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Principles of Allosteric Interactions in Cell Signaling

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† Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
‡ Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
§ Department of Biophysics, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, United States
∥ Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), and Center for Scientific Computation, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275, United States
[email protected]
Cite this: J. Am. Chem. Soc. 2014, 136, 51, 17692–17701
Publication Date (Web):December 4, 2014
https://doi.org/10.1021/ja510028c

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Abstract

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Linking cell signaling events to the fundamental physicochemical basis of the conformational behavior of single molecules and ultimately to cellular function is a key challenge facing the life sciences. Here we outline the emerging principles of allosteric interactions in cell signaling, with emphasis on the following points. (1) Allosteric efficacy is not a function of the chemical composition of the allosteric pocket but reflects the extent of the population shift between the inactive and active states. That is, the allosteric effect is determined by the extent of preferred binding, not by the overall binding affinity. (2) Coupling between the allosteric and active sites does not decide the allosteric effect; however, it does define the propagation pathways, the allosteric binding sites, and key on-path residues. (3) Atoms of allosteric effectors can act as “driver” or “anchor” and create attractive “pulling” or repulsive “pushing” interactions. Deciphering, quantifying, and integrating the multiple co-occurring events present daunting challenges to our scientific community.

Introduction

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Specific protein function is determined by the extent to which the protein populates a distinct active state. (1) Allostery, an inherent physical property of proteins, is a key factor governing the relative populations among accessible conformational states. (2) Allostery can be defined as the change in the distribution of the conformational ensemble through some perturbation, such as ligand binding or covalent post-translational modification (PTM) (3) or mutations taking place through directed evolution, (4, 5) to alter the population of the active state. (6-20) Nature has co-evolved ligand–host protein interactions, optimizing them to tune the populations of the active (or inactive) states for function, either by stabilizing the active conformation and destabilizing the inactive conformations, or vice versa. (21, 22) Here, we provide an overview of the fundamental underpinnings of allostery. We aim to delineate key challenging questions, such as, Can we predict a priori—and quantify—changes incurred by allosteric mutations or specific binding events to increase/decrease the population of the active or inactive state to up- or down-regulate the protein? When considered in large systems, on a cellular scale, with multiple events that form and/or disrupt non-covalent and covalent interactions, and with numerous mutations and different classes of molecules which are involved, these key questions stymie applications of the allosteric concept toward unveiling physiological signaling, deregulation in disease, and allosteric drug discovery. The extent of the stabilization, or population shift, toward the active (or inactive) state determines the efficacy of the mutation, PTM, or binding event. Below, we reason that our mechanistic understanding of the hallmarks of allostery already permits undertaking such challenges. We further formulate some guidelines toward such aims.
More and more data attest to the significance of allostery in cell life under physiological conditions (23-28) and in disease. (21, 29, 30) Allostery takes place across single molecules and large multimolecular assemblies, across the membrane, cytoplasm, and organelles, and across DNA and protein–DNA interactions. (14, 18, 31-44) Diseases often occur through allosteric mutations that shift the protein population from an OFF to a functional ON state and keep it there, with the ramifications propagating through cellular pathways, affecting the cell state. (21) Allostery is best described by a series of free energy landscape diagrams that map the conformational spread and the corresponding energy levels (Figure 1). Molecules exist as ensembles with certain conformational distributions under distinct conditions; allostery works by altering the distributions following some conformational trigger. They can be reflected by significant conformational changes at the active site and/or its dynamics between ordered and disordered states. (45) Allostery is the means through which the physicochemical basis of the conformational behavior of single molecules governs cellular behavior. (23) While not the only factor, it plays a decisive role in cellular response to changes in the environment, internal and external. Currently the mechanism of “how allostery works” on the molecular level is fairly well understood. (1) Linking fundamental mechanistic underpinnings, available data for proteins and pathways, and the dynamic spatial cellular organization (46) should allow us to address major bottlenecks in allostery-related research, such as quantification of the effects of mutations and identification of target proteins and sites, while accounting for regulatory feedback loops. (27, 47) It should also allow us to design allosteric modifiers for specific outcome, agonist or antagonist. (22) The number of studies where observations are interpreted through allostery escalates rapidly, indicating that the community increasingly grasps its significance to the living cell.

Figure 1

Figure 1. Extent of population shift, rather than binding affinity, determines allosteric efficacy. Here we use the distinct mutations of E3 ubiquitin ligase as an example. The population of the protein conformations is dominated by either the active form or the inactive form of the E3:E2∼Ub complex. The relative population of the inactive state (I state) and active state (A* state) depends on the relative energy of the two states. The free energy landscape is defined as ΔG = G(A*) – G(I). For the wild type (green curve), the dominant population is the active conformational state, allosterically facilitating the ubiquitin transfer process. Both destabilizing inactive-state mutants (blue curve) and stabilizing active-state mutants (red curve) can enhance the allosteric activity, here illustrated from the standpoint of population shift. The extent of enhancement is expressed by the free energy change due to the mutations, ΔΔGWT→M. The destabilizing inactive-state mutants may destabilize the inactive state but stabilize the active state to shift population. The stabilizing active-state mutants stabilize both the inactive and active states, to different extents, resulting in a shift of the population. The binding affinity, or the mechanism of population shift, does not determine the allosteric efficacy. It is the extent of population shift, or the free energy change ΔΔG, that determines the allosteric efficacy.

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Allostery initiates and propagates by breaking existing interactions in one state and gaining new interactions in the other. Through allosteric coupling, the distinct interactions formed at the allosteric site specify the outcome in the distal active site, and the pathway to get there. (1) Identifying the triggering ligand atoms at the allosteric site for the diverse ligand–host assemblies in the cell, the types of (attractive, repulsive) interactions that they form with the receptor atoms, and the allosteric consequences can provide the foundation for unraveling allosteric conformational control in cellular processes. (22) A receptor can bind tens of ligands or proteins at a given site; each can encode a distinct cellular outcome. (27) These may reflect different triggering “warheads”, thrusting against the allosteric receptor site, and shaping the consequent allosteric modulation. The barcode that they encrypt is challenging, and in the cellular environment, at any given time, multiple allosteric triggers could act on a protein. Below, we provide the principles, aiming to map the landscape of current allosteric research and to focus on bottleneck questions. Within this framework, we underscore the challenge of identification of allosteric effectors—anchors and drivers—triggering interactions, where an anchor stabilizes the bound state and the driver fires its allosteric ramifications. We highlight the complexity of multiple drivers which may exist in protein–protein interfaces. We further draft an outline of how this problem could be construed and deciphered.

A Unified Model of the Allosteric Activation (Inactivation) Mechanism

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Allostery reflects a change or a shift in the distribution of the conformational ensemble. Population shift between states takes place when the preceding state gets destabilized and/or the next state gets stabilized. Population shift takes place in proteins, nucleic acids, (48, 49) and lipid assemblies, (50, 51) including cholesterol (52) and phosphatidylinositol triphosphates. It takes place within molecules and across their interfaces. Population shift links protein behavior, cellular pathways, and regulation under normal physiological conditions and in disease. The concept of population shift that we suggested in the late 1990s (53-59) recognizes that all conformational states pre-exist, including the active, inactive, and rare high-energy transition states and substates in catalytic reactions. (58, 60-67) It posits that rather than morphing one state into another, the ensemble shifts from the less stable to the more stable via sampling pre-existing conformations, and that this is the origin of the allosteric effect. The change in the relative stabilities between the states can take place either by destabilizing one state (e.g., the inactive) with respect to another (the active), or by stabilizing one state (e.g., the active) with respect to the other (inactive), or by both mechanisms. Two largely overlapping sets of residues are associated with the subtle conformational changes between the active and inactive conformations. Dynamic fluctuations of these same residues are responsible for the conformational switch between the two states. (68) The relative populations of the active and inactive states are largely determined by how much stabilization these two residue sets contribute to each.
To clarify the fundamental basis of the allosteric mechanism, we have recently described “how allostery works” from three different points of view. (1) The unified view considers allostery from the thermodynamic standpoint, (69, 70) in terms of the energy landscape of population shift, (62, 71) and from a simplified structural view of allostery, all with exactly the same allosteric descriptors. The unified view of allostery posits that allosteric efficacy is determined by the extent of the population shift. Allosteric coupling (or a communication pathway) does not determine the allosteric efficacy; however, it defines key residues that are critical for population shift. This substantiates the many works aimed at detecting allosteric propagation pathways—experimentally and computationally. (1, 22) Both driver and critical anchor atoms exhibit specific interactions with their host protein, with the former mainly responsible for the allosteric efficacy and the latter for binding affinity (potency).

Allosteric Efficacy Is Determined by the Extent of Population Shift

Allosteric activation events can be portrayed by a double-well, two-state model. The protein dominantly populates one of the states, the inactive or the active. The states are separated by a sizable but surmountable free energy barrier. (71) The population of each state is determined by the free energy differences between the two states. Binding of the allosteric effectors shifts the population eliciting allosteric effects. This raises a question: Is the allosteric efficacy determined by the binding affinity of the allosteric effector to the host, or by the extent of population shift?
A recent study on E3 ubiquitin ligase sheds light on this question. Ubiquitination results in protein degradation and plays a critical role in nearly all cellular processes. A cascade of enzymes, including E1 ubiquitin-activating enzymes, E2 ubiquitin-conjugating enzymes, and E3 ubiquitin ligases, is involved in the ubiquitination process. It is known that binding of E3 ubiquitin ligases to E2s can allosterically bring substrates and E2s into proximity to facilitate substrate ubiquitination. But does the binding affinity of E3:E2 determine the allosteric efficacy of E3? A high-throughput “deep mutational scanning” method has been used to assess the effects of nearly 100 000 protein variants of the U-box domain of the murine E3 ligase ubiquitination factor E4B (Ube4b) on the ubiquitination activities. (72) Interestingly, two distinct classes of mutations were found to enhance activity. One class of mutations, L1107I and M1124V, increases E3:E2-binding affinity. We denote this type of mutations as “stabilizing active state mutants”. The other class of mutations, D1139N and N1142T, do not change the binding affinity significantly, but exhibit the strongest induction of the active conformational states for ubiquitination, acting as “destabilizing inactive state mutations”. We illustrate the mechanisms of these two types of mutations in Figure 1. To quantify the population shift from an inactive to an active state (or vice versa), the extent of preferential binding can be formulated by stabilization versus destabilization. In this case, the WT protein favors the active state. The stabilizing active state mutants stabilize the active conformation more than they destabilize the inactive conformation, resulting in population shift toward the active state and enhanced allosteric activity. The destabilizing inactive state mutants achieve the same goal probably by destabilizing the inactive conformation more than by stabilizing the active conformation. Both mechanisms can enhance allosteric activity, suggesting that binding affinity does not determine the allosteric efficacy. Instead, the allosteric efficacy is determined by the population shift from the inactive state to the active state, which is determined by the free energy change between the two states.
Free energy comprises of enthalpy and entropy. A binding event may have favorable enthalpy change but unfavorable entropy change. An allosteric effect could be either enthalpy-driven or entropy-driven, depending on which contributes more to the change of free energy. It is difficult to disentangle these two contributions to the change of free energy, partly because it is extremely hard to measure entropy, either experimentally or computationally. The difficulty of quantitatively measure entropy greatly hinders the understanding of allosteric efficacy, especially for entropy-driven allostery. Recently, NMR relaxation techniques have been successfully used to quantitatively measure conformational entropy. (73) Changes in conformational dynamics of fluctuating methyl groups in a protein between conformational states are used as a “dynamical proxy”, which is an excellent proxy to quantitatively describe conformational entropy. (74) Molecular dynamics simulations (75) further validates the link between conformational dynamics and conformational entropy and provides the atomistic interpretation of employing “dynamical proxy” as the “entropy meter”. These advances can permit further quantification of the allosteric efficacies.

Coupling between the Allosteric and Active Sites Defines Key Residues That Shift or Reverse Population Shift

Detection of coupling between residues has been successfully used to identify allosteric pathways, allosteric sites and key residues. However, without a perturbation, such as binding events or mutations, the coupling itself does not decide the allosteric effect. For example, a group of residues on the VHL protein are coupled; however, without a mutation, no allosteric effects are observed. (76) The dominant population of the WT VHL is in the inactive state. In this state, VHL interacts with the E3 ligase component elonginC to facilitate substrate ubiquitination. The inactive state does not lead to disease development. The disease-related mutation Y98N is located far away from the VHL interface with elonginC, but it allosterically disrupts the VHL interaction with elonginC. As a destabilizing-inactive-state mutant, Y98N shifts the population from the inactive to the active state, which leads to development of type 2B VHL diseases. The designed mutations of the key residues, G123F and D179N, far away from both Y98 and the VHL:elonginC interface, allosterically stabilize the VHL:elonginC interface of the Y98N mutant to rescue the protein–protein interactions. These mutations reverse the population shift from the disease-causing active state to the WT inactive state and allosterically rescue VHL’s function. The population shift and the reversed population shift are illustrated in Figure 2.

