Dihedral Angle Parameters in the GROMOS Force Field. J. Chem. Inf. Model. 2020, 60 (1), 279– 288, DOI: 10.1021/acs.jcim.9b01034

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Hamiltonian Reweighing To Refine Protein Backbone Dihedral Angle Parameters in the GROMOS Force Field

Diem, Matthias; Oostenbrink, Chris

Journal of Chemical Information and Modeling (2020), 60 (1), 279-288CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

Mol. dynamics simulations of proteins depend critically on the underlying force field, which may be parameterized against exptl. data or high-quality quantum calcns. Here, the authors develop search algorithms based on Monte Carlo and steepest descent calcns. to optimize the backbone dihedral angle parameters from a single ref. simulation. The authors apply these tools to improve the agreement between simulations of single, capped amino acids and exptl. detd. J values and secondary structure propensities of these mols. The parameters are further refined based on simulations of a set of seven proteins and finally validated in simulations on a large set of 52 protein structures. Improvements in the dihedral angle distributions are obsd., and structural propensities of the proteins are reproduced very well. Overall, the GROMOS 54A8_bb parameter set forms an improvement to previous parameter sets, both for small mols. and for protein simulations.

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Chodera, J.; Rizzi, A.; Naden, L.; Beauchamp, K.; Grinaway, P.; Fass, J.; Rustenburg, B.; Ross, G. A.; Simmonett, A.; Swenson, D. W. H. Choderalab/openmmtools: 0.14. 0-Exact Treatment of Alchemical PME Electrostatics, Water Cluster Test System, Optimizations. https://zenodo.org/record/1161149#.X0gc6HlKjIU.
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Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V. Coveney. Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility. Journal of Chemical Theory and Computation 2022, 18 (6) , 3972-3987. https://doi.org/10.1021/acs.jctc.2c00114

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Abstract

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Figure 1

Figure 1. Diagrammatic workflow for calculations performed in this work. SSP gradient method uses an exponential averaging method to calculate free energies. MBAR objective and FEP scan both use the MBAR estimator.

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Figure 2

Figure 2. Convergence for a calculation of the objective in the androgen receptor test case. ΔΔG_opt is reported as mean of three replicates with shaded area showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees of freedom, and SEM is the standard error of the mean computed from the sample standard deviation of the three independent replicate predictions.

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Figure 3

Figure 3. 3D structure for the androgen receptor ligand with all the optimized hydrogens explicitly labeled.

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Figure 4

Figure 4. Convergence for a calculation of the gradient in the androgen receptor test case. ΔΔG_grag/h is reported as the mean of three replicates with the shaded area showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees. Atom names in the legend can be seen in Figure 3.

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Figure 5

Figure 5. Cumulative ΔΔG_opt calculated by summing the MBAR objective for each step of the optimization plotted against optimization step for every system.

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Figure 6

Figure 6. ΔΔG_scan for each methylation in the androgen receptor system as the amount of sampling is increased. ΔΔG_scan are reported as mean of three replicates with shaded area showing 95% confidence interval computed as mean ± t₂·SEM, where t₂ is the t-distribution statistic with two degrees of freedom, and SEM is the standard error of the mean computed from the sample standard deviation of the three independent replicate predictions. All hydrogen names are taken from Figure 3.

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Figure 7

Figure 7. Androgen receptor ligand with all optimized hydrogens sized in proportion to their calculated Δσ.

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Figure 8

Figure 8. SARS PLPro ligand with all optimized hydrogens sized in proportion to their calculated Δσ.

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Figure 9

Figure 9. Renin ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

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Figure 10

Figure 10. Menin A ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

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Figure 11

Figure 11. Menin B ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

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Figure 12

Figure 12. Thrombin A ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

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Figure 13

Figure 13. Thrombin B ligand with all optimized hydrogens colored in proportion to their calculated Δq. Red is more positive and blue more negative. See all named hydrogens in Table S1.

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Figure 14

Figure 14. Thrombin C ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

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Figure 15

Figure 15. Thrombin D ligand with all optimized hydrogens sized in proportion to their calculated Δσ. See all named hydrogens in Table S1.

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Figure 16

Figure 16. Ligand with improved binding free energy found experimentally and final computationally optimized ligand built by choosing perturbations highlighted by our optimization methodology.

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References

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This article references 72 other publications.
1. 1
  Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M. K.; Greenwood, J.; Romero, D. L.; Masse, C.; Knight, J. L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D. L.; Jorgensen, W. L.; Berne, B. J.; Friesner, R. A.; Abel, R. Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field. J. Am. Chem. Soc. 2015, 137, 2695– 2703, DOI: 10.1021/ja512751q
  
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  Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field
  
  Wang, Lingle; Wu, Yujie; Deng, Yuqing; Kim, Byungchan; Pierce, Levi; Krilov, Goran; Lupyan, Dmitry; Robinson, Shaughnessy; Dahlgren, Markus K.; Greenwood, Jeremy; Romero, Donna L.; Masse, Craig; Knight, Jennifer L.; Steinbrecher, Thomas; Beuming, Thijs; Damm, Wolfgang; Harder, Ed; Sherman, Woody; Brewer, Mark; Wester, Ron; Murcko, Mark; Frye, Leah; Farid, Ramy; Lin, Teng; Mobley, David L.; Jorgensen, William L.; Berne, Bruce J.; Friesner, Richard A.; Abel, Robert
  
  Journal of the American Chemical Society (2015), 137 (7), 2695-2703CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
  
  Designing tight-binding ligands is a primary objective of small-mol. drug discovery. Over the past few decades, free-energy calcns. have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread com. application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the tech. challenges traditionally assocd. with running these types of calcns. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chem. perturbations, many of which involve significant changes in ligand chem. structures. In addn., we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compds. synthesized that have been predicted to be potent. Compds. predicted to be potent by this approach have a substantial redn. in false positives relative to compds. synthesized on the basis of other computational or medicinal chem. approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.
  
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2. 2
  Shaw, D. E.; Deneroff, M. M.; Dror, R. O.; Kuskin, J. S.; Larson, R. H.; Salmon, J. K.; Young, C.; Batson, B.; Bowers, K. J.; Chao, J. C.; Eastwood, M. P.; Gagliardo, J.; Grossman, J. P.; Ho, C. R.; Ierardi, D. J.; Kolossváry, I.; Klepeis, J. L.; Layman, T.; McLeavey, C.; Moraes, M. A.; Mueller, R.; Priest, E. C.; Shan, Y.; Spengler, J.; Theobald, M.; Towles, B.; Wang, S. C. Anton, a Special-Purpose Machine for Molecular Dynamics Simulation. Commun. ACM 2008, 51 (7), 91– 97, DOI: 10.1145/1364782.1364802
  
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3. 3
  Shaw, D. E.; Grossman, J.P.; Bank, J. A.; Batson, B.; Butts, J. A.; Chao, J. C.; Deneroff, M. M.; Dror, R. O.; Even, A.; Fenton, C. H.; Forte, A.; Gagliardo, J.; Gill, G.; Greskamp, B.; Ho, C. R.; Ierardi, D. J.; Iserovich, L.; Kuskin, J. S.; Larson, R. H.; Layman, T.; Lee, L.-S.; Lerer, A. K.; Li, C.; Killebrew, D.; Mackenzie, K. M.; Mok, S. Y.-H.; Moraes, M. A.; Mueller, R.; Nociolo, L. J.; Peticolas, J. L.; Quan, T.; Ramot, D.; Salmon, J. K.; Scarpazza, D. P.; Schafer, U. B.; Siddique, N.; Snyder, C. W.; Spengler, J.; Tang, P. T. P.; Theobald, M.; Toma, H.; Towles, B.; Vitale, B.; Wang, S. C.; Young, C. Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer. In SC ‘14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis 2014, 41– 53, DOI: 10.1109/SC.2014.9
  
  There is no corresponding record for this reference.
4. 4
  Lindorff-Larsen, K.; Maragakis, P.; Piana, S.; Shaw, D. E. Picosecond to Millisecond Structural Dynamics in Human Ubiquitin. J. Phys. Chem. B 2016, 120 (33), 8313– 8320, DOI: 10.1021/acs.jpcb.6b02024
  
  4
  Picosecond to Millisecond Structural Dynamics in Human Ubiquitin
  
  Lindorff-Larsen, Kresten; Maragakis, Paul; Piana, Stefano; Shaw, David E.
  
  Journal of Physical Chemistry B (2016), 120 (33), 8313-8320CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
  
  Human ubiquitin has been extensively characterized using a variety of exptl. and computational methods and has become an important model for studying protein dynamics. Nevertheless, it has proven difficult to characterize the microsecond time scale dynamics of this protein with atomistic resoln. Here we use an unbiased computer simulation to describe the structural dynamics of ubiquitin on the picosecond to millisecond time scale. In the simulation, ubiquitin interconverts between a small no. of distinct states on the microsecond to millisecond time scale. We find that the conformations visited by free ubiquitin in soln. are very similar to those found various crystal structures of ubiquitin in complex with other proteins, a finding in line with previous exptl. studies. We also observe weak but statistically significant correlated motions throughout the protein, including long-range concerted movement across the entire β sheet, consistent with recent exptl. observations. We expect that the detailed atomistic description of ubiquitin dynamics provided by this unbiased simulation may be useful in interpreting current and future expts. on this protein.
  
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5. 5
  Pierce, L. C. T.; Salomon-Ferrer, R.; Augusto F de Oliveira, C.; McCammon, J. A.; Walker, R. C. Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics. J. Chem. Theory Comput. 2012, 8 (9), 2997– 3002, DOI: 10.1021/ct300284c
  
  5
  Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics
  
  Pierce, Levi C. T.; Salomon-Ferrer, Romelia; Augusto F. de Oliveira, Cesar; McCammon, J. Andrew; Walker, Ross C.
  
  Journal of Chemical Theory and Computation (2012), 8 (9), 2997-3002CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  In this work, we critically assess the ability of the all-atom enhanced sampling method accelerated mol. dynamics (aMD) to investigate conformational changes in proteins that typically occur on the millisecond time scale. We combine aMD with the inherent power of graphics processor units (GPUs) and apply the implementation to the bovine pancreatic trypsin inhibitor (BPTI). A 500 ns aMD simulation is compared to a previous millisecond unbiased brute force MD simulation carried out on BPTI, showing that the same conformational space is sampled by both approaches. To our knowledge, this represents the first implementation of aMD on GPUs and also the longest aMD simulation of a biomol. run to date. Our implementation is available to the community in the latest release of the Amber software suite (v12), providing routine access to millisecond events sampled from dynamics simulations using off the shelf hardware.
  
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6. 6
  Salomon-Ferrer, R.; Götz, A. W.; Poole, D.; Le Grand, S.; Walker, R. C. Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. J. Chem. Theory Comput. 2013, 9 (9), 3878– 3888, DOI: 10.1021/ct400314y
  
  6
  Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald
  
  Salomon-Ferrer, Romelia; Gotz, Andreas W.; Poole, Duncan; Le Grand, Scott; Walker, Ross C.
  
  Journal of Chemical Theory and Computation (2013), 9 (9), 3878-3888CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  We present an implementation of explicit solvent all atom classical mol. dynamics (MD) within the AMBER program package that runs entirely on CUDA-enabled GPUs. First released publicly in Apr. 2010 as part of version 11 of the AMBER MD package and further improved and optimized over the last two years, this implementation supports the three most widely used statistical mech. ensembles (NVE, NVT, and NPT), uses particle mesh Ewald (PME) for the long-range electrostatics, and runs entirely on CUDA-enabled NVIDIA graphics processing units (GPUs), providing results that are statistically indistinguishable from the traditional CPU version of the software and with performance that exceeds that achievable by the CPU version of AMBER software running on all conventional CPU-based clusters and supercomputers. We briefly discuss three different precision models developed specifically for this work (SPDP, SPFP, and DPDP) and highlight the tech. details of the approach as it extends beyond previously reported work [Goetz et al., J. Chem. Theory Comput.2012, DOI: 10.1021/ct200909j; Le Grand et al., Comp. Phys. Comm.2013, DOI: 10.1016/j.cpc.2012.09.022].We highlight the substantial improvements in performance that are seen over traditional CPU-only machines and provide validation of our implementation and precision models. We also provide evidence supporting our decision to deprecate the previously described fully single precision (SPSP) model from the latest release of the AMBER software package.
  
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7. 7
  Eastman, P.; Swails, J.; Chodera, J. D.; McGibbon, R. T.; Zhao, Y.; Beauchamp, K. A.; Wang, L.-P.; Simmonett, A. C.; Harrigan, M. P.; Stern, C. D.; Wiewiora, R. P.; Brooks, B. R.; Pande, V. S. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLoS Comput. Biol. 2017, 13, e1005659, DOI: 10.1371/journal.pcbi.1005659
  
  7
  OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
  
  Eastman, Peter; Swails, Jason; Chodera, John D.; McGibbon, Robert T.; Zhao, Yutong; Beauchamp, Kyle A.; Wang, Lee-Ping; Simmonett, Andrew C.; Harrigan, Matthew P.; Stern, Chaya D.; Wiewiora, Rafal P.; Brooks, Bernard R.; Pande, Vijay S.
  