Figure 2

Figure 2. Key residue mutations may switch the population shift. The energy landscape diagram of the WT VHL protein is shown on the left. The key residue mutations (middle), such as VHL Y98N, elicit the allosteric event by shifting the free energy landscape. The rescue mutations (right), such as VHL G123F and D179N, may reverse the population shift to one similar to the WT.

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The PDZ domain has been extensively studied to identify key-residues on allosteric pathways. Numerous experimental and computational methods have been developed to identify key residues on PDZ allosteric pathways, including NMR, (77) chemical shift covariance analysis (CHESCA), (78) statistical coupling analysis (SCA), (79) elastic network models (ENMs), (80-82) anisotropic thermal diffusion (ATD) MD simulation, (83) and pump–probe MD (PPMD) simulations. (84) Recently, a new method called “rigid residue scan” was developed to identify key residues for protein allostery using the PDZ domain as an example. (85) By systematically keeping each residue rigid during the MD simulations and comparing the correlated motions between the bound (active) and unbound (inactive) states, this method identified two groups of key residues in allosteric pathways. The degree of dynamics of one group of key residues, “wire residues”, does not affect the protein residue-coupling upon effector binding. Considering that the energy flow along allosteric pathways may cause significant dynamic changes to on-path residues, these least disruptive residues upon effector binding appear ideal for carrying out the propagation of energy and may constitute key on-path residues. The dynamics of another group of key residues, called “switch residues”, are important to differentiate the unbound (inactive) and bound (active) states. Therefore, mutations of these “switch residues” may have the potential to reverse the population shift caused by the binding events, as illustrated in Figure 2, from the active to the inactive states.
The term “allosteric hotspots” has been used to describe residues on the protein surface that are important for allosteric regulation. Co-evolutionary analysis showed that residues that co-evolved comprise structurally contiguous networks called sectors, (86) and allosteric hotspots are connected to sectors. (87) From the ensemble point of view of allostery, a specific residue-by-residue pathway may not be necessary involved, especially for disordered proteins. (88) In these cases, allosteric hot spot residues are more important than others for population shift rather than energy propagation. Mutation of these residues will have the potential to alter the population distribution.
Why can single or multiple mutations alter the population distribution? We may explain this from the standpoint of protein folding. Protein folding is a process dominantly driven by the hydrophobic effect toward native conformations along a funnel-like free energy landscape. (89, 90) When the folding process reaches the bottom of the funnel, it settles down to its native state by specific stabilizing interactions. Functioning as a node in the cellular circuit, a monomeric protein is usually required to switch its dominant population of conformations between active (ON) and inactive (OFF) states. This argues that a protein has been optimized by evolution to fold into several switchable conformational states at the bottom of the folding funnel. As individual conformations are stabilized by distinct residue–residue interactions, their relative populations rely on key interactions within these. Relaxation dispersion NMR spectroscopy (91) can detect a less-populated state so long as its relative population is above 0.5% and the different conformational states exchange on the millisecond time scale. (65) Although residues involved in the stabilization highly overlap among the conformational states, it is not surprising that single or multiple mutations could alter or even reverse the relative populations of the active and inactive states. (5, 92)

The Concept of Conjoint Anchor and Driver Atoms

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A key question in elucidating allosteric mechanisms in specific systems relates to what determines the direction of the population shift and how to explain or predict its consequences; that is, will the ligand be an agonist, inverse agonist, or antagonist. (22) To clarify this, we distinguish between two types of ligand atoms, driver and anchor. The ligand binds at an allosteric pocket. The pocket conformation with which an anchor atom interacts is unaffected during the transition from the inactive to the active state (or vice versa). The accurate positioning of the anchor in the allosteric pocket is critical since it provides the foundation that allows a driver atom to perform a “pull” and/or “push” action. This shifts the receptor population from the inactive to the active state (or vice versa) through a pre-existing communication pathway which has been optimized by evolution. The extent that pulling stabilizes the active and/or pushing destabilizes the inactive conformation, determines the shift to the active state. The mechanism of stabilization (or destabilization) differs between the anchor and driver atoms, and the agonism is determined by the presence or absence of a driver in an allosteric ligand. An anchor atom is likely to have the same interactions with the receptor in the active and inactive conformations. In contrast, a driver atom may form stabilizing or destabilizing interactions. A stabilizing attractive driver interaction such as a hydrogen bond or salt bridge which forms in the active but not in the inactive conformation can “pull” the inactive conformation into the active conformation. A destabilizing repulsive driver interaction in an allosteric pocket—in a position and orientation determined by the anchor atom—incurs steric hindrance in the inactive but not in the active conformation. Thus, the subtle conformational changes between the active and inactive conformations explain the type of driver atom interactions and its action. “Pulling” or “pushing” by even a single driver atom can favor a specific conformation unlike an anchor atom which favors both (active, inactive) states and does not provoke a population shift. This explains why even a slight change in ligand interactions, involving a mere substitution of a single atom may promote different—agonist or antagonist—consequences. Surprisingly, we observed that a combination of the global backbone displacement (GBD) and the local structural environment (LSE) change which reflects the extent of structural changes between active and inactive states, is able to identify and distinguish between driver types as well as identify coupled residues along the propagation pathway. (22)
Below, we provide few examples. The first relates to DNA acting as an allosteric effector, cooperatively mediating conformational changes in the dimerization and cofactor binding surface of nuclear receptors. Glucocorticoid receptor (GR) is one such case. GR consists of the N-terminal domain, DNA-binding domain (DBD), hinge region, ligand binding domain (LBD), and the C-terminal domain. Binding of agonists such as hormones to the LBD in the cytoplasm allosterically induces GR dimerization with consequent translocation into the nucleus where the DBD binds to DNA response elements (REs) to activate transcription initiation. (32) DNA binding induces a conformational change which alters the cofactor binding sites, shifting the GR population toward conformations which are complementary to the cofactor to modulate the glucocorticoid activity. The role of the RE as an allosteric effector of the steroid receptors has been well established. (39, 93-96) Yamamoto and his colleagues have elegantly shown that GR binding to REs differing by a single base pair leads to differential effects in the GR conformations and activities. (32, 97) Different RE sequences allosterically shift the ensemble of GR conformations, via a six-residue segment that connects helix H1 and the dimerization loop (the GR lever arm), with the population distribution further modified through interactions of the ligand binding receptor domain. (96) The cofactor then binds to a complementary conformation populated by the driver nucleotides, thereby initiating the cascading signaling pathway. Allosteric conformational changes can alter GR surfaces interacting with cofactors such as p160/SRC (steroid receptor coactivator) proteins which can interact with histone acetyltransferases, such as CBP (cAMP response element-binding protein (CREB)-binding protein) and p300).
p53 provides another example of RE-specific signaling pathway: binding of the p53 DNA binding domain (DBD) to the p53 REs initiates signaling that propagates to the p53 activation domain (p53AD) which in turn binds Mediator to activate or initiate transcription by RNA Polymerase II (Pol II) at the promoter. (40) Different REs have slightly different atomic contacts with the DBDs. These result in different pathways which transmit the DNA sequence specificity to the activation domain to initiate or activate an initiated-and-stalled Pol II. (40) These examples suggest that specific interactions of the DNA act as driver for GR and p53 to elicit sequence-specific effects. To verify and pinpoint the assignments, a detailed analysis of the crystal structures is needed. The multiple structures of GR with mutated REs and the functional consequences provide rich data for such analysis.
The third example relates to Akt1 kinase. (98-100) The activation of Akt kinase is regulated by the phosphorylation state of two residues in the activation loop (T308 in Akt1) by PDK1 and in the carboxyl-terminal tail (S473 in Akt1). Phosphorylation of these regulatory sites can take place following conformational changes induced by releasing its regulatory pleckstrin homology domain to dock to membrane lipid products. PDK-dependent Akt1 phosphorylation is reversed by protein phosphatase 2A (PP2A) which dephosphorylates pT308 and, to a lesser extent, pS473. In the phosphorylated state Akt is active; dephosphorylation by the phosphatase deactivates it. ATP, bound at the catalytic site between the two Akt lobes, reduces the sensitivity of phosphorylated Akt to be dephosphorylated by protein phosphatase 2A. The binding of ATP stabilizes the closed conformational state which has a strong structural coupling with the phosphorylated T308 in the activation loop, shielding it from phosphatase access. Following hydrolysis, with the ADP in the catalytic pocket, Akt1 relaxes into its open conformation, pT308 is exposed and dephosphorylated. This mechanism may account for ATP/ADP acting as ON/OFF switches in Akt1 catalysis. The difference between the actions of ADP versus ATP argues that the γ phosphate group of the allosteric ATP acts as a driver. The sugar moiety may act as an anchor. However, the verification of anchor and driver via a simple structural analysis (22) is only feasible when the structure of ADP bound Akt1 is available.
ATP/ADP acting as ON/OFF switches is also observed in the allosteric mechanism in the chaperone protein hsp70. Hsp70 has two domains: an N-terminal nucleotide-binding domain (NBD) connected to a C-terminal substrate-binding domain (SBD) through a linker about 10–12 residues in length. Hsp70 has closed and open states that are important to its function. (101) In its open state, as shown on the left panel in Figure 3A, hsp70 binds to ATP at NBD and displays low binding affinity to the substrate at SBD. Therefore, substrates can easily dissociate from SBD and cannot be refolded in the open state of hsp70s. Following ATP hydrolysis at the NBD, hsp70 switches to its closed state, which has strong binding affinity to the substrate. This new state, in which ADP is now bound to the NBD (Figure 3A, right), is the state in which hsp70 performs its main tasks as a chaperone protein to either refold or assist in degrading misfolded peptides. ATP hydrolysis has an integral role in the allosteric mechanism of hsp70. Structural analysis, as shown in Figure 3B, indicates a “pulling effect” by the γ-phosphate, whereas the conformation around the sugar moiety is relatively unchanged. Therefore, similar to the Akt1 kinases, the difference between ATP and ADP suggests assigning a driver role to the γ-phosphate group and an anchor role to the rest of the ATP molecule.

Figure 3

Figure 3. Schematic illustration of the definition of anchor and driver in hsp70 allosteric activation. (A) A structural comparison of Escherichia coli hsp70 DnaK in the ATP-bound form (left panel, orange) (PDB code: 4B9Q (130)) and ADP-bound form (right panel, green) (PDB code: 2KHO (131)). (B) Comparison of ATP and ADP at the catalytic center of hsp70. The left and right panels show the hsp70 catalytic center residues coordinated with the ATP structure (left, DnaK.ATP complex, PDB code: 4B9Q) and ADP structure (right, Hsc70.ADP.Pi complex, PDB code: 1HPM (132)). The middle panel is the superimposition of the left and right panels. The circled driver atoms (γ-phosphate) of ATP “pull” the Lys70 and Glu171 by more than 2 Å in the low-substrate-affinity conformation. The circled anchor atoms induced little conformational change in hsp70 residues in both states.

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Amino acid synthesis can provide another striking example. (102) 3-Deoxy-d-arabino-heptulosonate 7-phosphate synthase (DAH7PS) catalyzes the first step in the shikimate pathway, which is responsible for the biosynthesis of the aromatic amino acids Trp, Phe, and Tyr. Mycobacterium tuberculosis expresses a single DAH7PS enzyme which is controlled by combinations of these residues. The tetrameric enzyme has three allosteric sites. Site 1 is at the tetramer interface and is occupied dominantly by Trp. Site 2 is at the dimer interface and is occupied dominantly by Phe. Site 3 is Tyr-selective occupied in only two of the four subunits. In addition to site 2, Phe also binds to site 3 at high concentrations. When only one type of amino acid is present, the enzyme is unnoticeably inhibited. Allosteric synergistic inhibition is observed by a combination of two amino acids, Trp+Phe or Trp+Tyr. However, maximal inhibition requires the involvement of all three amino acids, presumably with binding of Phe in site 2 and Tyr in site 3.
This fine-tuning of enzyme activity by three different allosteric effectors with three distinct binding sites provides an important example of complex allosteric regulation revealing a network of three synergistic allosteric sites on one enzyme. The inhibition data has three clear allosteric implications. First, the hydroxyl group of Tyr bound at site 3 is the driver while the aromatic rings in Phe and Tyr are anchors. Second, an individual amino acid binds alone at its corresponding site, acting as an allosteric modulator but not as an allosteric effector, which by itself, is able to reverse the population of the active and inhibited states. Third, two additive allosteric modulators become an allosteric effector. Although various crystal structures of apo as well as single and duo amino acids occupancy are available in the PDB, a systematic structural analysis via 3-D superposition among them revealed that the overall structure and particularly the catalytic triads at the active site show no noticeable change. The results imply that the population shift from the active to the inactive state due to allosteric amino acids binding has been compensated by crystal packing effects (or crystallization conditions). A recent study with microseconds molecular dynamic simulation (5) has provided direct support for this suggested reasoning.

A Cascade of Allostery in Cell Signaling

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Signaling takes place through single-chain proteins and pathways, and through pathway crosstalk, ultimately across the cellular network down to gene activation or transcriptional regulation in the nucleus. Pathways are often activated by external stimuli or by metabolic messengers. Binding of hormones, peptides or small molecules to an extracellular domain of a cell-surface receptor transmits extracellular information to the cell to activate downstream intracellular signaling events. Although the mechanisms are diverse, activation is often coupled to ligand-induced dimerization that results in intracellular dimerization, as in the case of epidermal growth factor receptor (EGFR), a receptor tyrosine kinase (RTK). (103) Signals propagate through protein–protein interactions (or second messengers), cascading in the cell to initiate or repress gene-specific transcription. Crosstalk between signaling pathways generally takes place through shared proteins or signaling molecules whose concentration is regulated by both pathways. The two major cellular signaling pathways downstream of the activated RTK and G protein-coupled receptor (GPCR), Ras-RAF-MEK-ERK and PI3K-AKT, as illustrated in Figure 4, provide good examples. The pathways transduce signals received at the cell surface to give rise to protein synthesis, cell proliferation and survival. In the under-regulated MAPK/ERK pathway (104-106) the signal initiates when an extra-cellular ligand (the epidermal growth factor, EGF) binds EGFR. A resultant large conformational change in the ectodomain then facilitates EGFR dimerization. In turn, the increased proximity results in intracellular kinase activation through an asymmetric dimerization mechanism. The SH2 domain of the adaptor protein GRB2 binds to the phosphotyrosine residues of the activated EGFR and recruits the guanine nucleotide exchange factor (GEF) SOS through two SH3 domains. SOS becomes activated and promotes Ras (a GTPase, often existing in its H-Ras or K-Ras isoforms) to exchange GDP for GTP by destabilizing the Ras-GDP interaction. The activated GTP-bound Ras then leads to activation of Raf (MAP3K). The EGFR-Ras-Raf-MEK-ERK pathway, summarized in Figure 4, is involved in phosphorylation of transcription factors, such as myc, and downstream kinases, such as MNK (MAP kinase-interacting serine/threonine kinase) which subsequently phosphorylates the CREB (cAMP response element-binding) protein. Thus, a signal which initiated by interaction with the extracellular domain of a membrane receptor leads to changes in DNA expression, acting as an ON or OFF switch of the cell cycle. (107) Evolutionary mechanisms of proteins shared among pathways remain unclear. However, allosteric residues at protein–protein interfaces coevolve. (108) Combined mutations by directed evolution have been reported to evolve a robust signal transduction pathway from weak cross-talk. (109) Therefore, it is possible that a number of residues of the shared protein coevolve to merge pathways. Such mutations may be viewed as latent drivers in the evolution of cancer. (110)

Figure 4

Figure 4. Cell signaling pathways are allosterically elicited by the activated receptor tyrosine kinase (RTK). The illustration includes the two major signaling pathways, Ras-RAF-MEK-ERK and PI3K-AKT, for protein synthesis, cell proliferation, and cell survival, and the ubiquitin-proteasome pathway for allosteric regulation and signaling of protein degradation.

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Ubiquitination signals protein degradation. A sequential cascade initiates with covalent binding of ubiquitin to E1, transfer to E2, and finally to substrates via E3 ligases. The spatial conformational arrangement in each step requires an allosteric cascade in the ubiquitination process. The activation of ubiquitin requires ATP-catalyzed adenylation and thioester bond formation between ubiquitin and an E1 cysteine residue. Spatially, there exists a ∼35 Å gap between the E1 adenylation site and the catalytic cysteine. There is also ∼20 Å gap between the catalytic cysteine sites of E1 and E2. (111) Crystallographic data (111, 112) suggested a “thioester switch” mechanism, in which the thioester bond serves as a driver to induce a large conformational change and shift the population from the inactive to the active state to transfer ubiquitin from E1 to E2. Binding of E3 (gp78) to E2, (113) rather than the overall binding affinity, allosterically induces conformational changes in the active sites, shifting the population to signal the end of the ubiquitin transfer from E1 to E2 and the start of the ubiquitin transfer step from E2 to the substrate. Couplings have been observed between residues of E3 ligases, (114-119) which determine key residues on the allosteric pathways, but not the allosteric efficacy. It is the protein–protein interactions, rather than binding affinity, that shift the populations to the active state to position E2 and the substrate in proximity to conclude the ubiquitination process.

Some Guidelines toward Delineating the Allosteric Efficacy

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As long as the conformational states of the active and major inactive structures are available, the structural mechanism of the allosteric agonism can be assessed by following the guidelines provided in the unified view of “how allostery works”. (1) The efficacy of allosteric activation is proportional to the stabilization of the active state plus destabilization of the prevailing inactive state and the activation event can be either an allosteric ligand binding or mutations. If structural comparison of the active and inactive conformations illustrates changes at the active and allosteric ligand binding sites, a straightforward structural analysis (22) may be sufficient to verify the driver and anchor. In principle, the parts of an agonist (driver) that stabilize the active or destabilize the inactive state can be distinguished from the (anchor) parts that are responsible for the binding. It is the degree of preferential binding to the active (or inactive) state but not the overall ligand binding potency that determines the allosteric efficacy of activation (or inhibition).
In allosteric regulation, the various extents of structural changes at the active site provide a direct explanation of protein function-switching. However, frequently there are no noticeable structural changes between a liganded active (or inactive) complex and an unliganded inactive (or active) protein. (120) In such cases, a simple structural analysis is clearly insufficient to unveil the allosteric efficacy. Three possible situations can account for cases that lack significant structural changes. First, the functional switch is mediated by a disorder-to-order population shift. In this case, only one structure is captured, which is the ordered state. Second, a ligand acting as an allosteric modulator instead of an agonist or inverse agonist is not expected to confer noticeable conformational changes unless an orthosteric ligand or an allosteric agonist are bound at the respective sites. Third, crystal packing or crystallization conditions stabilize the opposite state or destabilize the state promoted by the allosteric action, resulting in limited conformational change. Microseconds molecular dynamic simulations (5) might be able to sample the other prevailing state and overcome such a single state problem.

Conclusions

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The current understanding of the allosteric phenomena is based on molecular conformational ensembles rather than only two, T (tense) and R (relaxed) states. This leads to viewing allostery in terms of population statistics, conformational sampling and probabilities. Allostery works by shifting a populated conformation from one state to another. Because each distinct state has a distinct function, the shift alters the specific function executed by the molecule. Allosteric shift between states takes place through changes in the interactions, which alter the relative stabilities of the states. Allostery takes place in proteins, RNA, DNA and lipids; it propagates through their interactions, and the effects control cell signaling. Allostery can be expressed by small or large conformational (enthalpic) and/or dynamic (entropic) changes. (45) From the evolutionary standpoint, pre-existing optimized states facilitate the emergence of new functions; on the down side, it also leads to “allosteric diseases”, driven through pathological interactions, covalent (e.g., mutations) and non-covalent (pathogen proteins), which transform physiological signaling, keeping it in a constitutively ON (or OFF) state. Even though allostery takes place in single molecules, its consequences propagate through their interactions, which may eventually span the cell. (46) Since allostery reflects the behavior of the ensemble, allostery is a statistical effect, and this holds across the structural spectrum, from stable, structured proteins to highly disordered protein states. (57) The probabilistic nature of allostery can be seen from the behavior of inter-domain linkers and loops. Rather than sampling conformational space homogeneously, an allosteric event in one domain can result in biased sampling of space of the other. Evolution has pre-encoded successive conformational states along major allosteric propagation pathways in linker sequences, with each state encoded by the previous one. The lower barriers between hierarchically populated states result in faster time scales even for large conformational changes. (18)
Here, we outlined the principles of allosteric interactions in cell signaling and provided an overview of the mechanisms through which they operate. We distinguished between allosteric efficacy and potency; efficacy relates to the stabilizing/destabilizing effect of the effector on the active/inactive conformational substates of the protein and potency relates to binding affinity. We emphasized that an allosteric propagation pathway does not determine the allosteric efficacy; however, it defines what makes a binding site allosteric. Allosteric propagation consists of residues with coupled behavior. We suggested that two, largely overlapping, sets of residues are responsible for stabilizing the active and destabilizing the inactive states (or vice versa), and that these same residues are also responsible for dynamic fluctuation of the conformations. Finally, we emphasized that to reveal the allosteric mechanism availability of the structures of the active and inactive states is essential. The changes of the local environment and global backbone movement between the two states may provide clues. Conformational changes are likely to propagate through the backbone, whereas the perturbation upon effector interaction at the allosteric site may settle and be accommodated by local conformational changes of side chains. (121) When considering ligand design, it behooves us to recall that the change of affinity for an agonist at the allosteric site does not determine the capacity of the protein to acquire a specific allosteric effect. Instead, it is the relative stabilization (or destabilization) which determines the population shift from one state to the other. Collectively, these provide the underpinnings of allostery.
The overarching challenge is to quantify the allosteric efficacies and integrate the results across proteins and pathways in the living cell. To quantify the allosteric efficacies, a big challenge is to determine the structures of low-populated yet functionally important states, which may be accessible by relaxation dispersion NMR. (75, 122, 123) How to characterize the dynamics of the allosteric mechanism is yet another challenge facing experiment and computation. To identify atoms of allosteric effectors that can act as driver or anchor, which is critical for gaining insight in drug discovery, ultra-high-resolution X-ray structures at subatomic level will be invaluable. How to integrate the current knowledge of allostery at the molecular level across proteins and pathways in the living cell requires collaborative efforts of chemists, biophysics, and systems biologists, for example, to build allo-networks, which depict allostery at the cellular level. Lastly, we behooves us to mention that the concept of allostery has been used beyond the living cell by chemists, including the allosteric modulation of supramolecular chirality in an artificial self-assembled system, (124) biomimetic molecular allosteric analogues in chemosensors to amplify signaling, (125) to incorporate allosteric regulation in organometallic catalysts, (126) as well as in non-cooperative receptors, (127) to design allosterically tunable switches by heavy metals, (128) and to narrow the dynamic range of aptamer-based sensors. (129)

Author Information

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  • Corresponding Author
    • Ruth Nussinov - Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States;  Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
  • Authors
    • Chung-Jung Tsai - Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
    • Jin Liu - Department of Biophysics, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, United States;  Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), and Center for Scientific Computation, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

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This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract no. HHSN261200800001E. This research was supported (in part) by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

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This article references 132 other publications.