  PLoS Computational Biology (2017), 13 (7), e1005659/1-e1005659/17CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
  
  OpenMM is a mol. dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a math. description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.
  
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8. 8
  Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J. Y.; Wang, L.; Lupyan, D.; Dahlgren, M. K.; Knight, J. L.; Kaus, J. W.; Cerutti, D. S.; Krilov, G.; Jorgensen, W. L.; Abel, R.; Friesner, R. A. OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins. J. Chem. Theory Comput. 2016, 12 (1), 281– 296, DOI: 10.1021/acs.jctc.5b00864
  
  8
  OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins
  
  Harder, Edward; Damm, Wolfgang; Maple, Jon; Wu, Chuanjie; Reboul, Mark; Xiang, Jin Yu; Wang, Lingle; Lupyan, Dmitry; Dahlgren, Markus K.; Knight, Jennifer L.; Kaus, Joseph W.; Cerutti, David S.; Krilov, Goran; Jorgensen, William L.; Abel, Robert; Friesner, Richard A.
  
  Journal of Chemical Theory and Computation (2016), 12 (1), 281-296CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  The parametrization and validation of the OPLS3 force field for small mols. and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addn. of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chem. space, OPLS3 employs over an order of magnitude more ref. data and assocd. parameter types relative to other commonly used small mol. force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small mol. conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a no. of proteins. Together, the improvements made to both the small mol. and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.
  
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9. 9
  Roos, K.; Wu, C.; Damm, W.; Reboul, M.; Stevenson, J. M.; Lu, C.; Dahlgren, M. K.; Mondal, S.; Chen, W.; Wang, L.; Abel, R.; Friesner, R. A.; Harder, E. D. OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules. J. Chem. Theory Comput. 2019, 15, 1863, DOI: 10.1021/acs.jctc.8b01026
  
  9
  OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules
  
  Roos, Katarina; Wu, Chuanjie; Damm, Wolfgang; Reboul, Mark; Stevenson, James M.; Lu, Chao; Dahlgren, Markus K.; Mondal, Sayan; Chen, Wei; Wang, Lingle; Abel, Robert; Friesner, Richard A.; Harder, Edward D.
  
  Journal of Chemical Theory and Computation (2019), 15 (3), 1863-1874CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  Building upon the OPLS3 force field we report on an enhanced model, OPLS3e, that further extends its coverage of medicinally relevant chem. space by addressing limitations in chemotype transferability. OPLS3e accomplishes this by incorporating new parameter types that recognize moieties with greater chem. specificity and integrating an on-the-fly parametrization approach to the assignment of partial charges. As a consequence, OPLS3e leads to greater accuracy against performance benchmarks that assess small mol. conformational propensities, solvation, and protein-ligand binding.
  
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10. 10
  Maier, J. A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K. E.; Simmerling, C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015, 11 (8), 3696– 3713, DOI: 10.1021/acs.jctc.5b00255
  
  10
  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB
  
  Maier, James A.; Martinez, Carmenza; Kasavajhala, Koushik; Wickstrom, Lauren; Hauser, Kevin E.; Simmerling, Carlos
  
  Journal of Chemical Theory and Computation (2015), 11 (8), 3696-3713CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  Mol. mechanics is powerful for its speed in atomistic simulations, but an accurate force field is required. The Amber ff99SB force field improved protein secondary structure balance and dynamics from earlier force fields like ff99, but weaknesses in side chain rotamer and backbone secondary structure preferences have been identified. Here, we performed a complete refit of all amino acid side chain dihedral parameters, which had been carried over from ff94. The training set of conformations included multidimensional dihedral scans designed to improve transferability of the parameters. Improvement in all amino acids was obtained as compared to ff99SB. Parameters were also generated for alternate protonation states of ionizable side chains. Av. errors in relative energies of pairs of conformations were under 1.0 kcal/mol as compared to QM, reduced 35% from ff99SB. We also took the opportunity to make empirical adjustments to the protein backbone dihedral parameters as compared to ff99SB. Multiple small adjustments of φ and ψ parameters were tested against NMR scalar coupling data and secondary structure content for short peptides. The best results were obtained from a phys. motivated adjustment to the φ rotational profile that compensates for lack of ff99SB QM training data in the β-ppII transition region. Together, these backbone and side chain modifications (hereafter called ff14SB) not only better reproduced their benchmarks, but also improved secondary structure content in small peptides and reprodn. of NMR χ1 scalar coupling measurements for proteins in soln. We also discuss the Amber ff12SB parameter set, a preliminary version of ff14SB that includes most of its improvements.
  
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11. 11
  Vanommeslaeghe, K.; Hatcher, E.; Acharya, C.; Kundu, S.; Zhong, S.; Shim, J.; Darian, E.; Guvench, O.; Lopes, P.; Vorobyov, I.; Mackerell, A. D. CHARMM General Force Field: A Force Field for Drug-like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Fields. J. Comput. Chem. 2009, 31 (4), 671– 690, DOI: 10.1002/jcc.21367
  
  There is no corresponding record for this reference.
12. 12
  Abel, R.; Wang, L.; Harder, E. D.; Berne, B. J.; Friesner, R. A. Advancing Drug Discovery through Enhanced Free Energy Calculations. Acc. Chem. Res. 2017, 50 (7), 1625– 1632, DOI: 10.1021/acs.accounts.7b00083
  
  12
  Advancing Drug Discovery through Enhanced Free Energy Calculations
  
  Abel, Robert; Wang, Lingle; Harder, Edward D.; Berne, B. J.; Friesner, Richard A.
  
  Accounts of Chemical Research (2017), 50 (7), 1625-1632CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)
  
  A review. A principal goal of drug discovery project is to design mols. that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chem. and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup, have enabled accurate and reliable calcns. of protein-ligands binding free energies, and position free energy calcns. to play a guiding role in small mol. drug discovery. In this accounts, the authors outline the relevant methodol. advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with conventional FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force fields, and the advanced simulation set up that constitute the authors' FEP+ approach, followed by the presentation of extensive comparisons with expt., demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodol. development plans to address those limitations. The authors then report results from a recent drug discovery project, in which several thousand FEP+ calcns. were successfully deployed to simultaneously optimize potency, selectivity, and soly., illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calcns. to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compds., in various dimensions, for a wide range of targets. More effective integration of FEP+ calcns. into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compds. entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approxns. The authors conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theor. methods and models.
  
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13. 13
  Chodera, J. D.; Mobley, D. L.; Shirts, M. R.; Dixon, R. W.; Branson, K.; Pande, V. S. Alchemical Free Energy Methods for Drug Discovery: Progress and Challenges. Curr. Opin. Struct. Biol. 2011, 21 (2), 150– 160, DOI: 10.1016/j.sbi.2011.01.011
  
  13
  Alchemical free energy methods for drug discovery: Progress and challenges
  
  Chodera, John D.; Mobley, David L.; Shirts, Michael R.; Dixon, Richard W.; Branson, Kim; Pande, Vijay S.
  
  Current Opinion in Structural Biology (2011), 21 (2), 150-160CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
  
  A review. Improved rational drug design methods are needed to lower the cost and increase the success rate of drug discovery and development. Alchem. binding free energy calcns., one potential tool for rational design, have progressed rapidly over the past decade, but still fall short of providing robust tools for pharmaceutical engineering. Recent studies, esp. on model receptor systems, have clarified many of the challenges that must be overcome for robust predictions of binding affinity to be useful in rational design. In this review, inspired by a recent joint academic/industry meeting organized by the authors, we discuss these challenges and suggest a no. of promising approaches for overcoming them.
  
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14. 14
  Cournia, Z.; Allen, B.; Sherman, W. Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations. J. Chem. Inf. Model. 2017, 57 (12), 2911– 2937, DOI: 10.1021/acs.jcim.7b00564
  
  14
  Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations
  
  Cournia, Zoe; Allen, Bryce; Sherman, Woody
  
  Journal of Chemical Information and Modeling (2017), 57 (12), 2911-2937CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  
  A review. Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, tech., and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calcns., which rely on physics-based mol. simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, the authors present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. The authors focus specifically on relative binding free energies because the calcns. are less computationally intensive than abs. binding free energy (ABFE) calcns. and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a ref. mol. and new ideas (virtual mols.) can be used to prioritize mols. for synthesis. The authors describe the crit. aspects of running RBFE calcns., from both theor. and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, tech., and practical issues assocd. with running relative binding free energy simulations, with a focus on real-world drug discovery applications. The authors offer guidelines for improving the accuracy of RBFE simulations, esp. for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.
  
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15. 15
  van Vlijmen, H.; Desjarlais, R. L.; Mirzadegan, T. Computational Chemistry at Janssen. J. Comput.-Aided Mol. Des. 2017, 31 (3), 267– 273, DOI: 10.1007/s10822-016-9998-9
  
  15
  Computational chemistry at Janssen
  
  van Vlijmen, Herman; Desjarlais, Renee L.; Mirzadegan, Tara
  
  Journal of Computer-Aided Molecular Design (2017), 31 (3), 267-273CODEN: JCADEQ; ISSN:0920-654X. (Springer)
  
  Computer-aided drug discovery activities at Janssen are carried out by scientists in the Computational Chem. group of the Discovery Sciences organization. This perspective gives an overview of the organizational and operational structure, the science, internal and external collaborations, and the impact of the group on Drug Discovery at Janssen.
  
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16. 16
  Cole, D. J.; Janecek, M.; Stokes, J. E.; Rossmann, M.; Faver, J. C.; McKenzie, G. J.; Venkitaraman, A. R.; Hyvönen, M.; Spring, D. R.; Huggins, D. J.; Jorgensen, W. L. Computationally-Guided Optimization of Small-Molecule Inhibitors of the Aurora A kinase–TPX2 Protein–protein Interaction. Chem. Commun. 2017, 53, 9372– 9375, DOI: 10.1039/C7CC05379G
  
  16
  Computationally-guided optimization of small-molecule inhibitors of the Aurora A kinase-TPX2 protein-protein interaction
  
  Cole, Daniel J.; Janecek, Matej; Stokes, Jamie E.; Rossmann, Maxim; Faver, John C.; McKenzie, Grahame J.; Venkitaraman, Ashok R.; Hyvonen, Marko; Spring, David R.; Huggins, David J.; Jorgensen, William L.
  
  Chemical Communications (Cambridge, United Kingdom) (2017), 53 (67), 9372-9375CODEN: CHCOFS; ISSN:1359-7345. (Royal Society of Chemistry)
  
  Free energy perturbation theory, in combination with enhanced sampling of protein-ligand binding modes, was evaluated in the context of fragment-based drug design, and used to design 2 new small-mol. inhibitors of the human Aurora A kinase-TPX2 protein-protein interaction.
  
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17. 17
  Kuhn, M.; Firth-Clark, S.; Tosco, P.; Mey, A. S. J. S.; Mackey, M. D.; Michel, J. Assessment of Binding Affinity via Alchemical Free Energy Calculations. J. Chem. Inf. Model. 2020, 60, 3120, DOI: 10.1021/acs.jcim.0c00165
  
  17
  Assessment of Binding Affinity via Alchemical Free-Energy Calculations
  
  Kuhn, Maximilian; Firth-Clark, Stuart; Tosco, Paolo; Mey, Antonia S. J. S.; Mackey, Mark; Michel, Julien
  
  Journal of Chemical Information and Modeling (2020), 60 (6), 3120-3130CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  
  Free-energy calcns. have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calcns. and subsequent anal. of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free-energy calcns. is presented. Several sanity checks for assessing the reliability of the calcns. were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace, and OpenMM and uses the AMBER 14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrodinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with exptl. values. For the larger dataset, the av. correlation coeff. Rp was 0.70 ± 0.05 and av. Kendall's τ was 0.53 ± 0.05, which are broadly comparable to or better than previously reported results using other methods.
  
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18. 18
  Riniker, S.; Christ, C. D.; Hansen, H. S.; Hünenberger, P. H.; Oostenbrink, C.; Steiner, D.; van Gunsteren, W. F. Calculation of Relative Free Energies for Ligand-Protein Binding, Solvation, and Conformational Transitions Using the GROMOS Software. J. Phys. Chem. B 2011, 115 (46), 13570– 13577, DOI: 10.1021/jp204303a
  
  18
  Calculation of Relative Free Energies for Ligand-Protein Binding, Solvation, and Conformational Transitions Using the GROMOS Software
  
  Riniker, Sereina; Christ, Clara D.; Hansen, Halvor S.; Hunenberger, Philippe H.; Oostenbrink, Chris; Steiner, Denise; van Gunsteren, Wilfred F.
  