  1. 1
    Tsai, C.-J.; Nussinov, R. PLoS Comput. Biol. 2014, 10e1003394
    Google Scholar
    1
    A unified view of "how allostery works"
    Tsai, Chung-Jung; Nussinov, Ruth
    PLoS Computational Biology (2014), 10 (2), e1003394/1-e1003394/12, 12 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
    The question of how allostery works was posed almost 50 years ago. Since then it has been the focus of much effort. This is for two reasons: first, the intellectual curiosity of basic science and the desire to understand fundamental phenomena, and second, its vast practical importance. Allostery is at play in all processes in the living cell, and increasingly in drug discovery. Many models have been successfully formulated, and are able to describe allostery even in the absence of a detailed structural mechanism. However, conceptual schemes designed to qual. explain allosteric mechanisms usually lack a quant. math. model, and are unable to link its thermodn. and structural foundations. This hampers insight into oncogenic mutations in cancer progression and biased agonists' actions. Here, we describe how allostery works from three different standpoints: thermodn., free energy landscape of population shift, and structure; all with exactly the same allosteric descriptors. This results in a unified view which not only clarifies the elusive allosteric mechanism but also provides structural grasp of agonist-mediated signaling pathways, and guides allosteric drug discovery. Of note, the unified view reasons that allosteric coupling (or communication) does not det. the allosteric efficacy; however, a communication channel is what makes potential binding sites allosteric.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXkvVCmtLo%253D&md5=3912bf89813eeed254b3e3e4e1937f99
  2. 2
    Gunasekaran, K.; Ma, B. Y.; Nussinov, R. Proteins: Struct., Funct. Bioinf. 2004, 57, 433
    Google Scholar
    There is no corresponding record for this reference.
  3. 3
    Nussinov, R.; Tsai, C. J.; Xin, F.; Radivojac, P. Trends Biochem. Sci. 2012, 37, 447
    Google Scholar
    There is no corresponding record for this reference.
  4. 4
    Gao, X.; Xie, X.; Pashkov, I.; Sawaya, M. R.; Laidman, J.; Zhang, W.; Cacho, R.; Yeates, T. O.; Tang, Y. Chem. Biol. 2009, 16, 1064
    Google Scholar
    4
    Directed Evolution and Structural Characterization of a Simvastatin Synthase
    Gao, Xue; Xie, Xinkai; Pashkov, Inna; Sawaya, Michael R.; Laidman, Janel; Zhang, Wenjun; Cacho, Ralph; Yeates, Todd O.; Tang, Yi
    Chemistry & Biology (Cambridge, MA, United States) (2009), 16 (10), 1064-1074CODEN: CBOLE2; ISSN:1074-5521. (Cell Press)
    Enzymes from natural product biosynthetic pathways are attractive candidates for creating tailored biocatalysts to produce semisynthetic pharmaceutical compds. LovD is an acyltransferase that converts the inactive monacolin J acid (MJA) into the cholesterol-lowering lovastatin. LovD can also synthesize the blockbuster drug simvastatin using MJA and a synthetic α-dimethylbutyryl thioester, albeit with suboptimal properties as a biocatalyst. Here we used directed evolution to improve the properties of LovD toward semisynthesis of simvastatin. Mutants with improved catalytic efficiency, soly., and thermal stability were obtained, with the best mutant displaying an ∼11-fold increase in an Escherichia coli-based biocatalytic platform. To understand the structural basis of LovD enzymol., seven X-ray crystal structures were detd., including the parent LovD, an improved mutant G5, and G5 cocrystd. with ligands. Comparisons between the structures reveal that beneficial mutations stabilize the structure of G5 in a more compact conformation that is favorable for catalysis.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtlCnt7fM&md5=1b9a7341c930be855884d27dc91100f1
  5. 5
    Jiménez-Osés, G.; Osuna, S.; Gao, X.; Sawaya, M. R.; Gilson, L.; Collier, S. J.; Huisman, G. W.; Yeates, T. O.; Tang, Y.; Houk, K. N. Nat. Chem. Biol. 2014, 10, 431
    Google Scholar
    5
    The role of distant mutations and allosteric regulation on LovD active site dynamics
    Jimenez-Oses, Gonzalo; Osuna, Silvia; Gao, Xue; Sawaya, Michael R.; Gilson, Lynne; Collier, Steven J.; Huisman, Gjalt W.; Yeates, Todd O.; Tang, Yi; Houk, K. N.
    Nature Chemical Biology (2014), 10 (6), 431-436CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
    Natural enzymes have evolved to perform their cellular functions under complex selective pressures, which often require their catalytic activities to be regulated by other proteins. We contrasted a natural enzyme, LovD, which acts on a protein-bound (LovF) acyl substrate, with a lab.-generated variant that was transformed by directed evolution to accept instead a small free acyl thioester and no longer requires the acyl carrier protein. The resulting 29-mutant variant is 1000-fold more efficient in the synthesis of the drug simvastatin than the wild-type LovD. This is to our knowledge the first nonpatent report of the enzyme currently used for the manuf. of simvastatin as well as the intermediate evolved variants. Crystal structures and microsecond-scale mol. dynamics simulations revealed the mechanism by which the lab.-generated mutations free LovD from dependence on protein-protein interactions. Mutations markedly altered conformational dynamics of the catalytic residues, obviating the need for allosteric modulation by the acyl carrier LovF.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXmtlaht7Y%253D&md5=235f9c2bc5fe1574523a375dfd518bfa
  6. 6
    Tzeng, S. R.; Kalodimos, C. G. Curr. Opin. Struct. Biol. 2011, 21, 62
    Google Scholar
    6
    Protein dynamics and allostery: An NMR view
    Tzeng, Shiou-Ru; Kalodimos, Charalampos G.
    Current Opinion in Structural Biology (2011), 21 (1), 62-67CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
    A review. Allostery, the process by which distant sites within a protein system are energetically coupled, is an efficient and ubiquitous mechanism for activity regulation. A purely mech. view of allostery invoking only structural changes has developed over the decades as the classical view of the phenomenon. However, a fast growing list of examples illustrate the intimate link between internal motions over a wide range of time scales and function in protein-ligand interactions. Proteins respond to perturbations by redistributing their motions and they use fluctuating conformational states for binding and conformational entropy as a carrier of allosteric energy to modulate assocn. with ligands. In several cases allosteric interactions proceed with minimal or no structural changes. We discuss emerging paradigms for the central role of protein dynamics in allostery.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhvFKhuro%253D&md5=7ee2b2686e0c6a8ca4d337a33fd322ce
  7. 7
    Zhuravlev, P. I.; Papoian, G. A. Q. Rev. Biophys. 2010, 43, 295
    Google Scholar
    7
    Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework
    Zhuravlev, Pavel I.; Papoian, Garegin A.
    Quarterly Reviews of Biophysics (2010), 43 (3), 295-332CODEN: QURBAW; ISSN:0033-5835. (Cambridge University Press)
    A review. Energy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein - the native landscape - is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase vol. is much smaller, allowing a protein to fully sample its native energy landscape on the biol. timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a topog. map' of the native landscape that can be used for subsequent anal. Several major approaches to representing this topog. map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode anal. can provide such variables in cases where functional motions are dictated by the mol.'s architecture. Principal component anal. is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long mol. dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, preexisting equil.' and induced fit', are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some exptl. evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXht1Onu77F&md5=69a48379dd351943c953e41a949681fa
  8. 8
    Kenakin, T. P. Trends Pharmacol. Sci. 2009, 30, 460
    Google Scholar
    There is no corresponding record for this reference.
  9. 9
    Reiter, E.; Ahn, S.; Shukla, A. K.; Lefkowitz, R. J. Annu. Rev. Pharmacol. Toxicol. 2012, 52, 179
    Google Scholar
    There is no corresponding record for this reference.
  10. 10
    del Sol, A.; Tsai, C. J.; Ma, B. Y.; Nussinov, R. Structure 2009, 17, 1042
    Google Scholar
    10
    The Origin of Allosteric Functional Modulation: Multiple Pre-existing Pathways
    del Sol, Antonio; Tsai, Chung-Jung; Ma, Buyong; Nussinov, Ruth
    Structure (Cambridge, MA, United States) (2009), 17 (8), 1042-1050CODEN: STRUE6; ISSN:0969-2126. (Cell Press)
    A review. Although allostery draws increasing attention, not much is known about allosteric mechanisms. Here we argue that in all proteins, allosteric signals transmit through multiple, pre-existing pathways; which pathways dominate depend on protein topologies, specific binding events, covalent modifications, and cellular (environmental) conditions. Further, perturbation events at any site on the protein surface (or in the interior) will not create new pathways but only shift the pre-existing ensemble of pathways. Drugs binding at different sites or mutational events in disease shift the ensemble toward the same conformations; however, the relative populations of the different states will change. Consequently the obsd. functional, conformational, and dynamic effects will be different. This is the origin of allosteric functional modulation in dynamic proteins: allostery does not necessarily need to invoke conformational rearrangements to control protein activity and pre-existing pathways are always defaulted to during allostery regardless of the stimulant and perturbation site in the protein.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXps1Clt7s%253D&md5=a5c26c07e4a4fab4e85d0b1fae65a4ac
  11. 11
    Zocchi, G. Annu. Rev. Biophys. 2009, 38, 75
    Google Scholar
    There is no corresponding record for this reference.
  12. 12
    Whitley, M. J.; Lee, A. L. Curr. Protein & Pept. Sci. 2009, 10, 116
    Google Scholar
    12
    Frameworks for understanding long-range intra-protein communication
    Whitley, Matthew J.; Lee, Andrew L.
    Current Protein and Peptide Science (2009), 10 (2), 116-127CODEN: CPPSCM; ISSN:1389-2037. (Bentham Science Publishers Ltd.)
    A review. The phenomenon of intraprotein communication is fundamental to such processes as allostery and signaling, yet comparatively little is understood about its phys. origins despite notable progress in recent years. This review introduces contemporary but distinct frameworks for understanding intraprotein communication by presenting both the ideas behind them and a discussion of their successes and shortcomings. The first framework holds that intraprotein communication is accomplished by the sequential mech. linkage of residues spanning a gap between distal sites. According to the second framework, proteins are best viewed as ensembles of distinct structural microstates, the dynamical and thermodn. properties of which contribute to the exptl. observable macroscale properties. NMR spectroscopy is a powerful method for studying intraprotein communication, and the insights into both frameworks it provides are presented through a discussion of numerous examples from the literature. Distinct from mech. and thermodn. considerations of intraprotein communication are recently applied graph and network theoretic analyses. These computational methods reduce complex 3-dimensional protein architectures to simple maps comprised of nodes (residues) connected by edges (inter-residue interactions). Anal. of these graphs yields a characterization of the protein's topol. and network characteristics. These methods have shown proteins to be small world networks with moderately high local residue connectivities existing concurrently with a small but significant no. of long range connectivities. However, exptl. studies of the tantalizing idea that these putative long-range interaction pathways facilitate one or several macroscopic protein characteristics are unfortunately lacking at present. This review concludes by comparing and contrasting the presented frameworks and methodologies for studying intraprotein communication and suggests a manner in which they can be brought to bear simultaneously to further enhance the understanding of this important fundamental phenomenon.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXlsFSqsL4%253D&md5=41bc6cfba52490ceab5b687bba3b0c1a
  13. 13
    Tsai, C. J.; Del Sol, A.; Nussinov, R. Mol. Biosyst. 2009, 5, 207
    Google Scholar
    13
    Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms
    Tsai, Chung-Jung; del Sol, Antonio; Nussinov, Ruth
    Molecular BioSystems (2009), 5 (3), 207-216CODEN: MBOIBW; ISSN:1742-206X. (Royal Society of Chemistry)
    Allostery has come of age; the no., breadth and functional roles of documented protein allostery cases are rising quickly. Since all dynamic proteins are potentially allosteric and allostery plays crucial roles in all cellular pathways, sorting and classifying allosteric mechanisms in proteins should be extremely useful in understanding and predicting how the signals are regulated and transmitted through the dynamic multi-mol. cellular organizations. Classification organizes the complex information thereby unraveling relationships and patterns in mol. activation and repression. In signaling, current classification schemes consider classes of mols. according to their functions; for example, epinephrine and norepinephrine secreted by the central nervous system are classified as neurotransmitters. Other schemes would account for epinephrine when secreted by the adrenal medulla to be hormone-like. Yet, such classifications account for the global function of the mol.; not for the mol. mechanism of how the signal transmission initiates and how it is transmitted. Here we provide a unified view of allostery and the first classification framework. We expect that a classification scheme would assist in comprehension of allosteric mechanisms, in prediction of signaling on the mol. level, in better comprehension of pathways and regulation of the complex signals, in translating them to the cascading events, and in allosteric drug design. We further provide a range of examples illustrating mechanisms in protein allostery and their classification from the cellular functional standpoint.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXitF2hurw%253D&md5=a3f036625bbfdb5cd5a61df7af886ebd
  14. 14
    Goodey, N. M.; Benkovic, S. J. Nat. Chem. Biol. 2008, 4, 474
    Google Scholar
    14
    Allosteric regulation and catalysis emerge via a common route
    Goodey, Nina M.; Benkovic, Stephen J.
    Nature Chemical Biology (2008), 4 (8), 474-482CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
    A review. Allosteric regulation of protein function is a mechanism by which an event in one place of a protein structure causes an effect at another site, much like the behavior of a telecommunications network in which a collection of transmitters, receivers and transceivers communicate with each other across long distances. For example, ligand binding or an amino acid mutation at an allosteric site can alter enzymic activity or binding affinity in a distal region such as the active site or a second binding site. The mechanism of this site-to-site communication is of great interest, esp. since allosteric effects must be considered in drug design and protein engineering. In this review, conformational mobility as the common route between allosteric regulation and catalysis is discussed. We summarize recent exptl. data and the resulting insights into allostery within proteins, and we discuss the nature of future studies and the new applications that may result from increased understanding of this regulatory mechanism.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXoslClsb8%253D&md5=74d502f64c00fcef21c623bc84db3159
  15. 15
    Cui, Q.; Karplus, M. Protein Sci. 2008, 17, 1295
    Google Scholar
    15
    Allostery and cooperativity revisited
    Cui, Qiang; Karplus, Martin
    Protein Science (2008), 17 (8), 1295-1307CODEN: PRCIEI; ISSN:0961-8368. (Cold Spring Harbor Laboratory Press)
    A review. Although phenomenol. models that account for cooperativity in allosteric systems date back to the early and mid-60's [e.g., the D. E. Koshland, G. Nemethy, and D. Filmer, 1966 (KNF) and J. Momod, J. Wyman, and J. P. Changeux, 1965 (MWC) models], there is resurgent interest in the topic due to the recent exptl. and computational studies that attempted to reveal, at an atomistic level, how allostery actually works. Here, using systems for which atomistic simulations have been carried out in their groups as examples, the authors describe the current understanding of allostery and how the mechanisms go beyond the classical MWC/Pauling-KNF descriptions, and point out that the "new view" of allostery, emphasizing "population shifts," is, in fact, an "old view.". The presentation offers not only an up-to-date description of allostery from a theor./computational perspective, but also helps to resolve several outstanding issues concerning allostery.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXpsVyjurY%253D&md5=40d789fc47dcb9f01e797aa724861f77
  16. 16
    Leitner, D. M. Annu. Rev. Phys. Chem. 2008, 59, 233
    Google Scholar
    There is no corresponding record for this reference.
  17. 17
    Fuxreiter, M.; Simon, I.; Bondos, S. Trends Biochem. Sci. 2011, 36, 415
    Google Scholar
    There is no corresponding record for this reference.
  18. 18
    Ma, B.; Tsai, C.-J.; Haliloğlu, T.; Nussinov, R. Structure 2011, 19, 907
    Google Scholar
    There is no corresponding record for this reference.
  19. 19
    Dixit, A.; Verkhivker, G. M. PLoS Comput. Biol. 2011, 7e1002179
    Google Scholar
    There is no corresponding record for this reference.
  20. 20
    Nussinov, R.; Ma, B.; Tsai, C.-J. Biophys. Chem. 2014, 186, 22
    Google Scholar
    There is no corresponding record for this reference.
  21. 21
    Nussinov, R.; Tsai, C.-J. Cell 2013, 153, 293
    Google Scholar
    There is no corresponding record for this reference.
  22. 22
    Nussinov, R.; Tsai, C.-J. Trends Pharmacol. Sci. 2014, 35, 256
    Google Scholar
    There is no corresponding record for this reference.
  23. 23
    Nussinov, R.; Tsai, C. J.; Ma, B. Annu. Rev. Biophys. 2013, 42, 169
    Google Scholar
    23
    The underappreciated role of allostery in the cellular network
    Nussinov, Ruth; Tsai, Chung-Jung; Ma, Buyong
    Annual Review of Biophysics (2013), 42 (), 169-189CODEN: ARBNCV; ISSN:1936-122X. (Annual Reviews)
    A review. Allosteric propagation results in communication between distinct sites in the protein structure; it also encodes specific effects on cellular pathways, and in this way it shapes cellular response. One example of long-range effects is binding of morphogens to cell surface receptors, which initiates a cascade of protein interactions that leads to genome activation and specific cellular action. Allosteric propagation results from combinations of multiple factors, takes place through dynamic shifts of conformational ensembles, and affects the equil. of macromol. interactions. Here, we (a) emphasize the well-known yet still underappreciated role of allostery in conveying explicit signals across large multimol. assemblies and distances to specify cellular action; (b) stress the need for quantitation of the allosteric effects; and finally, (c) propose that each specific combination of allosteric effectors along the pathway spells a distinct function. The challenges are colossal; the inspiring reward will be predicting function, misfunction, and outcomes of drug regimes.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFGrs7bJ&md5=9d239bd6685c4c158ef706bd6081a183
  24. 24
    Liu, J.; Nussinov, R. Crit. Rev. Biochem. Mol. Biol. 2013, 48, 89
    Google Scholar
    24
    The role of allostery in the ubiquitin-proteasome system
    Liu, Jin; Nussinov, Ruth
    Critical Reviews in Biochemistry and Molecular Biology (2013), 48 (2), 89-97CODEN: CRBBEJ; ISSN:1040-9238. (Informa Healthcare)
    A review. The ubiquitin-proteasome system (UPS) is involved in many cellular processes including protein degrdn. Degrdn. of a protein via this system involves two successive steps: ubiquitination and degrdn. Ubiquitination tags the target protein with ubiquitin-like proteins (UBLs), such as ubiquitin, small ubiquitin-like modifier (SUMO) and NEDD8, via a cascade involving three enzymes: activating enzyme E1, conjugating enzyme E2 and E3 ubiquitin ligases. The proteasomes recognize the UBL-tagged substrate proteins and degrade them. Accumulating evidence indicates that allostery is a central player in the regulation of ubiquitination, as well as deubiquitination and degrdn. Here, we provide an overview of the key mechanistic roles played by allostery in all steps of these processes, and highlight allosteric drugs targeting them. Throughout the review, we emphasize the crucial mechanistic role played by linkers in allosterically controlling the UPS action by biasing the sampling of the conformational space, which facilitate the catalytic reactions of the ubiquitination and degrdn. Finally, we propose that allostery may similarly play key roles in the regulation of mol. machines in the cell, and as such allosteric drugs can be expected to be increasingly exploited in therapeutic regimes.
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  25. 25
    Palazzesi, F.; Barducci, A.; Tollinger, M.; Parrinello, M. Proc. Natl. Acad. Sci. U.S.A. 2013, 110, 14237
    Google Scholar
    There is no corresponding record for this reference.
  26. 26
    Korkmaz, E. N.; Nussinov, R.; Haliloglu, T. PLoS Comput. Biol. 2012, 8e1002420
    Google Scholar
    There is no corresponding record for this reference.
  27. 27
    Nussinov, R.; Ma, B.; Tsai, C.-J.; Csermely, P. Structure 2013, 21, 1509
    Google Scholar
    There is no corresponding record for this reference.
  28. 28
    Tsai, C.-J.; Nussinov, R. Biochem. J. 2011, 439, 15
    Google Scholar
    There is no corresponding record for this reference.
  29. 29
    Laine, E.; Auclair, C.; Tchertanov, L. PLoS Comput. Biol. 2012, 8e1002661
    Google Scholar
    There is no corresponding record for this reference.
  30. 30
    Endres, N. F.; Engel, K.; Das, R.; Kovacs, E.; Kuriyan, J. Curr. Opin. Struct. Biol. 2011, 21, 777
    Google Scholar
    30
    Regulation of the catalytic activity of the EGF receptor
    Endres, Nicholas F.; Engel, Kate; Das, Rahul; Kovacs, Erika; Kuriyan, John
    Current Opinion in Structural Biology (2011), 21 (6), 777-784CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
    A review. The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase involved in cell growth that is often misregulated in cancer. Several recent studies highlight the unique structural mechanisms involved in its regulation. Some elucidate the important role that the juxtamembrane segment and the transmembrane helix play in stabilizing the activating asym. kinase dimer, and suggest that its activation mechanism is likely to be conserved among the other human EGFR-related receptors. Other studies provide new explanations for two long obsd., but poorly understood phenomena, the apparent heterogeneity in ligand binding and the formation of ligand-independent dimers. New insights into the allosteric mechanisms utilized by intracellular regulators of EGFR provide hope that allosteric sites could be used as targets for drug development.
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  31. 31
    Collavin, L.; Lunardi, A.; Del Sal, G. Cell Death Differ. 2010, 17, 901
    Google Scholar
    31
    p53-family proteins and their regulators: hubs and spokes in tumor suppression
    Collavin, L.; Lunardi, A.; Del Sal, G.
    Cell Death and Differentiation (2010), 17 (6), 901-911CODEN: CDDIEK; ISSN:1350-9047. (Nature Publishing Group)
    A review. The tumor suppressor p53 is a central hub in a mol. network controlling cell proliferation and death in response to potentially oncogenic conditions, and a wide array of covalent modifications and protein interactions modulate the nuclear and cytoplasmic activities of p53. The p53 relatives, p73 and p63, are entangled in the same regulatory network, being subject at least in part to the same modifications and interactions that convey signals on p53, and actively contributing to the resulting cellular output. The emerging picture is that of an interconnected pathway, in which all p53-family proteins are involved in the response to oncogenic stress and physiol. inputs. Therefore, common and specific interactors of p53-family proteins can have a wide effect on function and dysfunction of this pathway. Many years of research have uncovered an impressive no. of p53-interacting proteins, but much less is known about protein interactions of p63 and p73. Yet, many interactors may be shared by multiple p53-family proteins, with similar or different effects. In this study we review shared interactors of p53-family proteins with the aim to encourage research into this field; this knowledge promises to unveil regulatory elements that could be targeted by a new generation of mols., and allow more efficient use of currently available drugs for cancer treatment. Cell Death and Differentiation (2010) 17, 901-911; doi:10.1038/cdd.2010.35; published online 9 Apr. 2010.
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  32. 32
    Gronemeyer, H.; Bourguet, W. Sci. Signaling 2009, 2pe34
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    32
    Allosteric effects govern nuclear receptor action: DNA appears as a player
    Gronemeyer Hinrich; Bourguet William
    Science signaling (2009), 2 (73), pe34 ISSN:.
    Nuclear receptors (NRs) are a family of transcription factors that regulate cognate gene networks, resulting in profound physiological and pathophysiological changes. Dysfunctional NR signaling leads to proliferative, reproductive, and metabolic diseases such as cancer, infertility, obesity, or diabetes. Indeed, NR-based pharmaceuticals are among the most commonly used drugs. NRs function by communicating with the intracellular and extracellular environment, thereby both sensing and modulating the status of cells. They respond to incoming signals by orchestrating transcriptional as well as nongenomic effects. They do so through an ability to respond to various effectors, such as the cognate ligand, by allosteric structural alterations that are the basis of further signal propagation. A mechanism has now been revealed by which DNA could act as an allosteric effector to modulate glucocorticoid receptor activity. This is a new regulatory paradigm for NR action that may help to explain how a receptor fine-tunes its target gene network.
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  33. 33
    Gupta, M.; Hendrickson, A. E. W.; Yun, S. S.; Han, J. J.; Schneider, P. A.; Koh, B. D.; Stenson, M. J.; Wellik, L. E.; Shing, J. C.; Peterson, K. L.; Flatten, K. S.; Hess, A. D.; Smith, B. D.; Karp, J. E.; Barr, S.; Witzig, T. E.; Kaufmann, S. H. Blood 2012, 119, 476
    Google Scholar
    There is no corresponding record for this reference.
  34. 34
    Johannessen, C. M.; Boehm, J. S.; Kim, S. Y.; Thomas, S. R.; Wardwell, L.; Johnson, L. A.; Emery, C. M.; Stransky, N.; Cogdill, A. P.; Barretina, J.; Caponigro, G.; Hieronymus, H.; Murray, R. R.; Salehi-Ashtiani, K.; Hill, D. E.; Vidal, M.; Zhao, J. J.; Yang, X. P.; Alkan, O.; Kim, S.; Harris, J. L.; Wilson, C. J.; Myer, V. E.; Finan, P. M.; Root, D. E.; Roberts, T. M.; Golub, T.; Flaherty, K. T.; Dummer, R.; Weber, B. L.; Sellers, W. R.; Schlegel, R.; Wargo, J. A.; Hahn, W. C.; Garraway, L. A. Nature 2010, 468, 968
    Google Scholar
    There is no corresponding record for this reference.
  35. 35
    Joseph, R. E.; Xie, Q. A.; Andreotti, A. H. J. Mol. Biol. 2010, 403, 231
    Google Scholar
    There is no corresponding record for this reference.
  36. 36
    Kar, G.; Keskin, O.; Gursoy, A.; Nussinov, R. Curr. Opin. Pharmacol. 2010, 10, 715
    Google Scholar
    36
    Allostery and population shift in drug discovery
    Kar, Gozde; Keskin, Ozlem; Gursoy, Attila; Nussinov, Ruth
    Current Opinion in Pharmacology (2010), 10 (6), 715-722CODEN: COPUBK; ISSN:1471-4892. (Elsevier Ltd.)