  Journal of Physical Chemistry B (2011), 115 (46), 13570-13577CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
  
  The calcn. of the relative free energies of ligand-protein binding, of solvation for different compds., and of different conformational states of a polypeptide is of considerable interest in the design or selection of potential enzyme inhibitors. Since such processes in aq. soln. generally comprise energetic and entropic contributions from many mol. configurations, adequate sampling of the relevant parts of configurational space is required and can be achieved through mol. dynamics simulations. Various techniques to obtain converged ensemble avs. and their implementation in the GROMOS software for biomol. simulation are discussed, and examples of their application to biomols. in aq. soln. are given.
  
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19. 19
  Bissantz, C.; Kuhn, B.; Stahl, M. A Medicinal Chemist’s Guide to Molecular Interactions. J. Med. Chem. 2010, 53 (14), 5061– 5084, DOI: 10.1021/jm100112j
  
  19
  A Medicinal Chemist's Guide to Molecular Interactions
  
  Bissantz, Caterina; Kuhn, Bernd; Stahl, Martin
  
  Journal of Medicinal Chemistry (2010), 53 (14), 5061-5084CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  
  A review.
  
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20. 20
  Wieder, M.; Garon, A.; Perricone, U.; Boresch, S.; Seidel, T.; Almerico, A. M.; Langer, T. Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations. J. Chem. Inf. Model. 2017, 57 (2), 365– 385, DOI: 10.1021/acs.jcim.6b00674
  
  20
  Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations
  
  Wieder, Marcus; Garon, Arthur; Perricone, Ugo; Boresch, Stefan; Seidel, Thomas; Almerico, Anna Maria; Langer, Thierry
  
  Journal of Chemical Information and Modeling (2017), 57 (2), 365-385CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  
  The authors present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high no. of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and re-scored to generate a single hit-list. The score for a particular mol. is calcd. based on the no. of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. The authors test the performance of CHA for virtual screening using screening databases with active and inactive compds. for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of a protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the exptl. structure in the PDB and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availability of diverse sets of different pharmacophore models is utilized to analyze some addnl. questions of interest in 3D pharmacophore based virtual screening.
  
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21. 21
  Newton, A. S.; Faver, J. C.; Micevic, G.; Muthusamy, V.; Kudalkar, S. N.; Bertoletti, N.; Anderson, K. S.; Bosenberg, M. W.; Jorgensen, W. L. Structure-Guided Identification of DNMT3B Inhibitors. ACS Med. Chem. Lett. 2020, 11 (5), 971– 976, DOI: 10.1021/acsmedchemlett.0c00011
  
  21
  Structure-Guided Identification of DNMT3B Inhibitors
  
  Newton, Ana S.; Faver, John C.; Micevic, Goran; Muthusamy, Viswanathan; Kudalkar, Shalley N.; Bertoletti, Nicole; Anderson, Karen S.; Bosenberg, Marcus W.; Jorgensen, William L.
  
  ACS Medicinal Chemistry Letters (2020), 11 (5), 971-976CODEN: AMCLCT; ISSN:1948-5875. (American Chemical Society)
  
  Methyltransferase 3 beta (DNMT3B) inhibitors that interfere with cancer growth are emerging possibilities for treatment of melanoma. Herein we identify small mol. inhibitors of DNMT3B starting from a homol. model based on a DNMT3A crystal structure. Virtual screening by docking led to purchase of 15 compds., among which 5 were found to inhibit the activity of DNMT3B with IC50 values of 13-72μM in a fluorogenic assay. Eight analogs of 7, 10, and 12 were purchased to provide 2 more active compds. Compd. 11 is particularly notable as it shows good selectivity with no inhibition of DNMT1 and 22μM potency toward DNMT3B. Following addnl. de novo design, exploratory synthesis of 17 analogs of 11 delivered 5 addnl. inhibitors of DNMT3B with the most potent being 33h with an IC50 of 8.0μM. This result was well confirmed in an ultrahigh-performance liq. chromatog. (UHPLC)-based anal. assay, which yielded an IC50 of 4.8μM. Structure-activity data are rationalized based on computed structures for DNMT3B complexes.
  
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22. 22
  Liosi, M.-E.; Krimmer, S. G.; Newton, A. S.; Dawson, T. K.; Puleo, D. E.; Cutrona, K. J.; Suzuki, Y.; Schlessinger, J.; Jorgensen, W. L. Selective Janus Kinase 2 (JAK2) Pseudokinase Ligands with a Diaminotriazole Core. J. Med. Chem. 2020, 63 (10), 5324– 5340, DOI: 10.1021/acs.jmedchem.0c00192
  
  22
  Selective Janus Kinase 2 (JAK2) Pseudokinase Ligands with a Diaminotriazole Core
  
  Liosi Maria-Elena; Krimmer Stefan G; Newton Ana S; Dawson Thomas K; Cutrona Kara J; Jorgensen William L; Puleo David E; Suzuki Yoshihisa; Schlessinger Joseph
  
  Journal of medicinal chemistry (2020), 63 (10), 5324-5340 ISSN:.
  
  Janus kinases (JAKs) are non-receptor tyrosine kinases that are essential components of the JAK-STAT signaling pathway. Associated aberrant signaling is responsible for many forms of cancer and disorders of the immune system. The present focus is on the discovery of molecules that may regulate the activity of JAK2 by selective binding to the JAK2 pseudokinase domain, JH2. Specifically, the Val617Phe mutation in JH2 stimulates the activity of the adjacent kinase domain (JH1) resulting in myeloproliferative disorders. Starting from a non-selective screening hit, we have achieved the goal of discovering molecules that preferentially bind to the ATP binding site in JH2 instead of JH1. We report the design and synthesis of the compounds and binding results for the JH1, JH2, and JH2 V617F domains, as well as five crystal structures for JH2 complexes. Testing with a selective and non-selective JH2 binder on the autophosphorylation of wild-type and V617F JAK2 is also contrasted.
  
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23. 23
  Schindler, C.; Rippmann, F.; Kuhn, D. Relative Binding Affinity Prediction of Farnesoid X Receptor in the D3R Grand Challenge 2 Using FEP+. J. Comput.-Aided Mol. Des. 2018, 32 (1), 265– 272, DOI: 10.1007/s10822-017-0064-z
  
  23
  Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+
  
  Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel
  
  Journal of Computer-Aided Molecular Design (2018), 32 (1), 265-272CODEN: JCADEQ; ISSN:0920-654X. (Springer)
  
  Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the com. free energy calcn. protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two exptl. data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for mol. modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future inhouse drug design projects.
  
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24. 24
  Keränen, H.; Pérez-Benito, L.; Ciordia, M.; Delgado, F.; Steinbrecher, T. B.; Oehlrich, D.; van Vlijmen, H. W. T.; Trabanco, A. A.; Tresadern, G. Acylguanidine Beta Secretase 1 Inhibitors: A Combined Experimental and Free Energy Perturbation Study. J. Chem. Theory Comput. 2017, 13 (3), 1439– 1453, DOI: 10.1021/acs.jctc.6b01141
  
  24
  Acylguanidine Beta Secretase 1 Inhibitors: A Combined Experimental and Free Energy Perturbation Study
  
  Keranen, Henrik; Perez-Benito, Laura; Ciordia, Myriam; Delgado, Francisca; Steinbrecher, Thomas B.; Oehlrich, Daniel; van Vlijmen, Herman W. T.; Trabanco, Andres A.; Tresadern, Gary
  
  Journal of Chemical Theory and Computation (2017), 13 (3), 1439-1453CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  A series of acylguanidine beta secretase 1 (BACE1) inhibitors with modified scaffold and P3 pocket substituent was synthesized and studied with Free Energy Perturbation (FEP) calcns. The resulting mols. showed potencies in enzymic BACE1 inhibition assays up to 1 nM. The correlation between the predicted activity from the FEP calcns. and the exptl. activity was good for the P3 pocket substituents. The av. mean unsigned error (MUE) between prediction and expt. was 0.68±0.17 kcal/mol for the default 5 ns lambda window simulation time improving to 0.35±0.13 kcal/mol for 40 ns. FEP calcns. for the P2' pocket substituents on the same acylguanidine scaffold also showed good agreement with expt. and the results remained stable with repeated simulations and increased simulation time. It proved more difficult to use FEP calcns. to study the scaffold modification from increasing 5 to 6 and 7 membered-rings. Although prediction and expt. were in agreement for short 2 ns simulations, as the simulation time increased the results diverged. This was improved by the use of a newly developed 'Core Hopping FEP+' approach, which also showed improved stability in repeat calcns. The origins of these differences along with the value of repeat and longer simulation times are discussed. This work provides a further example of the use of FEP as a computational tool for mol. design.
  
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25. 25
  Song, L. F.; Lee, T.-S.; Zhu, C.; York, D. M.; Merz, K. M., Jr. Using AMBER18 for Relative Free Energy Calculations. J. Chem. Inf. Model. 2019, 59 (7), 3128– 3135, DOI: 10.1021/acs.jcim.9b00105
  
  25
  Using AMBER18 for Relative Free Energy Calculations
  
  Song, Lin Frank; Lee, Tai-Sung; Zhu, Chun; York, Darrin M.; Merz, Kenneth M.
  
  Journal of Chemical Information and Modeling (2019), 59 (7), 3128-3135CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  
  With renewed interest in free energy methods in contemporary structure-based need to validate against multiple targets and force fields to assess the overall ability of these methods to accurately predict relative binding free energies. We computed relative binding free energies using GPU accelerated Thermodn. Integration (GPU-TI) on a dataset originally assembled by Schrodinger, Inc.. Using their GPU free energy code (FEP+) and the OPLS2.1 force field combined with the REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol. In our study using GPU-TI from AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 perturbations contained in this data set. A more detailed analyses of our results suggested that the obsd. differences between the two studies arise from differences in sampling protocols along with differences in the force fields employed. Future work should address the problem of establishing benchmark quality results with robust statistical error bars obtained through multiple independent runs and enhanced sampling, which is possible with the GPU-accelerated features in AMBER.
  
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26. 26
  Irwin, B. W. J.; Huggins, D. J. Estimating Atomic Contributions to Hydration and Binding Using Free Energy Perturbation. J. Chem. Theory Comput. 2018, 14 (6), 3218– 3227, DOI: 10.1021/acs.jctc.8b00027
  
  26
  Estimating Atomic Contributions to Hydration and Binding Using Free Energy Perturbation
  
  Irwin, Benedict W. J.; Huggins, David J.
  
  Journal of Chemical Theory and Computation (2018), 14 (6), 3218-3227CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  We present a general method called atom-wise free energy perturbation (AFEP), which extends a conventional mol. dynamics free energy perturbation (FEP) simulation to give the contribution to a free energy change from each atom. AFEP is derived from an expansion of the Zwanzig equation used in the exponential averaging method by defining that the system total energy can be partitioned into contributions from each atom. A partitioning method is assumed and used to group terms in the expansion to correspond to individual atoms. AFEP is applied to six example free energy changes to demonstrate the method. Firstly, the hydration free energies of methane, methanol, methylamine, methanethiol, and caffeine in water. AFEP highlights the atoms in the mols. that interact favorably or unfavorably with water. Finally AFEP is applied to the binding free energy of human immunodeficiency virus type 1 protease to lopinavir, and AFEP reveals the contribution of each atom to the binding free energy, indicating candidate areas of the mol. to improve to produce a more strongly binding inhibitor. FEP gives a single value for the free energy change and is already a very useful method. AFEP gives a free energy change for each "part" of the system being simulated, where part can mean individual atoms, chem. groups, amino acids, or larger partitions depending on what the user is trying to measure. This method should have various applications in mol. dynamics studies of phys., chem., or biochem. phenomena, specifically in the field of computational drug discovery.
  
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27. 27
  Lee, L.-P.; Tidor, B. Optimization of Electrostatic Binding Free Energy. J. Chem. Phys. 1997, 106 (21), 8681– 8690, DOI: 10.1063/1.473929
  
  27
  Optimization of electrostatic binding free energy
  
  Lee, Lee-Peng; Tidor, Bruce
  
  Journal of Chemical Physics (1997), 106 (21), 8681-8690CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  
  An analytic result is derived that defines the charge distribution of the tightest-binding ligand given a receptor charge distribution and spherical geometries. Using the framework of continuum electrostatics, the optimal distribution is expressed as a set of multipoles detd. by minimizing the electrostatic free energy of binding. Results for two simple receptor systems are presented to illustrate applications of the theory.
  
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28. 28
  Kangas, E.; Tidor, B. Electrostatic Complementarity at Ligand Binding Sites: Application to Chorismate Mutase. J. Phys. Chem. B 2001, 105 (4), 880– 888, DOI: 10.1021/jp003449n
  
  28
  Electrostatic Complementarity at Ligand Binding Sites: Application to Chorismate Mutase
  
  Kangas, Erik; Tidor, Bruce
  
  Journal of Physical Chemistry B (2001), 105 (4), 880-888CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)
  
  Charge optimization methods facilitate examn. and, potentially, improvement of electrostatic interactions between binding partners. Here charge optimization was applied to the chorismate mutase from Bacillus subtilis binding an endo-oxabicyclic transition-state analog. Electrostatically optimized templates based on calcns. using the x-ray crystal structure were used to define regions of the transition-state analog whose electrostatic properties are sub-optimal for binding. Variants of the analog that could exhibit improved electrostatic affinity for the enzyme were considered that more closely mimicked the optimal charge distributions. Results indicate that the transition-state analog is remarkably complementary to the enzyme active site in terms of electrostatics throughout much of the binding site. Particularly good electrostatic complementarity is exhibited for most of the groups on the analog that make hydrogen bonds with the enzyme. While some small potential opportunities for improvement exist throughout the ligand, one significant under-compensated interaction stands out. The C10 carboxylate pays substantial desolvation penalty upon binding but does not recover strong hydrogen-bonded interactions with the enzyme in the available crystal structure. Calcns. suggest that replacement with a virtually isosteric nitro group may improve the electrostatic contribution to the binding free energy by 2-3 kcal/mol. The principal mechanism is a decrease in the desolvation penalty of the ligand with a smaller loss in ligand-enzyme interactions. This work shows that charge optimization techniques are capable of identifying chem. reasonable substitutions to known ligands that produce substantial improvements in computed binding affinity through electrostatic enhancement. Comparison of the results across twelve independently refined active sites confirms that the approach is not overly sensitive to subtle structural variation.
  
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29. 29
  Radhakrishnan, M. L. Theor. Chem. Acc. 2012, 131, 1252, DOI: 10.1007/s00214-012-1252-5
  
  There is no corresponding record for this reference.
30. 30
  Wade, A. D.; Huggins, D. J. Optimization of Protein–Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method. J. Chem. Theory Comput. 2019, 15 (11), 6504– 6512, DOI: 10.1021/acs.jctc.9b00976
  
  29
  Optimization of Protein-Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method
  
  Wade, Alexander D.; Huggins, David J.
  
  Journal of Chemical Theory and Computation (2019), 15 (11), 6504-6512CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  The authors present an explicit solvent alchem. free-energy method for optimizing the partial charges of a ligand to maximize the binding affinity with a receptor. This methodol. can be applied to known ligand-protein complexes to det. an optimized set of ligand partial at. changes. Three protein-ligand complexes have been optimized in this work: FXa, P38 and the androgen receptor. The sets of optimized charges can be used to identify design principles for chem. changes to the ligands which improve the binding affinity for all three systems. In this work, beneficial chem. mutations are generated from these principles and the resulting mols. tested using free-energy perturbation calcns. The authors show that three quarters of the chem. changes are predicted to improve the binding affinity, with an av. improvement for the beneficial mutations of approx. 1 kcal/mol. In the cases where exptl. data is available, the agreement between prediction and expt. is also good. The results demonstrate that charge optimization in explicit solvent is a useful tool for predicting beneficial chem. changes such as pyridinations, fluorinations, and oxygen to sulfur mutations.
  
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31. 31
  Wan, S.; Bhati, A. P.; Zasada, S. J.; Wall, I.; Green, D.; Bamborough, P.; Coveney, P. V. Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study. J. Chem. Theory Comput. 2017, 13 (2), 784– 795, DOI: 10.1021/acs.jctc.6b00794
  
  30
  Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study
  
  Wan, Shunzhou; Bhati, Agastya P.; Zasada, Stefan J.; Wall, Ian; Green, Darren; Bamborough, Paul; Coveney, Peter V.
  
  Journal of Chemical Theory and Computation (2017), 13 (2), 784-795CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  Binding free energies of bromodomain inhibitors are calcd. with recently formulated approaches, namely ESMACS (enhanced sampling of mol. dynamics with approxn. of continuum solvent) and TIES (thermodn. integration with enhanced sampling). A set of compds. is provided by GlaxoSmithKline, which represents a range of chem. functionality and binding affinities. The predicted binding free energies exhibit a good Spearman correlation of 0.78 with the exptl. data from the 3-trajectory ESMACS, and an excellent correlation of 0.92 from the TIES approach where applicable. Given access to suitable high end computing resources and a high degree of automation, the authors can compute individual binding affinities in a few hours with precisions no greater than 0.2 kcal/mol for TIES, and no larger than 0.34 kcal/mol and 1.71 kcal/mol for the 1- and 3-trajectory ESMACS approaches.
  
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32. 32
  Lawrenz, M.; Baron, R.; McCammon, J. A. Independent-Trajectories Thermodynamic-Integration Free-Energy Changes for Biomolecular Systems: Determinants of H5N1 Avian Influenza Virus Neuraminidase Inhibition by Peramivir. J. Chem. Theory Comput. 2009, 5 (4), 1106– 1116, DOI: 10.1021/ct800559d
  
  31
  Independent-Trajectories Thermodynamic-Integration Free-Energy Changes for Biomolecular Systems: Determinants of H5N1 Avian Influenza Virus Neuraminidase Inhibition by Peramivir
  
  Lawrenz, Morgan; Baron, Riccardo; McCammon, J. Andrew
  
  Journal of Chemical Theory and Computation (2009), 5 (4), 1106-1116CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  Free-energy changes are essential physicochem. quantities for understanding most biochem. processes. Yet, the application of accurate thermodn.-integration (TI) computation to biol. and macromol. systems is limited by finite-sampling artifacts. In this paper, the authors employ independent-trajectories thermodn.-integration (IT-TI) computation to est. improved free-energy changes and their uncertainties for (bio)mol. systems. IT-TI aids sampling statistics of the thermodn. macrostates for flexible assocg. partners by ensemble averaging of multiple, independent simulation trajectories. The authors study peramivir (PVR) inhibition of the H5N1 avian influenza virus neuraminidase flexible receptor (N1). Binding site loops 150 and 119 are highly mobile, as revealed by N1-PVR 20-ns mol. dynamics. Due to such heterogeneous sampling, std. TI binding free-energy ests. span a rather large free-energy range, from a 19% underestimation to a 29% overestimation of the exptl. ref. value (-62.2±1.8 kJ mol-1). Remarkably, the authors' IT-TI binding free-energy est. (-61.1±5.4 kJ mol-1) agrees with a 2% relative difference. In addn., IT-TI runs provide a statistics-based free-energy uncertainty for the process of interest. Using ∼800 ns of overall sampling, the authors investigate N1-PVR binding determinants by IT-TI alchem. modifications of PVR moieties. These results emphasize the dominant electrostatic contribution, particularly through the N1 E277-PVR guanidinium interaction. Future drug development may be also guided by properly tuning ligand flexibility and hydrophobicity. IT-TI will allow estn. of relative free energies for systems of increasing size, with improved reliability by employing large-scale distributed computing.
  
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33. 33
  Vilseck, J. Z.; Armacost, K. A.; Hayes, R. L.; Goh, G. B.; Brooks, C. L., 3rd Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics. J. Phys. Chem. Lett. 2018, 9 (12), 3328– 3332, DOI: 10.1021/acs.jpclett.8b01284
  
  32
  Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics
  
  Vilseck, Jonah Z.; Armacost, Kira A.; Hayes, Ryan L.; Goh, Garrett B.; Brooks, Charles L.
  
  Journal of Physical Chemistry Letters (2018), 9 (12), 3328-3332CODEN: JPCLCD; ISSN:1948-7185. (American Chemical Society)
  
  In this study, we demonstrate the extensive scalability of the biasing potential replica exchange multisite λ dynamics (BP-REX MSλD) free energy method by calcg. binding affinities for 512 inhibitors to HIV Reverse Transcriptase (HIV-RT). This is the largest exploration of chem. space using free energy methods known to date, requires only a few simulations, and identifies 55 new inhibitor designs against HIV-RT predicted to be at least as potent as a tight binding ref. compd. (i.e., as potent as 56 nM). We highlight that BP-REX MSλD requires an order of magnitude less computational resources than conventional free energy methods while maintaining a similar level of precision, overcomes the inherent poor scalability of conventional free energy methods, and enables the exploration of combinatorially large chem. spaces in the context of in silico drug discovery.
  
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34. 34
  Bennett, C. H. Efficient Estimation of Free Energy Differences from Monte Carlo Data. J. Comput. Phys. 1976, 22 (2), 245– 268, DOI: 10.1016/0021-9991(76)90078-4
  
  There is no corresponding record for this reference.
35. 35
  Shirts, M. R.; Chodera, J. D. Statistically Optimal Analysis of Samples from Multiple Equilibrium States. J. Chem. Phys. 2008, 129 (12), 124105, DOI: 10.1063/1.2978177
  
  34
  Statistically optimal analysis of samples from multiple equilibrium states
  
  Shirts, Michael R.; Chodera, John D.
  
  Journal of Chemical Physics (2008), 129 (12), 124105/1-124105/10CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  
  We present a new estimator for computing free energy differences and thermodn. expectations as well as their uncertainties from samples obtained from multiple equil. states via either simulation or expt. The estimator, which we call the multistate Bennett acceptance ratio estimator (MBAR) because it reduces to the Bennett acceptance ratio estimator (BAR) when only two states are considered, has significant advantages over multiple histogram reweighting methods for combining data from multiple states. It does not require the sampled energy range to be discretized to produce histograms, eliminating bias due to energy binning and significantly reducing the time complexity of computing a soln. to the estg. equations in many cases. Addnl., an est. of the statistical uncertainty is provided for all estd. quantities. In the large sample limit, MBAR is unbiased and has the lowest variance of any known estimator for making use of equil. data collected from multiple states. We illustrate this method by producing a highly precise est. of the potential of mean force for a DNA hairpin system, combining data from multiple optical tweezer measurements under const. force bias. (c) 2008 American Institute of Physics.
  
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36. 36
  Riniker, S.; Christ, C. D.; Hansen, N.; Mark, A. E.; Nair, P. C.; van Gunsteren, W. F. Comparison of Enveloping Distribution Sampling and Thermodynamic Integration to Calculate Binding Free Energies of Phenylethanolamine N-Methyltransferase Inhibitors. J. Chem. Phys. 2011, 135 (2), 024105, DOI: 10.1063/1.3604534
  
  35
  Comparison of enveloping distribution sampling and thermodynamic integration to calculate binding free energies of phenylethanolamine N-methyltransferase inhibitors
  
  Riniker, Sereina; Christ, Clara D.; Hansen, Niels; Mark, Alan E.; Nair, Pramod C.; van Gunsteren, Wilfred F.
  
  Journal of Chemical Physics (2011), 135 (2), 024105/1-024105/13CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  
  The relative binding free energy between two ligands to a specific protein can be obtained using various computational methods. The more accurate and also computationally more demanding techniques are the so-called free energy methods which use conformational sampling from mol. dynamics or Monte Carlo simulations to generate thermodn. avs. Two such widely applied methods are the thermodn. integration (TI) and the recently introduced enveloping distribution sampling (EDS) methods. In both cases relative binding free energies are obtained through the alchem. perturbations of one ligand into another in water and inside the binding pocket of the protein. TI requires many sep. simulations and the specification of a pathway along which the system is perturbed from one ligand to another. Using the EDS approach, only a single automatically derived ref. state enveloping both end states needs to be sampled. In addn., the choice of an optimal pathway in TI calcns. is not trivial and a poor choice may lead to poor convergence along the pathway. Given this, EDS is expected to be a valuable and computationally efficient alternative to TI. In this study, the performances of these two methods are compared using the binding of ten tetrahydroisoquinoline derivs. to phenylethanolamine N-transferase as an example. The ligands involve a diverse set of functional groups leading to a wide range of free energy differences. In addn., two different schemes to det. automatically the EDS ref. state parameters and two different topol. approaches are compared. (c) 2011 American Institute of Physics.
  
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37. 37
  Liu, H.; Mark, A. E.; van Gunsteren, W. F. Estimating the Relative Free Energy of Different Molecular States with Respect to a Single Reference State. J. Phys. Chem. 1996, 100 (22), 9485– 9494, DOI: 10.1021/jp9605212
  
  36
  Estimating the Relative Free Energy of Different Molecular States with Respect to a Single Reference State
  
  Liu, Haiyan; Mark, Alan E.; van Gunsteren, Wilfred F.
  