    A review. Proteins can exist in a large no. of conformations around their native states that can be characterized by an energy landscape. The landscape illustrates individual valleys, which are the conformational substates. From the functional standpoint, there are two key points: first, all functionally relevant substates pre-exist; and second, the landscape is dynamic and the relative populations of the substates will change following allosteric events. Allosteric events perturb the structure, and the energetic strain propagates and shifts the population. This can lead to changes in the shapes and properties of target binding sites. Here we present an overview of dynamic conformational ensembles focusing on allosteric events in signaling. We propose that combining equil. fluctuation concepts with genomic screens could help drug discovery.
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  37. 37
    Marti, M. A.; Estrin, D. A.; Roitberg, A. E. J. Phys. Chem. B 2009, 113, 2135
    Google Scholar
    There is no corresponding record for this reference.
  38. 38
    Martin-Montalvo, A.; Villalba, J. M.; Navas, P.; de Cabo, R. Oncogene 2011, 30, 505
    Google Scholar
    There is no corresponding record for this reference.
  39. 39
    Meijsing, S. H.; Pufall, M. A.; So, A. Y.; Bates, D. L.; Chen, L.; Yamamoto, K. R. Science 2009, 324, 407
    Google Scholar
    There is no corresponding record for this reference.
  40. 40
    Meyer, K. D.; Lin, S. C.; Bernecky, C.; Gao, Y. F.; Taatjes, D. J. Nat. Struct. Mol. Biol. 2010, 17, 753
    Google Scholar
    40
    p53 activates transcription by directing structural shifts in Mediator
    Meyer, Krista D.; Lin, Shih-chieh; Bernecky, Carrie; Gao, Yuefeng; Taatjes, Dylan J.
    Nature Structural & Molecular Biology (2010), 17 (6), 753-760CODEN: NSMBCU; ISSN:1545-9993. (Nature Publishing Group)
    It is not well understood how the human Mediator complex, transcription factor IIH and RNA polymerase II (Pol II) work together with activators to initiate transcription. Activator binding alters Mediator structure, yet the functional consequences of such structural shifts remain unknown. The p53 C terminus and its activation domain interact with different Mediator subunits, and we find that each interaction differentially affects Mediator structure; strikingly, distinct p53-Mediator structures differentially affect Pol II activity. Only the p53 activation domain induces the formation of a large pocket domain at the Mediator-Pol II interaction site, and this correlates with activation of stalled Pol II to a productively elongating state. Moreover, we define a Mediator requirement for TFIIH-dependent Pol II C-terminal domain phosphorylation and identify substantial differences in Pol II C-terminal domain processing that correspond to distinct p53-Mediator structural states. Our results define a fundamental mechanism by which p53 activates transcription and suggest that Mediator structural shifts trigger activation of stalled Pol II complexes.
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  41. 41
    Nussinov, R. Br. J. Pharmacol. 2012, 165, 2110
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    41
    Allosteric modulators can restore function in an amino acid neurotransmitter receptor by slightly altering intra-molecular communication pathways
    Nussinov, Ruth
    British Journal of Pharmacology (2012), 165 (7), 2110-2112CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)
    A review. Mutations, even if not directly in the ligand binding sites of proteins, can lead to disease. In cell surface receptors, this can happen if they uncouple conformational changes that take place upon agonist (or antagonist) binding to the extracellular domain and the intracellular response. Uncoupling can take place by disrupting a major allosteric propagation pathway between the extra- and intracellular domains. The author provides a mechanistic explanation: first describing how propagation takes place; second, what can happen in the presence of a disease-related mutation which is distant from the binding site; and finally, how drugs may overcome this disruption and rescue function. The mutations in the glycine receptor α1 subunit (α1R271Q/L) which cause the neuromotor disorder hyperekplexia are on example of such allosteric mutations. In this issue of the BJP, Shan et al. show that normal function was restored to these mutant receptors by substitution of the segment which contained the mutated position, by a homologous one. An allosteric drug could mimic the effects of such substitution. Within this framework, the author highlights the advantages of allosteric drugs and the challenges in their design.
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  42. 42
    Nussinov, R.; Tsai, C.-J.; Csermely, P. Trends Pharmacol. Sci. 2011, 32, 686
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    There is no corresponding record for this reference.
  43. 43
    Pfaff, S. J.; Fletterick, R. J. J. Biol. Chem. 2010, 285, 15256
    Google Scholar
    There is no corresponding record for this reference.
  44. 44
    Pan, Y. P.; Tsai, C. J.; Ma, B. Y.; Nussinov, R. Trends Genet. 2010, 26, 75
    Google Scholar
    44
    Mechanisms of transcription factor selectivity
    Pan, Yongping; Tsai, Chung-Jung; Ma, Buyong; Nussinov, Ruth
    Trends in Genetics (2010), 26 (2), 75-83CODEN: TRGEE2; ISSN:0168-9525. (Elsevier B.V.)
    A review. The initiation of transcription is regulated by transcription factors (TFs) binding to DNA response elements (REs). How do TFs recognize specific binding sites among the many similar ones available in the genome Recent research has illustrated that even a single nucleotide substitution can alter the selective binding of TFs to coregulators, that prior binding events can lead to selective DNA binding, and that selectivity is influenced by the availability of binding sites in the genome. Here, we combine structural insights with recent genomics screens to address the problem of TF-DNA interaction specificity. The emerging picture of selective binding site sequence recognition and TF activation involves three major factors: the cellular network, protein and DNA as dynamic conformational ensembles and the tight packing of multiple TFs and coregulators on stretches of regulatory DNA. The classification of TF recognition mechanisms based on these factors impacts our understanding of how transcription initiation is regulated.
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  45. 45
    Tsai, C. J.; del Sol, A.; Nussinov, R. J. Mol. Biol. 2008, 378, 1
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    There is no corresponding record for this reference.
  46. 46
    Nussinov, R. Phys. Biol. 2013, 10045004
    Google Scholar
    There is no corresponding record for this reference.
  47. 47
    Nussinov, R.; Tsai, C.-J. Chem. Biol. 2014, 21, 311
    Google Scholar
    47
    Free Energy Diagrams for Protein Function
    Nussinov, Ruth; Tsai, Chung-Jung
    Chemistry & Biology (Oxford, United Kingdom) (2014), 21 (3), 311-318CODEN: CBOLE2; ISSN:1074-5521. (Elsevier Ltd.)
    A review. Simplified representations can be powerful. Two common examples are sequence logos and ribbon diagrams. Both have been extraordinarily successful in capturing complex static features of sequences and structures. Capturing function is challenging, since activation involves triggered dynamic shifts between ON and OFF states. Here, we show that simple funnel drawings can capture and usefully portray proteins by their cellular triggering mechanism. The funnel shape around the proteins' native states can describe mechanisms of upstream signal integration and downstream response. "Function diagrams" are important: they can combine diverse biochem. data to visually distinguish among activation (or recruitment) mechanisms and tag proteins in cellular networks, clarifying their mechanism at a glance. We create templates for function classification and suggest that they can extend signaling pathway maps. Of note, the diagrams describe free energy landscapes; thus, they can be quantified. We name our dynamic free-energy diagrams dFEDs.
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  48. 48
    Russell, R.; Zhuang, X. W.; Babcock, H. P.; Millett, I. S.; Doniach, S.; Chu, S.; Herschlag, D. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 155
    Google Scholar
    There is no corresponding record for this reference.
  49. 49
    Kim, S.; Brostromer, E.; Xing, D.; Jin, J. S.; Chong, S. S.; Ge, H.; Wang, S. Y.; Gu, C.; Yang, L. J.; Gao, Y. Q.; Su, X. D.; Sun, Y. J.; Xie, X. S. Science 2013, 339, 816
    Google Scholar
    There is no corresponding record for this reference.
  50. 50
    Piwonski, H. M.; Goomanovsky, M.; Bensimon, D.; Horovitz, A.; Haran, G. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, E1437
    Google Scholar
    There is no corresponding record for this reference.
  51. 51
    Perez, Y.; Maffei, M.; Igea, A.; Amata, I.; Gairi, M.; Nebreda, A. R.; Bernado, P.; Pons, M. Sci. Rep. 2013, 31295
    Google Scholar
    51
    Lipid binding by the Unique and SH3 domains of c-Src suggests a new regulatory mechanism
    Perez, Yolanda; Maffei, Mariano; Igea, Ana; Amata, Irene; Gairi, Margarida; Nebreda, Angel R.; Bernado, Pau; Pons, Miquel
    Scientific Reports (2013), 3 (), 1295, 9 pp.CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)
    C-Src is a non-receptor tyrosine kinase involved in numerous signal transduction pathways. The kinase, SH3 and SH2 domains of c-Src are attached to the membrane-anchoring SH4 domain through the flexible Unique domain. Here we show intra- and inter-mol. interactions involving the Unique and SH3 domains suggesting the presence of a previously unrecognized addnl. regulation layer in c-Src. We have characterized lipid binding by the Unique and SH3 domains, their intramol. interaction and its allosteric modulation by a SH3-binding peptide or by Calcium-loaded calmodulin binding to the Unique domain. We also show reduced lipid binding following phosphorylation at conserved sites of the Unique domain. Finally, we show that injection of full-length c-Src with mutations that abolish lipid binding by the Unique domain causes a strong in vivo phenotype distinct from that of wild-type c-Src in a Xenopus oocyte model system, confirming the functional role of the Unique domain in c-Src regulation.
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  52. 52
    Liu, W.; Chun, E.; Thompson, A. A.; Chubukov, P.; Xu, F.; Katritch, V.; Han, G. W.; Roth, C. B.; Heitman, L. H.; Ijzerman, A. P.; Cherezov, V.; Stevens, R. C. Science 2012, 337, 232
    Google Scholar
    52
    Structural Basis for Allosteric Regulation of GPCRs by Sodium Ions
    Liu, Wei; Chun, Eugene; Thompson, Aaron A.; Chubukov, Pavel; Xu, Fei; Katritch, Vsevolod; Han, Gye Won; Roth, Christopher B.; Heitman, Laura H.; IJzerman, Adriaan P.; Cherezov, Vadim; Stevens, Raymond C.
    Science (Washington, DC, United States) (2012), 337 (6091), 232-236CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Pharmacol. responses of G protein-coupled receptors (GPCRs) can be fine-tuned by allosteric modulators. Structural studies of such effects have been limited due to the medium resoln. of GPCR structures. We reengineered the human A2A adenosine receptor by replacing its third intracellular loop with apocytochrome b562RIL and solved the structure at 1.8 angstrom resoln. The high-resoln. structure allowed us to identify 57 ordered water mols. inside the receptor comprising three major clusters. The central cluster harbors a putative sodium ion bound to the highly conserved aspartate residue Asp2.50. Addnl., two cholesterols stabilize the conformation of helix VI, and one of 23 ordered lipids intercalates inside the ligand-binding pocket. These high-resoln. details shed light on the potential role of structured water mols., sodium ions, and lipids/cholesterol in GPCR stabilization and function.
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  53. 53
    Tsai, C. J.; Kumar, S.; Ma, B. Y.; Nussinov, R. Protein Sci. 1999, 8, 1181
    Google Scholar
    53
    Folding funnels, binding funnels, and protein function
    Tsai, Chung-Jung; Kumar, Sandeep; Ma, Buyong; Nussinov, Ruth
    Protein Science (1999), 8 (6), 1181-1190CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
    A review with 66 refs. Folding funnels have been the focus of considerable attention during the last few years. These have mostly been discussed in the general context of the theory of protein folding. Here we extend the utility of the concept of folding funnels, relating them to biol. mechanisms and function. In particular, here we describe the shape of the funnels in light of protein synthesis and folding; flexibility, conformational diversity, and binding mechanisms; and the assocd. binding funnels, illustrating the multiple routes and the range of complexed conformers. Specifically, the walls of the folding funnels, their crevices, and bumps are related to the complexity of protein folding, and hence to sequential vs. non-sequential folding. Whereas the former is more frequently obsd. in eukaryotic proteins, where the rate of protein synthesis is slower, the latter is more frequent in prokaryotes, with faster translation rates. The bottoms of the funnels reflect the extent of the flexibility of the proteins. Rugged floors imply a range of conformational isomers, which may be close on the energy landscape. Rather than undergoing an induced fit binding mechanism, the conformational ensembles around the rugged bottoms argue that the conformers, which are most complementary to the ligand, will bind to it with the equil. shifting in their favor. Furthermore, depending on the extent of the ruggedness, or of the smoothness with only a few min., we may infer nonspecific, broad range vs. specific binding. In particular, folding and binding are similar processes, with similar underlying principles. Hence, the shape of the folding funnel of the monomer enables making reasonable guesses regarding the shape of the corresponding binding funnel. Proteins having a broad range of binding, such as proteolytic enzymes or relatively nonspecific endonucleases, may be expected to have not only rugged floors in their folding funnels, but their binding funnels will also behave similarly, with a range of complexed conformations. Hence, knowledge of the shape of the folding funnels is biol. very useful. The converse also holds: if kinetic and thermodn. data are available, hints regarding the role of the protein and its binding selectivity may be obtained. Thus, the utility of the concept of the funnel carries over to the origin of the protein and to its function.