  Journal of Physical Chemistry (1996), 100 (22), 9485-9494CODEN: JPCHAX; ISSN:0022-3654. (American Chemical Society)
  
  The authors have investigated the feasibility of predicting free energy differences between a manifold of mol. states from a single simulation or ensemble representing one ref. state. Two formulas that are based on the so-called λ- coupling parameter approach are analyzed and compared: (i) expansion of the free energy F(λ) into a Taylor series around a ref. state (λ = 0), and (ii) the so-called free energy perturbation formula. The results obtained by these extrapolation methods are compared to exact (target) values calcd. by thermodn. integration for mutations in two mol. systems: a model dipolar diat. mol. in water, and a series of para-substituted phenols in water. For moderate charge redistribution (≈0.5 e), both extrapolation methods reproduce the exact free energy differences. For free energy changes due to a change of atom type or size, the Taylor expansion method fails completely, while the perturbation formula yields moderately accurate predictions. Both extrapolation methods fail when a mutation involves the creation or deletion of atoms, due to the poor sampling in the ref. state simulation of the configurations that are important in the end states of interest. To overcome this sampling difficulty, a procedure based on the perturbation formula and on biasing the sampling in the ref. state is proposed, in which soft-core interaction sites are incorporated into the Hamiltonian of the ref. state at positions where atoms are to be created or deleted. For mutations going from p-methylphenol to the other five differently para-substituted phenols, the differences in free energy are correctly predicted using extrapolation based on a single simulation of a biased, non-phys. ref. state. Since a large no. of mutations can be investigated using a recorded trajectory of a single simulation, the proposed method is potentially viable in practical applications such as drug design.
  
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38. 38
  Mordasini, T. Z.; McCammon, J. A. Calculations of Relative Hydration Free Energies: A Comparative Study Using Thermodynamic Integration and an Extrapolation Method Based on a Single Reference State. J. Phys. Chem. B 2000, 104, 360– 367, DOI: 10.1021/jp993102o
  
  37
  Calculations of Relative Hydration Free Energies: A Comparative Study Using Thermodynamic Integration and an Extrapolation Method Based on a Single Reference State
  
  Mordasini, Tiziana Z.; McCammon, J. Andrew
  
  Journal of Physical Chemistry B (2000), 104 (2), 360-367CODEN: JPCBFK; ISSN:1089-5647. (American Chemical Society)
  
  The relative hydration free energies of a series of org. mols. were calcd. from mol. dynamics (MD) simulations using an extrapolation method in combination with soft-core potential scaling. This technique consists in first generating one single long trajectory using an unphys. ref. state and in using afterward this trajectory for the estn. of the free energy difference between several mols. First, we investigated the accuracy of the method for the deletion of small functional groups. A trajectory from an MD simulation of pyrogallol (1,2,3-trihydroxybenzene) was used to calc. by extrapolation the free energy changes for the mutations of pyrogallol, catechol, and phenol to benzene in water and in vacuo. The results were compared to those obtained by thermodn. integration, to exptl. data, and to values calcd. using a semiempirical method. In a second step, increasingly larger mutations were studied in order to investigate the limitations of the method. The influence of various simulation parameters (choice of the unphys. ref. state, simulation length, soft-core scaling parameter α) on the final free energy values was examd. Benzene derivs. with hydroxyalkyl and/or bulky functional groups of increasing sizes were mutated into benzene. The results for simulations both in water and in vacuo were compared to the free energy results obtained by thermodn. integration and to the exptl. values of similar mols. The results showed that for small mutations (deletion of functional groups with up to three atoms) the extrapolation method is reliable. However, the free energies calcd. for the deletion of larger functional groups showed different accuracy levels depending on the chosen simulation parameters. For the largest mutations the thermodn. integration method also showed convergence problems. This study therefore demonstrated the usefulness of the extrapolation method for mols. of similar size, but showed the difficulties of obtaining reliable results for mols. that substantially differ from each other.
  
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39. 39
  Raman, E. P.; Vanommeslaeghe, K.; MacKerell, A. D. Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations. J. Chem. Theory Comput. 2012, 8, 3513– 3525, DOI: 10.1021/ct300088r
  
  38
  Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations
  
  Raman, E. Prabhu; Vanommeslaeghe, Kenno; MacKerell, Alexander D., Jr.
  
  Journal of Chemical Theory and Computation (2012), 8 (10), 3513-3525CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  The in silico Site Identification by Ligand Competitive Satn. (SILCS) approach identifies the binding sites of representative chem. entities on the entire protein surface, information that can be applied for computational fragment-based drug design. The authors report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single-step free energy perturbation (SSFEP) calcns. to identify site-specific favorable chem. modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the exptl. trends in relative hydration free energies of benzene analogs and for two data sets of exptl. relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins are first analyzed to identify favorable benzene binding sites, following which an ensemble of benzene-protein conformations for that site was obtained. The SSFEP protocol applied to that ensemble results in good reprodn. of exptl. free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and anal. of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.
  
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40. 40
  Stroet, M.; Koziara, K. B.; Malde, A. K.; Mark, A. E. Optimization of Empirical Force Fields by Parameter Space Mapping: A Single-Step Perturbation Approach. J. Chem. Theory Comput. 2017, 13, 6201– 6212, DOI: 10.1021/acs.jctc.7b00800
  
  39
  Optimization of Empirical Force Fields by Parameter Space Mapping: A Single-Step Perturbation Approach
  
  Stroet, Martin; Koziara, Katarzyna B.; Malde, Alpeshkumar K.; Mark, Alan E.
  
  Journal of Chemical Theory and Computation (2017), 13 (12), 6201-6212CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  A general method for parametrizing at. interaction functions is presented. The method is based on an anal. of surfaces corresponding to the difference between calcd. and target data as a function of alternative combinations of parameters (parameter space mapping). The consideration of surfaces in parameter space as opposed to local values or gradients leads to a better understanding of the relationships between the parameters being optimized and a given set of target data. This in turn enables for a range of target data from multiple mols. to be combined in a robust manner and for the optimal region of parameter space to be trivially identified. The effectiveness of the approach is illustrated by using the method to refine the chlorine 6-12 Lennard-Jones parameters against exptl. solvation free enthalpies in water and hexane as well as the d. and heat of vaporization of the liq. at atm. pressure for a set of 10 arom.-chloro compds. simultaneously. Single-step perturbation is used to efficiently calc. solvation free enthalpies for a wide range of parameter combinations. The capacity of this approach to parametrize accurate and transferrable force fields is discussed.
  
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41. 41
  Oostenbrink, C.; Van Gunsteren, W. F. Single-Step Perturbations to Calculate Free Energy Differences from Unphysical Reference States: Limits on Size, Flexibility, and Character. J. Comput. Chem. 2003, 24, 1730– 1739, DOI: 10.1002/jcc.10304
  
  40
  Single-step perturbations to calculate free energy differences from unphysical reference states: Limits on size, flexibility, and character
  
  Oostenbrink, Chris; Van Gunsteren, Wilfred F.
  
  Journal of Computational Chemistry (2003), 24 (14), 1730-1739CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  
  Relative free energies for a series of not too different compds. can be estd. accurately from a single simulation of an unphys. ref. state that encompasses the characteristic mol. features of the compds. Previously, this method has been applied to the calcn. of free energies of solvation and of ligand binding for small mols. In the present study we investigate the limits to the accuracy of the method by applying it to a realistic model of the binding of a set of rather large ligands to the protein factor Xa, a key protein in current efforts to design anticoagulation drugs. The evaluation of the binding free energies and conformations of nine derivs. of a biphenylamidino inhibitor leads to insights regarding the effect of the size, flexibility, and character of the unphys. part of the ligand in the ref. state on the accuracy of the predicted binding free energies.
  
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42. 42
  Oostenbrink, C.; van Gunsteren, W. F. Free Energies of Ligand Binding for Structurally Diverse Compounds. Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (19), 6750– 6754, DOI: 10.1073/pnas.0407404102
  
  41
  Free energies of ligand binding for structurally diverse compounds
  
  Oostenbrink, Chris; van Gunsteren, Wilfred F.
  
  Proceedings of the National Academy of Sciences of the United States of America (2005), 102 (19), 6750-6754CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  
  The one-step perturbation approach is an efficient means to calc. many relative free energies from a common ref. compd. Combining lessons learned in previous studies, an application of the method is presented that allows for the calcn. of relative binding free energies for structurally rather diverse compds. from only a few simulations. Based on the well known statistical-mech. perturbation formula, the results do not require any empirical parameters, or training sets, only limited knowledge of the binding characteristics of the ligands suffices to design appropriate ref. compds. Depending on the choice of ref. compd., relative free energies of binding rigid ligands to the ligand-binding domain of the estrogen receptor can be obtained that show good agreement with the exptl. values. The approach presented here can easily be applied to many rigid ligands, and it should be relatively easy to extend the method to account for ligand flexibility. The free-energy calcns. can be straightforwardly parallelized, allowing for an efficient means to understand and predict relative binding free energies.
  
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43. 43
  Wade, A. D.; Rizzi, A.; Wang, Y.; Huggins, D. J. Computational Fluorine Scanning Using Free-Energy Perturbation. J. Chem. Inf. Model. 2019, 59, 2776, DOI: 10.1021/acs.jcim.9b00228
  
  42
  Computational Fluorine Scanning Using Free-Energy Perturbation
  
  Wade, Alexander D.; Rizzi, Andrea; Wang, Yuanqing; Huggins, David J.
  
  Journal of Chemical Information and Modeling (2019), 59 (6), 2776-2784CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  
  We present perturbative fluorine scanning, a computational fluorine scanning approach using free-energy perturbation. This method can be applied to mol. dynamics simulations of a single compd. and make predictions for the best binders out of numerous fluorinated analogs. We tested the method on nine test systems: renin, DPP4, menin, P38, factor Xa, CDK2, AKT, JAK2, and androgen receptor. The predictions were in excellent agreement with more rigorous alchem. free-energy calcns. and in good agreement with exptl. data for most of the test systems. However, the agreement with expt. was very poor in some of the test systems, and this highlights the need for improved force fields in addn. to accurate treatment of tautomeric and protonation states. The method is of particular interest due to the wide use of fluorine in medicinal chem. to improve binding affinity and ADME properties. The promising results on this test case suggest that perturbative fluorine scanning will be a useful addn. to the available arsenal of free-energy methods.
  
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44. 44
  Raman, E. P.; Lakkaraju, S. K.; Denny, R. A.; MacKerell, A. D. Estimation of Relative Free Energies of Binding Using Pre-Computed Ensembles Based on the Single-Step Free Energy Perturbation and the Site-Identification by Ligand Competitive Saturation Approaches. J. Comput. Chem. 2017, 38, 1238– 1251, DOI: 10.1002/jcc.24522
  
  43
  Estimation of relative free energies of binding using pre-computed ensembles based on the single-step free energy perturbation and the site-identification by Ligand competitive saturation approaches
  
  Raman, E. Prabhu; Lakkaraju, Sirish Kaushik; Denny, Rajiah Aldrin; MacKerell, Alexander D. Jr
  
  Journal of Computational Chemistry (2017), 38 (15), 1238-1251CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  
  Accurate and rapid estn. of relative binding affinities of ligand-protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design. In the present study relative free energies of binding are estd. for one or two nonhydrogen atom changes in compds. targeting the proteins ACK1 and p38 MAP kinase using three methods. The methods include std. FEP, single-step free energy perturbation (SSFEP) and the site-identification by ligand competitive satn. (SILCS) ligand grid free energy (LGFE) approach. Results show the SSFEP and SILCS LGFE methods to be competitive with or better than the FEP results for the studied systems, with SILCS LGFE giving the best agreement with exptl. results. This is supported by addnl. comparisons with published FEP data on p38 MAP kinase inhibitors. While both the SSFEP and SILCS LGFE approaches require a significant upfront computational investment, they offer a 1000-fold computational savings over FEP for calcg. the relative affinities of ligand modifications once those precomputations are complete. An illustrative example of the potential application of these methods in the context of screening large nos. of transformations is presented. Thus, the SSFEP and SILCS LGFE approaches represent viable alternatives for actively driving ligand design during drug discovery and development. © 2016 Wiley Periodicals, Inc.
  
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45. 45
  Oostenbrink, C. Free Energy Calculations from One-Step Perturbations. Methods Mol. Biol. 2012, 819, 487– 499, DOI: 10.1007/978-1-61779-465-0_28
  
  44
  Free energy calculations from one-step perturbations
  
  Oostenbrink, Chris
  
  Methods in Molecular Biology (New York, NY, United States) (2012), 819 (Computational Drug Discovery and Design), 487-499CODEN: MMBIED; ISSN:1064-3745. (Springer)
  
  The one-step perturbation approach offers an efficient means to est. free energy differences. It may be applied to est. solvation free energies, conformational preferences or relative free energies of binding of series of compds. to a common receptor. Applicability of the method depends on the possibility to define a proper ref. state which may in itself be an unphys. mol. Here, we describe practical considerations and explicit guidelines to define a proper ref. state, and to efficiently calc. relative free energies. The strengths and limitations of the method are highlighted and special considerations are noted. The method may be applied using many different simulation programs. Here, analyses are exemplified at the hand of the GROMOS simulation package.
  