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  54. 54
    Ma, B. Y.; Kumar, S.; Tsai, C. J.; Nussinov, R. Protein Eng. 1999, 12, 713
    Google Scholar
    54
    Folding funnels and binding mechanisms
    Ma, Buyong; Kumar, Sandeep; Tsai, Chung-Jung; Nussinov, Ruth
    Protein Engineering (1999), 12 (9), 713-720CODEN: PRENE9; ISSN:0269-2139. (Oxford University Press)
    The long-held views on lock-and-key vs. induced fit in binding arose from the notion that a protein exists in a single, most stable conformation, dictated by its sequence. However, in soln., proteins exist in a range of conformations, which may be described by statistical mech. laws, and their populations follow statistical distributions. Upon binding, the equil. will shift in favor of the bound conformation, from the ensemble of conformations around the bottom of the folding funnel. Hence, here we extend the implications and the usefulness of the folding funnel concept to explain fundamental binding mechanisms.
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  55. 55
    Tsai, C. J.; Ma, B. Y.; Nussinov, R. Proc. Natl. Acad. Sci. U.S.A. 1999, 96, 9970
    Google Scholar
    There is no corresponding record for this reference.
  56. 56
    Kumar, S.; Ma, B. Y.; Tsai, C. J.; Sinha, N.; Nussinov, R. Protein Sci. 2000, 9, 10
    Google Scholar
    56
    Folding and binding cascades: dynamic landscapes and population shifts
    Kumar, Sandeep; Ma, Buyong; Tsai, Chung-Jung; Sinha, Neeti; Nussinov, Ruth
    Protein Science (2000), 9 (1), 10-19CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
    A review, with ∼56 refs. Whereas previously we have successfully utilized the folding funnels concept to rationalize binding mechanisms and to describe binding, here we further extend the concept of folding funnels, illustrating its utility in explaining enzyme pathways, multimol. assocns., and allostery. This extension is based on the recognition that funnels are not stationary; rather, they are dynamic, depending on the phys. or binding conditions. Different binding states change the surrounding environment of proteins. The changed environment is in turn expressed in shifted energy landscapes, with different shapes and distributions of populations of conformers. Hence, the function of a protein and its properties are not only decided by the static folded three-dimensional structure; they are detd. by the distribution of its conformational substates, and in particular, by the redistributions of the populations under different environments. That is, protein function derives from its dynamic energy landscape, caused by changes in its surroundings.
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  57. 57
    Tsai, C. J.; Ma, B. Y.; Sham, Y. Y.; Kumar, S.; Nussinov, R. Proteins: Struct., Funct. Genet. 2001, 44, 418
    Google Scholar
    There is no corresponding record for this reference.
  58. 58
    Ma, B. Y.; Shatsky, M.; Wolfson, H. J.; Nussinov, R. Protein Sci. 2002, 11, 184
    Google Scholar
    58
    Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations
    Ma, Buyong; Shatsky, Maxim; Wolfson, Haim J.; Nussinov, Ruth
    Protein Science (2002), 11 (2), 184-197CODEN: PRCIEI; ISSN:0961-8368. (Cold Spring Harbor Laboratory Press)
    A review. Here, we comment on the steadily increasing body of data showing that proteins with specificity actually bind ligands of diverse shapes, sizes, and compn. Such a phenomenon is not surprising when one considers that binding is a dynamic process with populations in equil. and that the shape of the binding site is strongly influenced by the mol. partner. It derives implicity from the concept of populations. All proteins, specific and nonspecific, exist in ensembles of substates. If the library of ligands in soln. is large enough, favorably matching ligands with altered shapes and sizes can be expected to bind, with a redistribution of the protein populations. Point mutations at spatially distant sites may exert large conformational rearrangements and hinge effects, consistent with mutations away from the binding site leading to population shifts and (cross-)drug resistance. A similar effect is obsd. in protein superfamilies, in which different sequences with similar topologies display similar large-scale dynamic motions. The hinges are frequently at analogous sites, yet with different substrate specificity. Similar topologies yield similar conformational isomers, although with different distributions of population times, owing to the change in the conditions, i.e., the change in the sequences. In turn, different distributions relate to binding of different sizes and shapes. Hence, the binding site shape and size are defined by the ligand. They are not independent entities of fixed proportions and cannot be analyzed independently of the binding partner. Such a proposition derives from viewing proteins as dynamic distributions, presenting to the incoming ligands a range of binding site shapes. It illustrates how presumably specific binding mols. can bind multiple ligands. In terms of drug design, the ability of a single receptor to recognize many dissimilar ligands shows the need to consider more diverse mols. It provides a rationale for higher affinity inhibitors that are not derived from substrates at their transition states and indicates flexible docking schemes.
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  59. 59
    Boehr, D. D.; Nussinov, R.; Wright, P. E. Nat. Chem. Biol. 2009, 5, 789
    Google Scholar
    59
    The role of dynamic conformational ensembles in biomolecular recognition
    Boehr, David D.; Nussinov, Ruth; Wright, Peter E.
    Nature Chemical Biology (2009), 5 (11), 789-796CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
    A review. Mol. recognition by biomols. is central to all biol. processes. For the past 50 yr, the 'induced fit' hypothesis of D. E. Koshland (1958) has been the textbook explanation for mol. recognition events. However, recent exptl. evidence supports an alternative mechanism. The 'conformational selection' model postulates that all conformations pre-exist, and that the ligand selects the most favored conformation. Following binding, the ensemble undergoes a population shift, redistributing the conformational states. Both conformational selection and induced fit appear to play roles. Following binding by a primary conformational selection event, optimization of side-chain and backbone interactions is likely to proceed by an induced fit mechanism. Conformational selection has been obsd. for protein-ligand, protein-protein, protein-DNA, protein-RNA and RNA-ligand interactions. These data support a new mol. recognition paradigm for processes as diverse as signaling, catalysis, gene regulation, and protein aggregation in disease, which has the potential to significantly impact researchers' views and strategies in drug design, biomol. engineering, and mol. evolution.
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  60. 60
    Bosshard, H. R. News Physiol. Sci. 2001, 16, 171
    Google Scholar
    60
    Molecular recognition by induced fit: how fit is the concept?
    Bosshard, Hans Rudolf
    News in Physiological Sciences (2001), 16 (Aug.), 171-174CODEN: NEPSEY; ISSN:0886-1714. (International Union of Physiological Sciences)
    A review. Induced fit explains why biomols. can bind together even if they are not optimized for binding. However, induced fit can lead to a kinetic bottleneck and does not describe every interaction in the absence of prior complementarity. Preselection of a fitting conformer is an alternative to induced fit. Topics discussed include the expt. demonstration of binding by conformational selection and the energy landscape model of protein conformation.
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  61. 61
    Csermely, P.; Palotai, R.; Nussinov, R. Trends Biochem. Sci. 2010, 35, 539
    Google Scholar
    61
    Induced fit, conformational selection and independent dynamic segments: an extended view of binding events
    Csermely, Peter; Palotai, Robin; Nussinov, Ruth
    Trends in Biochemical Sciences (2010), 35 (10), 539-546CODEN: TBSCDB; ISSN:0968-0004. (Elsevier Ltd.)
    A review. Single mol. and NMR measurements of protein dynamics increasingly uncover the complexity of binding scenarios. Here, we describe an extended conformational selection model that embraces a repertoire of selection and adjustment processes. Induced fit can be viewed as a subset of this repertoire, whose contribution is affected by the bond types stabilizing the interaction and the differences between the interacting partners. We argue that protein segments whose dynamics are distinct from the rest of the protein (discrete breathers) can govern conformational transitions and allosteric propagation that accompany binding processes and, as such, might be more sensitive to mutational events. Addnl., we highlight the dynamic complexity of binding scenarios as they relate to events such as aggregation and signalling, and the crowded cellular environment.
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  62. 62
    Gardino, A. K.; Villali, J.; Kivenson, A.; Lei, M.; Liu, C. F.; Steindel, P.; Eisenmesser, E. Z.; Labeikovsky, W.; Wolf-Watz, M.; Clarkson, M. W.; Kern, D. Cell 2009, 139, 1109
    Google Scholar
    There is no corresponding record for this reference.
  63. 63
    Fraser, J. S.; Clarkson, M. W.; Degnan, S. C.; Erion, R.; Kern, D.; Alber, T. Nature 2009, 462, 669
    Google Scholar
    There is no corresponding record for this reference.
  64. 64
    Boehr, D. D. Cell 2009, 139, 1049
    Google Scholar
    There is no corresponding record for this reference.
  65. 65
    Korzhnev, D. M.; Kay, L. E. Acc. Chem. Res. 2008, 41, 442
    Google Scholar
    There is no corresponding record for this reference.
  66. 66
    Ziarek, J. J.; Getschman, A. E.; Butler, S. J.; Taleski, D.; Stephens, B.; Kufareva, I.; Handel, T. M.; Payne, R. J.; Volkman, B. F. ACS Chem. Biol. 2013, 8, 1955
    Google Scholar
    There is no corresponding record for this reference.
  67. 67
    Long, D.; Brueschweiler, R. J. Am. Chem. Soc. 2011, 133, 18999
    Google Scholar
    There is no corresponding record for this reference.
  68. 68
    Aykaç Fas, B.; Tutar, Y.; Haliloğlu, T. PLoS Comput. Biol. 2013, 9e1003141
    Google Scholar
    There is no corresponding record for this reference.
  69. 69
    Leff, P. Trends Pharmacol. Sci. 1995, 16, 89
    Google Scholar
    There is no corresponding record for this reference.
  70. 70
    Hall, D. A. Mol. Pharmacol. 2000, 58, 1412
    Google Scholar
    There is no corresponding record for this reference.
  71. 71
    Deupi, X.; Kobilka, B. K. Physiology 2010, 25, 293
    Google Scholar
    71
    Energy landscapes as a tool to integrate GPCR structure, dynamics, and function
    Deupi, Xavier; Kobilka, Brian K.
    Physiology (2010), 25 (Oct.), 293-303CODEN: PHYSCI; ISSN:1548-9213. (International Union of Physiological Sciences)
    A review. G protein-coupled receptors (GPCRs) are versatile signaling mols. that mediate the majority of physiol. responses to hormones and neurotransmitters. Recent: high-resoln. structural insight into GPCR structure and dynamics are beginning to shed light on the mol. basis of this versatility. We use energy landscapes to conceptualize the link between structure and function.
    >> More from SciFinder ®
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  72. 72
    Starita, L. M.; Pruneda, J. N.; Lo, R. S.; Fowler, D. M.; Kim, H. J.; Hiatt, J. B.; Shendure, J.; Brzovic, P. S.; Fields, S.; Klevit, R. E. Proc. Natl. Acad. Sci. U.S.A. 2013, 110, E1263
    Google Scholar
    There is no corresponding record for this reference.
  73. 73
    Marlow, M. S.; Dogan, J.; Frederick, K. K.; Valentine, K. G.; Wand, A. J. Nat. Chem. Biol. 2010, 6, 352
    Google Scholar
    73
    The role of conformational entropy in molecular recognition by calmodulin
    Marlow, Michael S.; Dogan, Jakob; Frederick, Kendra K.; Valentine, Kathleen G.; Wand, A. Joshua
    Nature Chemical Biology (2010), 6 (5), 352-358CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
    The phys. basis for high-affinity interactions involving proteins is complex and potentially involves a range of energetic contributions. Among these are changes in protein conformational entropy, which cannot yet be reliably computed from mol. structures. We have recently used changes in conformational dynamics as a proxy for changes in conformational entropy of calmodulin upon assocn. with domains from regulated proteins. The apparent change in conformational entropy was linearly related to the overall binding entropy. This view warrants a more quant. foundation. Here we calibrate an 'entropy meter' using an exptl. dynamical proxy based on NMR relaxation and show that changes in the conformational entropy of calmodulin are a significant component of the energetics of binding. Furthermore, the distribution of motion at the interface between the target domain and calmodulin is surprisingly noncomplementary. These observations promote modification of our understanding of the energetics of protein-ligand interactions.
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  74. 74
    Wand, A. J. Curr. Opin. Struct. Biol. 2013, 23, 75
    Google Scholar
    74
    The dark energy of proteins comes to light: conformational entropy and its role in protein function revealed by NMR relaxation
    Wand, A. Joshua
    Current Opinion in Structural Biology (2013), 23 (1), 75-81CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
    A review. Historically it has been virtually impossible to exptl. det. the contribution of residual protein entropy to fundamental protein activities such as the binding of ligands. Recent progress has illuminated the possibility of employing NMR relaxation methods to quant. det. the role of changes in conformational entropy in mol. recognition by proteins. The method rests on using fast internal protein dynamics as a proxy. Initial results reveal a large and variable role for conformational entropy in the binding of ligands by proteins. Such a role for conformational entropy in mol. recognition has significant implications for enzymol., signal transduction, allosteric regulation and the development of protein-directed pharmaceuticals.