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46. 46
  Baron, R. Computational Drug Discovery and Design; Baron, R., Ed.; Springer: New York, 2012.
  
  There is no corresponding record for this reference.
47. 47
  Graf, M. M. H.; Zhixiong, L.; Bren, U.; Haltrich, D.; van Gunsteren, W. F.; Oostenbrink, C. Pyranose Dehydrogenase Ligand Promiscuity: A Generalized Approach to Simulate Monosaccharide Solvation, Binding, and Product Formation. PLoS Comput. Biol. 2014, 10 (12), e1003995 DOI: 10.1371/journal.pcbi.1003995
  
  46
  Pyranose dehydrogenase ligand promiscuity: a generalized approach to simulate monosaccharide solvation, binding, and product formation
  
  Graf, Michael M. H.; Lin, Zhixiong; Bren, Urban; Haltrich, Dietmar; van Gunsteren, Wilfred F.; Oostenbrink, Chris
  
  PLoS Computational Biology (2014), 10 (12), e1003995/1-e1003995/13, 13 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
  
  The flavoenzyme pyranose dehydrogenase (PDH) from the litter decompg. fungus Agaricus meleagris oxidizes many different carbohydrates occurring during lignin degrdn. This promiscuous substrate specificity makes PDH a promising catalyst for bioelectrochem. applications. A generalized approach to simulate all 32 possible aldohexopyranoses in the course of one or a few mol. dynamics (MD) simulations is reported. Free energy calcns. according to the one-step perturbation (OSP) method revealed the solvation free energies (ΔGsolv) of all 32 aldohexopyranoses in water, which have not yet been reported in the literature. The free energy difference between β- and α-anomers (ΔGβ-α) of all D-stereoisomers in water were compared to exptl. values with a good agreement. Moreover, the free-energy differences (ΔG) of the 32 stereoisomers bound to PDH in two different poses were calcd. from MD simulations. The relative binding free energies (ΔΔGbind) were calcd. and, where available, compared to exptl. values, approximated from Km values. The agreement was very good for one of the poses, in which the sugars are positioned in the active site for oxidn. at C1 or C2. Distance anal. between hydrogens of the monosaccharide and the reactive N5-atom of the FAD revealed that oxidn. is possible at HC1 or HC2 for pose A, and at HC3 or HC4 for pose B. Exptl. detected oxidn. products could be rationalized for the majority of monosaccharides by combining ΔΔGbind and a reweighted distance anal. Furthermore, several oxidn. products were predicted for sugars that have not yet been tested exptl., directing further analyses. This study rationalizes the relationship between binding free energies and substrate promiscuity in PDH, providing novel insights for its applicability in bioelectrochem. The results suggest that a similar approach could be applied to study promiscuity of other enzymes.
  
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48. 48
  Perthold, J. W.; Petrov, D.; Oostenbrink, C. Towards Automated Free Energy Calculation with Accelerated Enveloping Distribution Sampling (A-EDS). J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00456 .
  
  There is no corresponding record for this reference.
49. 49
  Perthold, J. W.; Oostenbrink, C. Accelerated Enveloping Distribution Sampling: Enabling Sampling of Multiple End States While Preserving Local Energy Minima. J. Phys. Chem. B 2018, 122 (19), 5030– 5037, DOI: 10.1021/acs.jpcb.8b02725
  
  48
  Accelerated Enveloping Distribution Sampling: Enabling Sampling of Multiple End States while Preserving Local Energy Minima
  
  Perthold, Jan Walther; Oostenbrink, Chris
  
  Journal of Physical Chemistry B (2018), 122 (19), 5030-5037CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
  
  Enveloping distribution sampling (EDS) is an efficient approach to calc. multiple free-energy differences from a single mol. dynamics (MD) simulation. However, the construction of an appropriate ref.-state Hamiltonian that samples all states efficiently is not straightforward. We propose a novel approach for the construction of the EDS ref.-state Hamiltonian, related to a previously described procedure to smoothen energy landscapes. In contrast to previously suggested EDS approaches, our ref.-state Hamiltonian preserves local energy min. of the combined end-states. Moreover, we propose an intuitive, robust and efficient parameter optimization scheme to tune EDS Hamiltonian parameters. We demonstrate the proposed method with established and novel test systems and conclude that our approach allows for the automated calcn. of multiple free-energy differences from a single simulation. Accelerated EDS promises to be a robust and user-friendly method to compute free-energy differences based on solid statistical mechanics.
  
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50. 50
  Aldeghi, M.; Heifetz, A.; Bodkin, M. J.; Knapp, S.; Biggin, P. C. Accurate Calculation of the Absolute Free Energy of Binding for Drug Molecules. Chem. Sci. 2016, 7 (1), 207– 218, DOI: 10.1039/C5SC02678D
  
  49
  Accurate calculation of the absolute free energy of binding for drug molecules
  
  Aldeghi, Matteo; Heifetz, Alexander; Bodkin, Michael J.; Knapp, Stefan; Biggin, Philip C.
  
  Chemical Science (2016), 7 (1), 207-218CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)
  
  Accurate prediction of binding affinities has been a central goal of computational chem. for decades, yet remains elusive. Despite good progress, the required accuracy for use in a drug-discovery context has not been consistently achieved for drug-like mols. Here, we perform abs. free energy calcns. based on a thermodn. cycle for a set of diverse inhibitors binding to bromodomain-contg. protein 4 (BRD4) and demonstrate that a mean abs. error of 0.6 kcal mol-1 can be achieved. We also show a similar level of accuracy (1.0 kcal mol-1) can be achieved in pseudo prospective approach. Bromodomains are epigenetic mark readers that recognize acetylation motifs and regulate gene transcription, and are currently being investigated as therapeutic targets for cancer and inflammation. The unprecedented accuracy offers the exciting prospect that the binding free energy of drug-like compds. can be predicted for pharmacol. relevant targets.
  
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51. 51
  Woods, C. J.; Essex, J. W.; King, M. A. The Development of Replica-Exchange-Based Free-Energy Methods. J. Phys. Chem. B 2003, 107 (49), 13703– 13710, DOI: 10.1021/jp0356620
  
  50
  The Development of Replica-Exchange-Based Free-Energy Methods
  
  Woods, Christopher J.; Essex, Jonathan W.; King, Michael A.
  
  Journal of Physical Chemistry B (2003), 107 (49), 13703-13710CODEN: JPCBFK; ISSN:1520-6106. (American Chemical Society)
  
  The calcn. of relative free energies that involve large reorganizations of the environment is one of the great challenges of condensed-phase simulation. Such calcns. are of particular importance in protein-ligand free-energy calcns. To meet this challenge, we have developed new free-energy techniques that combine the advantages of the replica-exchange method with free-energy perturbation (FEP) and finite-difference thermodn. integration (FDTI). These new techniques are tested and compared with FEP, FDTI, and the adaptive umbrella weighted histogram anal. method (AdUmWHAM) on the challenging calcn. of the relative hydration free energy of methane and water. This calcn. involves a large solvent configurational change. Through the use of replica-exchange moves along the λ-coordinate, the configurations sampled along λ are allowed to mix, which leads to dramatic improvements in solvent configurational sampling, an efficient redn. of random sampling error, and a redn. of general simulation error. This is achieved at effectively no extra computational cost, relative to std. FEP or FDTI.
  
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52. 52
  Chodera, J. D.; Shirts, M. R. Replica Exchange and Expanded Ensemble Simulations as Gibbs Sampling: Simple Improvements for Enhanced Mixing. J. Chem. Phys. 2011, 135 (19), 194110, DOI: 10.1063/1.3660669
  
  51
  Replica exchange and expanded ensemble simulations as Gibbs sampling: Simple improvements for enhanced mixing
  
  Chodera, John D.; Shirts, Michael R.
  
  Journal of Chemical Physics (2011), 135 (19), 194110/1-194110/15CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  
  The widespread popularity of replica exchange and expanded ensemble algorithms for simulating complex mol. systems in chem. and biophysics has generated much interest in discovering new ways to enhance the phase space mixing of these protocols in order to improve sampling of uncorrelated configurations. Here, we demonstrate how both of these classes of algorithms can be considered as special cases of Gibbs sampling within a Markov chain Monte Carlo framework. Gibbs sampling is a well-studied scheme in the field of statistical inference in which different random variables are alternately updated from conditional distributions. While the update of the conformational degrees of freedom by Metropolis Monte Carlo or mol. dynamics unavoidably generates correlated samples, we show how judicious updating of the thermodn. state indexes-corresponding to thermodn. parameters such as temp. or alchem. coupling variables-can substantially increase mixing while still sampling from the desired distributions. We show how state update methods in common use can lead to suboptimal mixing, and present some simple, inexpensive alternatives that can increase mixing of the overall Markov chain, reducing simulation times necessary to obtain ests. of the desired precision. These improved schemes are demonstrated for several common applications, including an alchem. expanded ensemble simulation, parallel tempering, and multidimensional replica exchange umbrella sampling. (c) 2011 American Institute of Physics.
  
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53. 53
  Macdonald, H. B.; Cave-Ayland, C.; Essex, J. Predicting Water Networks and Ligand Binding Free Energies in Proteins Using Grand Canonical Monte Carlo. In Abstracts of Papers of the American Chemical Society; American Chemical Society: Washington, DC, 2018; Vol. 255.
  
  There is no corresponding record for this reference.
54. 54
  Ross, G. A.; Bruce Macdonald, H. E.; Cave-Ayland, C.; Cabedo Martinez, A. I.; Essex, J. W. Replica-Exchange and Standard State Binding Free Energies with Grand Canonical Monte Carlo. J. Chem. Theory Comput. 2017, 13 (12), 6373– 6381, DOI: 10.1021/acs.jctc.7b00738
  
  53
  Replica-Exchange and Standard State Binding Free Energies with Grand Canonical Monte Carlo
  
  Ross, Gregory A.; Bruce Macdonald, Hannah E.; Cave-Ayland, Christopher; Cabedo Martinez, Ana I.; Essex, Jonathan W.
  
  Journal of Chemical Theory and Computation (2017), 13 (12), 6373-6381CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  The ability of grand canonical Monte Carlo (GCMC) to create and annihilate mols. in a given region greatly aids the identification of water sites and water binding free energies in protein cavities. However, acceptance rates without the application of biased moves can be low, resulting in large variations in the obsd. water occupancies. Here, we show that replica exchange of the chem. potential significantly reduces the variance of the GCMC data. This improvement comes at a negligible increase in computational expense when simulations comprise of runs at different chem. potentials. Replica exchange GCMC is also found to substantially increase of the precision of std. state water binding free energies as calcd. with grand canonical integration, which has allowed us to address a missing std. state correction.
  
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55. 55
  Ben-Shalom, I. Y.; Lin, C.; Kurtzman, T.; Walker, R.; Gilson, M. K. Equilibration of Buried Water Molecules to Enhance Protein-Ligand Binding Free Energy Calculations. Biophys. J. 2020, 118 (3), 144a, DOI: 10.1016/j.bpj.2019.11.908
  
  There is no corresponding record for this reference.
56. 56
  Jagger, B. R.; Kochanek, S. E.; Haldar, S.; Amaro, R. E.; Mulholland, A. J. Multiscale Simulation Approaches to Modeling Drug–protein Binding. Curr. Opin. Struct. Biol. 2020, 61, 213– 221, DOI: 10.1016/j.sbi.2020.01.014
  
  55
  Multiscale simulation approaches to modeling drug-protein binding
  
  Jagger, Benjamin R.; Kochanek, Sarah E.; Haldar, Susanta; Amaro, Rommie E.; Mulholland, Adrian J.
  
  Current Opinion in Structural Biology (2020), 61 (), 213-221CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
  
  A review. Simulations can provide detailed insight into the mol. processes involved in drug action, such as protein-ligand binding, and can therefore be a valuable tool for drug design and development. Processes with a large range of length and timescales may be involved, and understanding these different scales typically requires different types of simulation methodol. Ideally, simulations should be able to connect across scales, to analyze and predict how changes at one scale can influence another. Multiscale simulation methods, which combine different levels of treatment, are an emerging frontier with great potential in this area. Here we review multiscale frameworks of various types, and selected applications to biomol. systems with a focus on drug-ligand binding.
  
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57. 57
  Haldar, S.; Comitani, F.; Saladino, G.; Woods, C.; van der Kamp, M. W.; Mulholland, A. J.; Gervasio, F. L. A Multiscale Simulation Approach to Modeling Drug–Protein Binding Kinetics. J. Chem. Theory Comput. 2018, 14 (11), 6093– 6101, DOI: 10.1021/acs.jctc.8b00687
  
  56
  A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics
  
  Haldar, Susanta; Comitani, Federico; Saladino, Giorgio; Woods, Christopher; van der Kamp, Marc W.; Mulholland, Adrian J.; Gervasio, Francesco Luigi
  
  Journal of Chemical Theory and Computation (2018), 14 (11), 6093-6101CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  Drug-target binding kinetics has recently emerged as a sometimes crit. determinant of in vivo efficacy and toxicity. Its rational optimization to improve potency or reduce side effects of drugs is, however, extremely difficult. Mol. simulations can play a crucial role in identifying features and properties of small ligands and their protein targets affecting the binding kinetics, but significant challenges include the long timescales involved in (un)binding events and the limited accuracy of empirical atomistic force-fields (lacking e.g. changes in electronic polarization). In an effort to overcome these hurdles, we propose a method that combines state-of-the-art enhanced sampling simulations and quantum mechanics/mol. mechanics (QM/MM) calcns. at the BLYP/VDZ level to compute assocn. free energy profiles and characterize the binding kinetics in terms of structure and dynamics of the transition state ensemble. We test our combined approach on the binding of the anticancer drug imatinib to Src kinase, a well-characterized target for cancer therapy with a complex binding mechanism involving significant conformational changes. The results indicate significant changes in polarization along the binding pathways, which affect the predicted binding kinetics. This is likely to be of widespread importance in ligand-target binding.
  
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58. 58
  Hamann, L. G.; Manfredi, M. C.; Sun, C.; Krystek, S. R.; Huang, Y.; Bi, Y.; Augeri, D. J.; Wang, T.; Zou, Y.; Betebenner, D. A.; Fura, A.; Seethala, R.; Golla, R.; Kuhns, J. E.; Lupisella, J. A.; Darienzo, C. J.; Custer, L. L.; Price, J. L.; Johnson, J. M.; Biller, S. A.; Zahler, R.; Ostrowski, J. Tandem Optimization of Target Activity and Elimination of Mutagenic Potential in a Potent Series of N-Aryl Bicyclic Hydantoin-Based Selective Androgen Receptor Modulators. Bioorg. Med. Chem. Lett. 2007, 17 (7), 1860– 1864, DOI: 10.1016/j.bmcl.2007.01.076
  
  57
  Tandem optimization of target activity and elimination of mutagenic potential in a potent series of N-aryl bicyclic hydantoin-based selective androgen receptor modulators
  
  Hamann, Lawrence G.; Manfredi, Mark C.; Sun, Chongqing; Krystek, Stanley R.; Huang, Yanting; Bi, Yingzhi; Augeri, David J.; Wang, Tammy; Zou, Yan; Betebenner, David A.; Fura, Aberra; Seethala, Ramakrishna; Golla, Rajasree; Kuhns, Joyce E.; Lupisella, John A.; Darienzo, Celia J.; Custer, Laura L.; Price, Jennifer L.; Johnson, James M.; Biller, Scott A.; Zahler, Robert; Ostrowski, Jacek
  
  Bioorganic & Medicinal Chemistry Letters (2007), 17 (7), 1860-1864CODEN: BMCLE8; ISSN:0960-894X. (Elsevier Ltd.)
  
  Pharmacokinetic studies in cynomolgus monkeys with a novel prototype selective androgen receptor modulator revealed trace amts. of an aniline fragment released through hydrolytic metab. This aniline fragment was detd. to be mutagenic in an Ames assay. Subsequent concurrent optimization for target activity and avoidance of mutagenicity led to the identification of a pharmacol. superior clin. candidate without mutagenic potential.
  
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59. 59
  Ostrowski, J.; Kuhns, J. E.; Lupisella, J. A.; Manfredi, M. C.; Beehler, B. C.; Krystek, S. R., Jr; Bi, Y.; Sun, C.; Seethala, R.; Golla, R.; Sleph, P. G.; Fura, A.; An, Y.; Kish, K. F.; Sack, J. S.; Mookhtiar, K. A.; Grover, G. J.; Hamann, L. G. Pharmacological and X-Ray Structural Characterization of a Novel Selective Androgen Receptor Modulator: Potent Hyperanabolic Stimulation of Skeletal Muscle with Hypostimulation of Prostate in Rats. Endocrinology 2007, 148 (1), 4– 12, DOI: 10.1210/en.2006-0843
  
  58
  Pharmacological and X-ray structural characterization of a novel selective androgen receptor modulator: potent hyperanabolic stimulation of skeletal muscle with hypostimulation of prostate in rats
  
  Ostrowski, Jacek; Kuhns, Joyce E.; Lupisella, John A.; Manfredi, Mark C.; Beehler, Blake C.; Krystek, Stanley R., Jr.; Bi, Yingzhi; Sun, Chongqing; Seethala, Ramakrishna; Golla, Rajasree; Sleph, Paul G.; Fura, Aberra; An, Yongmi; Kish, Kevin F.; Sack, John S.; Mookhtiar, Kasim A.; Grover, Gary J.; Hamann, Lawrence G.
  
  Endocrinology (2007), 148 (1), 4-12CODEN: ENDOAO; ISSN:0013-7227. (Endocrine Society)
  
  A novel, highly potent, orally active, nonsteroidal tissue selective androgen receptor (AR) modulator (BMS-564929) has been identified, and this compd. has been advanced to clin. trials for the treatment of age-related functional decline. BMS-564929 is a subnanomolar AR agonist in vitro, is highly selective for the AR vs. other steroid hormone receptors, and exhibits no significant interactions with SHBG or aromatase. Dose response studies in castrated male rats show that BMS-564929 is substantially more potent than testosterone (T) in stimulating the growth of the levator ani muscle, and unlike T, highly selective for muscle vs. prostate. Key differences in the binding interactions of BMS-564929 with the AR relative to the native hormones were revealed through x-ray crystallog., including several unique contacts located in specific helixes of the ligand binding domain important for coregulatory protein recruitment. Results from addnl. pharmacol. studies effectively exclude alternative mechanistic contributions to the obsd. tissue selectivity of this unique, orally active androgen. Because concerns regarding the potential hyperstimulatory effects on prostate and an inconvenient route of administration are major drawbacks that limit the clin. use of T, the potent oral activity and tissue selectivity exhibited by BMS-564929 are expected to yield a clin. profile that provides the demonstrated beneficial effects of T in muscle and other tissues with a more favorable safety window.
  
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60. 60
  Matter, H.; Scheiper, B.; Steinhagen, H.; Böcskei, Z.; Fleury, V.; McCort, G. Structure-Based Design and Optimization of Potent Renin Inhibitors on 5- or 7-Azaindole-Scaffolds. Bioorg. Med. Chem. Lett. 2011, 21 (18), 5487– 5492, DOI: 10.1016/j.bmcl.2011.06.112
  
  59
  Structure-based design and optimization of potent renin inhibitors on 5- or 7-azaindole-scaffolds
  
  Matter, Hans; Scheiper, Bodo; Steinhagen, Henning; Boecskei, Zsolt; Fleury, Valerie; McCort, Gary
  
  Bioorganic & Medicinal Chemistry Letters (2011), 21 (18), 5487-5492CODEN: BMCLE8; ISSN:0960-894X. (Elsevier B.V.)
  
  The selective inhibition of the aspartyl protease renin is of high interest to control hypertension and assocd. cardiovascular risk factors. Following on preceding contributions, we report herein on the optimization of two series of azaindoles to arrive at potent and non-chiral renin inhibitors. The previously discovered azaindole scaffold was further explored by structure-based drug design in combination with parallel synthesis. This results in the identification of novel 5- or 7-azaindole derivs. with remarkable potency for renin inhibition. The best compds. on both series show IC50 values between 3 and 8 nM.
  
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61. 61
  He, S.; Senter, T. J.; Pollock, J.; Han, C.; Upadhyay, S. K.; Purohit, T.; Gogliotti, R. D.; Lindsley, C. W.; Cierpicki, T.; Stauffer, S. R.; Grembecka, J. High-Affinity Small-Molecule Inhibitors of the Menin-Mixed Lineage Leukemia (MLL) Interaction Closely Mimic a Natural Protein–Protein Interaction. J. Med. Chem. 2014, 57 (4), 1543– 1556, DOI: 10.1021/jm401868d
  
  60
  High-Affinity Small-Molecule Inhibitors of the Menin-Mixed Lineage Leukemia (MLL) Interaction Closely Mimic a Natural Protein-Protein Interaction
  
  He, Shihan; Senter, Timothy J.; Pollock, Jonathan; Han, Changho; Upadhyay, Sunil Kumar; Purohit, Trupta; Gogliotti, Rocco D.; Lindsley, Craig W.; Cierpicki, Tomasz; Stauffer, Shaun R.; Grembecka, Jolanta
  
  Journal of Medicinal Chemistry (2014), 57 (4), 1543-1556CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  
  The protein-protein interaction (PPI) between menin and mixed lineage leukemia (MLL) plays a crit. role in acute leukemias, and inhibition of this interaction represents a new potential therapeutic strategy for MLL leukemias. We report development of a novel class of small-mol. inhibitors of the menin-MLL interaction, the hydroxy- and aminomethylpiperidine compds., which originated from HTS of ∼288000 small mols. We detd. menin-inhibitor co-crystal structures and found that these compds. closely mimic all key interactions of MLL with menin. Extensive crystallog. studies combined with structure-based design were applied for optimization of these compds., resulting in MIV-6R, which inhibits the menin-MLL interaction with IC50 = 56 nM. Treatment with MIV-6 demonstrated strong and selective effects in MLL leukemia cells, validating specific mechanism of action. Our studies provide novel and attractive scaffold as a new potential therapeutic approach for MLL leukemias and demonstrate an example of PPI amenable to inhibition by small mols.
  
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62. 62
  Baum, B.; Muley, L.; Smolinski, M.; Heine, A.; Hangauer, D.; Klebe, G. Non-Additivity of Functional Group Contributions in Protein–Ligand Binding: A Comprehensive Study by Crystallography and Isothermal Titration Calorimetry. J. Mol. Biol. 2010, 397 (4), 1042– 1054, DOI: 10.1016/j.jmb.2010.02.007
  
  61
  Non-additivity of Functional Group Contributions in Protein-Ligand Binding: A Comprehensive Study by Crystallography and Isothermal Titration Calorimetry
  
  Baum, Bernhard; Muley, Laveena; Smolinski, Michael; Heine, Andreas; Hangauer, David; Klebe, Gerhard
  
  Journal of Molecular Biology (2010), 397 (4), 1042-1054CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
  
  Additivity of functional group contributions to protein-ligand binding is a very popular concept in medicinal chem. as the basis of rational design and optimized lead structures. Most of the currently applied scoring functions for docking build on such additivity models. Even though the limitation of this concept is well known, case studies examg. in detail why additivity fails at the mol. level are still very scarce. The present study shows, by use of crystal structure anal. and isothermal titrn. calorimetry for a congeneric series of thrombin inhibitors, that extensive cooperative effects between hydrophobic contacts and hydrogen bond formation are intimately coupled via dynamic properties of the formed complexes. The formation of optimal lipophilic contacts with the surface of the thrombin S3 pocket and the full desolvation of this pocket can conflict with the formation of an optimal hydrogen bond between ligand and protein. The mutual contributions of the competing interactions depend on the size of the ligand hydrophobic substituent and influence the residual mobility of ligand portions at the binding site. Anal. of the individual crystal structures and factorizing the free energy into enthalpy and entropy demonstrates that binding affinity of the ligands results from a mixt. of enthalpic contributions from hydrogen bonding and hydrophobic contacts, and entropic considerations involving an increasing loss of residual mobility of the bound ligands. This complex picture of mutually competing and partially compensating enthalpic and entropic effects dets. the non-additivity of free energy contributions to ligand binding at the mol. level.
  
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63. 63
  Ghosh, A. K.; Takayama, J.; Aubin, Y.; Ratia, K.; Chaudhuri, R.; Baez, Y.; Sleeman, K.; Coughlin, M.; Nichols, D. B.; Mulhearn, D. C.; Prabhakar, B. S.; Baker, S. C.; Johnson, M. E.; Mesecar, A. D. Structure-Based Design, Synthesis, and Biological Evaluation of a Series of Novel and Reversible Inhibitors for the Severe Acute Respiratory Syndrome- Coronavirus Papain-Like Protease. J. Med. Chem. 2009, 52 (16), 5228– 5240, DOI: 10.1021/jm900611t
  
  62
  Structure-Based Design, Synthesis, and Biological Evaluation of a Series of Novel and Reversible Inhibitors for the Severe Acute Respiratory Syndrome-Coronavirus Papain-Like Protease
  
  Ghosh, Arun K.; Takayama, Jun; Aubin, Yoann; Ratia, Kiira; Chaudhuri, Rima; Baez, Yahira; Sleeman, Katrina; Coughlin, Melissa; Nichols, Daniel B.; Mulhearn, Debbie C.; Prabhakar, Bellur S.; Baker, Susan C.; Johnson, Michael E.; Mesecar, Andrew D.
  
  Journal of Medicinal Chemistry (2009), 52 (16), 5228-5240CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  
  We describe here the design, synthesis, mol. modeling, and biol. evaluation of a series of small mol., nonpeptide inhibitors of SARS-CoV PLpro. Our initial lead compd. was identified via high-throughput screening of a diverse chem. library. We subsequently carried out structure-activity relationship studies and optimized the lead structure to potent inhibitors that have shown antiviral activity against SARS-CoV infected Vero E6 cells. Upon the basis of the X-ray crystal structure of inhibitor (III)-bound to SARS-CoV PLpro, a drug design template was created. Our structure-based modification led to the design of a more potent inhibitor, (I) (enzyme IC50 = 0.46 μM; antiviral EC50 = 6 μM). Interestingly, its methylamine deriv., (II), displayed good enzyme inhibitory potency (IC50 = 1.3 μM) and the most potent SARS antiviral activity (EC50 = 5.2 μM) in the series. We have carried out computational docking studies and generated a predictive 3D-QSAR model for SARS-CoV PLpro inhibitors.
  
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64. 64
  Ratia, K.; Pegan, S.; Takayama, J.; Sleeman, K.; Coughlin, M.; Baliji, S.; Chaudhuri, R.; Fu, W.; Prabhakar, B. S.; Johnson, M. E.; Baker, S. C.; Ghosh, A. K.; Mesecar, A. D. A Noncovalent Class of Papain-like Protease/deubiquitinase Inhibitors Blocks SARS Virus Replication. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (42), 16119– 16124, DOI: 10.1073/pnas.0805240105
  
  63
  A noncovalent class of papain-like protease/deubiquitinase inhibitors blocks SARS virus replication
  
  Ratia, Kiira; Pegan, Scott; Takayama, Jun; Sleeman, Katrina; Coughlin, Melissa; Baliji, Surendranath; Chaudhuri, Rima; Fu, Wentao; Prabhakar, Bellur S.; Johnson, Michael E.; Baker, Susan C.; Ghosh, Arun K.; Mesecar, Andrew D.
  
  Proceedings of the National Academy of Sciences of the United States of America (2008), 105 (42), 16119-16124CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  
  We report the discovery and optimization of a potent inhibitor against the papain-like protease (PLpro) from the coronavirus that causes severe acute respiratory syndrome (SARS-CoV). This unique protease is not only responsible for processing the viral polyprotein into its functional units but is also capable of cleaving ubiquitin and ISG15 conjugates and plays a significant role in helping SARS-CoV evade the human immune system. We screened a structurally diverse library of 50,080 compds. for inhibitors of PLpro and discovered a noncovalent lead inhibitor with an IC50 value of 20 μM, which was improved to 600 nM via synthetic optimization. The resulting compd., GRL0617, inhibited SARS-CoV viral replication in Vero E6 cells with an EC50 of 15 μM and had no assocd. cytotoxicity. The X-ray structure of PLpro in complex with GRL0617 indicates that the compd. has a unique mode of inhibition whereby it binds within the 54-53 subsites of the enzyme and induces a loop closure that shuts down catalysis at the active site. These findings provide proof-of-principle that PLpro is a viable target for development of antivirals directed against SARS-CoV, and that potent noncovalent cysteine protease inhibitors can be developed with specificity directed toward pathogenic deubiquitinating enzymes without inhibiting host DUBs.
  
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65. 65
  Maestro, version 9.1; Schrödinger, LLC: New York, 2010.
  
  There is no corresponding record for this reference.
66. 66
  Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Antechamber: An Accessory Software Package for Molecular Mechanical Calculations. J. Am. Chem. Soc. 2001, 222, U403
  
  There is no corresponding record for this reference.
67. 67
  Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and Testing of a General Amber Force Field. J. Comput. Chem. 2004, 25 (9), 1157– 1174, DOI: 10.1002/jcc.20035
  
  67
  Development and testing of a general Amber force field
  
  Wang, Junmei; Wolf, Romain M.; Caldwell, James W.; Kollman, Peter A.; Case, David A.
  
  Journal of Computational Chemistry (2004), 25 (9), 1157-1174CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  
  We describe here a general Amber force field (GAFF) for org. mols. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most org. and pharmaceutical mols. that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited no. of atom types, but incorporates both empirical and heuristic models to est. force consts. and partial at. charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallog. structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 Å, which is comparable to that of the Tripos 5.2 force field (0.25 Å) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 Å, resp.). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermol. energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 Å and 1.2 kcal/mol, resp. These data are comparable to results from Parm99/RESP (0.16 Å and 1.18 kcal/mol, resp.), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to expt.) is about 0.5 kcal/mol. GAFF can be applied to wide range of mols. in an automatic fashion, making it suitable for rational drug design and database searching.
  
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68. 68
  Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: II. Parameterization and Validation. J. Comput. Chem. 2002, 23 (16), 1623– 1641, DOI: 10.1002/jcc.10128
  
  68
  Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. parameterization and validation
  
  Jakalian, Araz; Jack, David B.; Bayly, Christopher I.
  
  Journal of Computational Chemistry (2002), 23 (16), 1623-1641CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  
  We present the first global parameterization and validation of a novel charge model, called AM1-BCC, which quickly and efficiently generates high-quality at. charges for computer simulations of org. mols. in polar media. The goal of the charge model is to produce at. charges that emulate the HF/6-31G* electrostatic potential (ESP) of a mol. Underlying electronic structure features, including formal charge and electron delocalization, are first captured by AM1 population charges; simple additive bond charge corrections (BCCs) are then applied to these AM1 at. charges to produce the AM1-BCC charges. The parameterization of BCCs was carried out by fitting to the HF/6-31G* ESP of a training set of >2700 mols. Most org. functional groups and their combinations were sampled, as well as an extensive variety of cyclic and fused bicyclic heteroaryl systems. The resulting BCC parameters allow the AM1-BCC charging scheme to handle virtually all types of org. compds. listed in The Merck Index and the NCI Database. Validation of the model was done through comparisons of hydrogen-bonded dimer energies and relative free energies of solvation using AM1-BCC charges in conjunction with the 1994 Cornell et al. forcefield for AMBER. Homo-dimer and hetero-dimer hydrogen-bond energies of a diverse set of org. mols. were reproduced to within 0.95 kcal/mol RMS deviation from the ab initio values, and for DNA dimers the energies were within 0.9 kcal/mol RMS deviation from ab initio values. The calcd. relative free energies of solvation for a diverse set of monofunctional isosteres were reproduced to within 0.69 kcal/mol of expt. In all these validation tests, AMBER with the AM1-BCC charge model maintained a correlation coeff. above 0.96. Thus, the parameters presented here for use with the AM1-BCC method present a fast, accurate, and robust alternative to HF/6-31G* ESP-fit charges for general use with the AMBER force field in computer simulations involving org. small mols.
  
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69. 69
  Naden, L. N.; Shirts, M. R. Linear Basis Function Approach to Efficient Alchemical Free Energy Calculations. 2. Inserting and Deleting Particles with Coulombic Interactions. J. Chem. Theory Comput. 2015, 11 (6), 2536– 2549, DOI: 10.1021/ct501047e
  
  69
  Linear Basis Function Approach to Efficient Alchemical Free Energy Calculations. 2. Inserting and Deleting Particles with Coulombic Interactions
  
  Naden, Levi N.; Shirts, Michael R.
  
  Journal of Chemical Theory and Computation (2015), 11 (6), 2536-2549CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
  
  We extend our previous linear basis function approach for alchem. free energy calcns. to the insertion and deletion of charged particles in dense fluids. We compute a near optimal statistical path to introduce Coulombic interactions into various mols. in soln. and find that this near optimal path is only marginally more efficient than simple linear coupling of electrostatics in all cases where a repulsive core is already present. We also explore the order in which nonbonded forces are coupled to the environment in alchem. transformations. We test two sets of Lennard-Jones basis functions, a Weeks-Chandler-Andersen (WCA) and a 12-6 decompn. of the repulsive and attractive forces turned on in sequence along with changes in charge, to det. a statistically optimized order in which forces should be coupled. The WCA decompn. has lower statistical uncertainty as coupling the attractive r-6 basis function contributes non-negligible statistical error. In all cases, the charge should be coupled only after the repulsive core is fully coupled, and the WCA attractive portion can be coupled at any stage without significantly changing the efficiency. The statistical uncertainty of two of the basis function approaches with charged particles is nearly identical to the soft core approach for decoupling electrostatics, though the correlation times for sampling are often longer for a soft core electrostatics approach than the basis function approach. The basis function approach for introducing or removing mols. or functional groups thus represents a useful alternative to the soft core approach with a no. of clear computational advantages.
  
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70. 70
  Pechlaner, M.; Reif, M. M.; Oostenbrink, C. Reparametrisation of United-Atom Amine Solvation in the GROMOS Force Field. Mol. Phys. 2017, 115 (9–12), 1144– 1154, DOI: 10.1080/00268976.2016.1255797
  
  70
  Reparametrisation of united-atom amine solvation in the GROMOS force field
  
  Pechlaner, Maria; Reif, Maria M.; Oostenbrink, Chris
  
  Molecular Physics (2017), 115 (9-12), 1144-1154CODEN: MOPHAM; ISSN:0026-8976. (Taylor & Francis Ltd.)
  
  A review. New parameters for ammonia, mono-, di- and trimethylated amine compatible with GROMOS force fields are presented. A directed search in parameter space by steepest descent minimisation led to an optimized charge set with good agreement to exptl. data on abs. and relative free energies of solvation in water, chloroform and carbon tetrachloride. The final model is characterised in terms of structural and dynamic properties.
  
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71. 71
  Diem, M.; Oostenbrink, C. Hamiltonian Reweighing To Refine Protein Backbone Dihedral Angle Parameters in the GROMOS Force Field. J. Chem. Inf. Model. 2020, 60 (1), 279– 288, DOI: 10.1021/acs.jcim.9b01034
  
  71
  Hamiltonian Reweighing To Refine Protein Backbone Dihedral Angle Parameters in the GROMOS Force Field
  
  Diem, Matthias; Oostenbrink, Chris
  
  Journal of Chemical Information and Modeling (2020), 60 (1), 279-288CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  
  Mol. dynamics simulations of proteins depend critically on the underlying force field, which may be parameterized against exptl. data or high-quality quantum calcns. Here, the authors develop search algorithms based on Monte Carlo and steepest descent calcns. to optimize the backbone dihedral angle parameters from a single ref. simulation. The authors apply these tools to improve the agreement between simulations of single, capped amino acids and exptl. detd. J values and secondary structure propensities of these mols. The parameters are further refined based on simulations of a set of seven proteins and finally validated in simulations on a large set of 52 protein structures. Improvements in the dihedral angle distributions are obsd., and structural propensities of the proteins are reproduced very well. Overall, the GROMOS 54A8_bb parameter set forms an improvement to previous parameter sets, both for small mols. and for protein simulations.
  
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72. 72
  Chodera, J.; Rizzi, A.; Naden, L.; Beauchamp, K.; Grinaway, P.; Fass, J.; Rustenburg, B.; Ross, G. A.; Simmonett, A.; Swenson, D. W. H. Choderalab/openmmtools: 0.14. 0-Exact Treatment of Alchemical PME Electrostatics, Water Cluster Test System, Optimizations. https://zenodo.org/record/1161149#.X0gc6HlKjIU.
  
  There is no corresponding record for this reference.
- PDB: 2NW4
- PDB: 3E9S
- PDB: 3OOT
- PDB: 4OG6
- PDB: 2ZNK
Supporting Information
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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.0c00610.
- Details of protein preparation specific to each system; all ligand structures in full 3D with all optimized hydrogens labeled explicitly (Table S1); calculations for validation of free energies obtained from final optimized sigmas (Figures S1–S3 and Table S2); all convergence graphs not presented in the main text for ΔΔG_scan calculations (Figures S4–S11); diagram for steric clashes in androgen receptor (Figure S12); all optimized sigmas for the menin B ligand (Table S3); all optimized charges for the thrombin B ligand (Table S4); Figure S13 depicting the thermodynamic cycle used for relative binding free energy calculations in this work (PDF)
- ci0c00610_si_001.pdf (1.72 MB)
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Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method

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Abstract

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Introduction

Method

System Setup

Molecular Dynamics

Workflow

Figure 1

Optimization

MBAR Objective

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SSP Gradient

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Optimization Validation

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FEP Scans

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Summary of Methods

Results

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Figure 16

Conclusion

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Abstract

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