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  75. 75
    Kasinath, V.; Sharp, K. A.; Wand, A. J. J. Am. Chem. Soc. 2013, 135, 15092
    Google Scholar
    There is no corresponding record for this reference.
  76. 76
    Gerber, S. H.; Rah, J. C.; Min, S. W.; Liu, X.; de Wit, H.; Dulubova, I.; Meyer, A. C.; Rizo, J.; Arancillo, M.; Hammer, R. E.; Verhage, M.; Rosenmund, C.; Sudhof, T. C. Science 2008, 321, 1507
    Google Scholar
    There is no corresponding record for this reference.
  77. 77
    Manley, G.; Loria, J. P. Arch. Biochem. Biophys. 2012, 519, 223
    Google Scholar
    There is no corresponding record for this reference.
  78. 78
    Selvaratnam, R.; Chowdhury, S.; VanSchouwen, B.; Melacini, G. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 6133
    Google Scholar
    There is no corresponding record for this reference.
  79. 79
    Lockless, S. W.; Ranganathan, R. Science 1999, 286, 295
    Google Scholar
    There is no corresponding record for this reference.
  80. 80
    De Los Rios, P.; Cecconi, F.; Pretre, A.; Dietler, G.; Michielin, O.; Piazza, F.; Juanico, B. Biophys. J. 2005, 89, 14
    Google Scholar
    There is no corresponding record for this reference.
  81. 81
    Gerek, Z. N.; Keskin, O.; Ozkan, S. B. Proteins 2009, 77, 796
    Google Scholar
    There is no corresponding record for this reference.
  82. 82
    Gerek, Z. N.; Ozkan, S. B. PLoS Comput. Biol. 2011, 7e1002154
    Google Scholar
    There is no corresponding record for this reference.
  83. 83
    Ota, N.; Agard, D. A. J. Mol. Biol. 2005, 351, 345
    Google Scholar
    There is no corresponding record for this reference.
  84. 84
    Sharp, K.; Skinner, J. J. Proteins 2006, 65, 347
    Google Scholar
    There is no corresponding record for this reference.
  85. 85
    Kalescky, R.; Liu, J.; Tao, P. J. Phys. Chem. A. 2014,  DOI: 10.1021/jp5083455
    Google Scholar
    There is no corresponding record for this reference.
  86. 86
    Halabi, N.; Rivoire, O.; Leibler, S.; Ranganathan, R. Cell 2009, 138, 774
    Google Scholar
    There is no corresponding record for this reference.
  87. 87
    Reynolds, K. A.; McLaughlin, R. N.; Ranganathan, R. Cell 2011, 147, 1564
    Google Scholar
    There is no corresponding record for this reference.
  88. 88
    Hilser, V. J.; Thompson, E. B. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 8311
    Google Scholar
    There is no corresponding record for this reference.
  89. 89
    Wolynes, P. G.; Onuchic, J. N.; Thirumalai, D. Science 1995, 267, 1619
    Google Scholar
    89
    Navigating the folding routes
    Wolynes, Peter G.; Onuchic, Jose N.; Thirumalai, D.
    Science (Washington, D. C.) (1995), 267 (5204), 1619-20CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    A review and discussion with 11 refs. Computer models and conclusions from these models of protein folding are described.
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    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXksVajtLc%253D&md5=942686abd595fa3283b334da0304a585
  90. 90
    Dill, K. A.; Chan, H. S. Nat. Struct. Biol. 1997, 4, 10
    Google Scholar
    There is no corresponding record for this reference.
  91. 91
    Palmer, A. G.; Kroenke, C. D.; Loria, J. P. Nucl. Magn. Reson. Biol. Macromol., B 2001, 339, 204
    Google Scholar
    91
    Nuclear magnetic resonance methods for quantifying microsecond-to-millisecond motions in biological macromolecules
    Palmer, Arthur G., III; Kroenke, Christopher D.; Loria, J. Patrick
    Methods in Enzymology (2001), 339 (Nuclear Magnetic Resonance of Biological Macromolecules, Part B), 204-238CODEN: MENZAU; ISSN:0076-6879. (Academic Press)
    A review. A review focuses on a subset of 13C and 15N heteronuclear ZZ-exchange, Carr-Purcell-Meiboom-Gill, and R[tρ] relaxation techniques that are sensitive to mol. motions or chem. kinetic processes on μs-ms time scales. The theor. and exptl. aspects of homonuclear and heteronuclear rotating frame relaxation are also discussed. (c) 2001 Academic Press.
    >> More from SciFinder ®
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  92. 92
    Wan, P. T. C.; Garnett, M. J.; Roe, S. M.; Lee, S.; Niculescu-Duvaz, D.; Good, V. M.; Jones, C. M.; Marshall, C. J.; Springer, C. J.; Barford, D.; Marais, R.; Cancer Genome, P. Cell 2004, 116, 855
    Google Scholar
    There is no corresponding record for this reference.
  93. 93
    Reid, K. J.; Hendy, S. C.; Saito, J.; Sorensen, P.; Nelson, C. C. J. Biol. Chem. 2001, 276, 2943
    Google Scholar
    There is no corresponding record for this reference.
  94. 94
    Wood, J. R.; Greene, G. L.; Nardulli, A. M. Mol. Cell. Biol. 1998, 18, 1927
    Google Scholar
    94
    Estrogen response elements function as allosteric modulators of estrogen receptor conformation
    Wood, Jennifer R.; Greene, Geoffrey L.; Nardulli, Ann M.
    Molecular and Cellular Biology (1998), 18 (4), 1927-1934CODEN: MCEBD4; ISSN:0270-7306. (American Society for Microbiology)
    The estrogen receptor (ER) is a ligand-dependent transcription factor that regulates the expression of estrogen-responsive genes. ER-mediated transcriptional changes are brought about by interaction of the ER with the estrogen response element (ERE). In this study, the authors examd. the interaction of the Xenopus laevis ER DNA binding domain (DBD) and the intact ER with the X. laevis vitellogenin A2 ERE and the human pS2 ERE. Using gel mobility shift, DNase I footprinting, and methylation interference assays, the authors demonstrated that the DBD bound only as a dimer to the A2 ERE. However, the DBD bound as a monomer to the consensus pS2 ERE half site at lower DBD concns. and then as a homodimer to the consensus and imperfect pS2 ERE half site at higher DBD concns. Antibody supershift expts. carried out with partially purified, yeast expressed full-length ER demonstrated that three ER-specific antibodies interacted differentially with A2 and pS2 ERE-bound ER, indicating that receptor epitopes were differentially exposed. Furthermore, partial digestion of the A2 and pS2 ERE-bound ER with chymotrypsin or trypsin produced distinct protease cleavage patterns. Taken together, these data provide evidence that differential interaction of the DBD with the A2 and pS2 EREs brings about global changes in ER conformation. The conformational changes in ER induced by individual ERE sequences could lead to assocn. of the receptor with different transcription factors and assist in the differential modulation of estrogen-responsive genes in target cells.
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  95. 95
    Geserick, C.; Meyer, H. A.; Haendler, B. Mol. Cell. Endocrinol. 2005, 236, 1
    Google Scholar
    95
    The role of DNA response elements as allosteric modulators of steroid receptor function
    Geserick, Christoph; Meyer, Hellmuth-Alexander; Haendler, Bernard
    Molecular and Cellular Endocrinology (2005), 236 (1-2), 1-7CODEN: MCEND6; ISSN:0303-7207. (Elsevier Ltd.)
    A review. Steroid receptors are ligand-activated transcription factors which control the expression of their target genes by binding to specific DNA elements. Consensus response elements have been delineated for the glucocorticoid, androgen, progesterone and mineralocorticoid receptors on one hand (steroid response element, SRE) and for the estrogen receptor on the other hand (estrogen response element, ERE). Small variations in these sequences not only affect the binding but may also have a dramatic impact on the transcriptional activity of steroid receptors. It has now become obvious that DNA response elements do not merely tether regulatory proteins to control regions of target genes but may addnl. impart conformational changes onto the DNA-binding domain as well as to neighboring domains of steroid receptors. This in turn will create unique platforms for selective recruitment of cofactors and possibly for induction of modifications in local chromatin architecture. An addnl. level of complexity is added by the frequent presence of multiple response elements in gene promoter regions. The allosteric effects of DNA response elements on steroid receptors may be essential for differential gene expression and this offers interesting perspectives for the identification of selective modulators.
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  96. 96
    Nagy, L.; Schwabe, J. W. R. Trends Biochem. Sci. 2004, 29, 317
    Google Scholar
    There is no corresponding record for this reference.
  97. 97
    Whittle, C. M.; Lazakovitch, E.; Gronostajski, R. M.; Lieb, J. D. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 12049
    Google Scholar
    There is no corresponding record for this reference.
  98. 98
    Chan, T. O.; Zhang, J.; Rodeck, U.; Pascal, J. M.; Armen, R. S.; Spring, M.; Dumitru, C. D.; Myers, V.; Li, X.; Cheung, J. Y.; Feldman, A. M. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, E1120
    Google Scholar
    There is no corresponding record for this reference.
  99. 99
    Lin, K.; Lin, J.; Wu, W. I.; Ballard, J.; Lee, B. B.; Gloor, S. L.; Vigers, G. P. A.; Morales, T. H.; Friedman, L. S.; Skelton, N.; Brandhuber, B. J. Sci. Signaling 2012, 5, ra37
    Google Scholar
    99
    An ATP-site on-off switch that restricts phosphatase accessibility of Akt
    Lin Kui; Lin Jie; Wu Wen-I; Ballard Joshua; Lee Brian B; Gloor Susan L; Vigers Guy P A; Morales Tony H; Friedman Lori S; Skelton Nicholas; Brandhuber Barbara J
    Science signaling (2012), 5 (223), ra37 ISSN:.
    The protein serine-threonine kinase Akt undergoes a substantial conformational change upon activation, which is induced by the phosphorylation of two critical regulatory residues, threonine 308 and serine 473. Paradoxically, treating cells with adenosine 5'-triphosphate (ATP)-competitive inhibitors of Akt results in increased phosphorylation of both residues. We show that binding of ATP-competitive inhibitors stabilized a conformation in which both phosphorylated sites were inaccessible to phosphatases. ATP binding also produced this protection of the phosphorylated sites, whereas interaction with its hydrolysis product adenosine 5'-diphosphate (ADP) or allosteric Akt inhibitors resulted in increased accessibility of these phosphorylated residues. ATP-competitive inhibitors mimicked ATP by targeting active Akt. Forms of Akt activated by an oncogenic mutation or myristoylation were more potently inhibited by the ATP-competitive inhibitors than was wild-type Akt. These data support a new model of kinase regulation, wherein nucleotides modulate an on-off switch in Akt through conformational changes, which is disrupted by ATP-competitive inhibitors.
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  100. 100
    Okuzumi, T.; Fiedler, D.; Zhang, C.; Gray, D. C.; Aizenstein, B.; Hoffman, R.; Shokat, K. M. Nat. Chem. Biol. 2009, 5, 484
    Google Scholar
    100
    Inhibitor hijacking of Akt activation
    Okuzumi, Tatsuya; Fiedler, Dorothea; Zhang, Chao; Gray, Daniel C.; Aizenstein, Brian; Hoffman, Randy; Shokat, Kevan M.
    Nature Chemical Biology (2009), 5 (7), 484-493CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
    The kinase Akt plays a central role as a regulator of multiple growth factor input signals, thus making it an attractive anticancer drug target. A-443654 is an ATP-competitive Akt inhibitor. Unexpectedly, treatment of cells with A-443654 causes paradoxical hyperphosphorylation of Akt at its two regulatory sites (Thr308 and Ser473). We explored whether inhibitor-induced hyperphosphorylation of Akt by A-443654 is a consequence of disrupted feedback regulation at a pathway level or whether it is a direct consequence of inhibitor binding to the ATP binding site of Akt. Catalytically inactive mutants of Akt revealed that binding of an inhibitor to the ATP site of Akt is sufficient to directly cause hyperphosphorylation of the kinase in the absence of any pathway feedback effects. We conclude that ATP-competitive Akt inhibitors impart regulatory phosphorylation of their target kinase Akt. These results provide new insights into both natural regulation of Akt activation and Akt inhibitors entering the clinic.
    >> More from SciFinder ®
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  101. 101
    Zhuravleva, A.; Clerico, E. M.; Gierasch, L. M. Cell 2012, 151, 1296
    Google Scholar
    There is no corresponding record for this reference.
  102. 102
    Blackmore, N. J.; Reichau, S.; Jiao, W.; Hutton, R. D.; Baker, E. N.; Jameson, G. B.; Parker, E. J. J. Mol. Biol. 2013, 425, 1582
    Google Scholar
    There is no corresponding record for this reference.
  103. 103
    Lemmon, M. A.; Schlessinger, J. Cell 2010, 141, 1117
    Google Scholar
    103
    Cell signaling by receptor tyrosine kinases
    Lemmon, Mark A.; Schlessinger, Joseph
    Cell (Cambridge, MA, United States) (2010), 141 (7), 1117-1134CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
    A review. Recent structural studies of receptor tyrosine kinases (RTKs) have revealed unexpected diversity in the mechanisms of their activation by growth factor ligands. Strategies for inducing dimerization by ligand binding are surprisingly diverse, as are mechanisms that couple this event to activation of the intracellular tyrosine kinase domains. As the understanding of these details becomes increasingly sophisticated, it provides an important context for therapeutically countering the effects of pathogenic RTK mutations in cancer and other diseases. Much remains to be learned, however, about the complex signaling networks downstream from RTKs and how alterations in these networks are translated into cellular responses.
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  104. 104
    Gantke, T.; Sriskantharajah, S.; Sadowski, M.; Ley, S. C. Immunol. Rev. 2012, 246, 168
    Google Scholar
    104
    IκB kinase regulation of the TPL-2/ERK MAPK pathway
    Gantke, Thorsten; Sriskantharajah, Srividya; Sadowski, Michael; Ley, Steven C.
    Immunological Reviews (2012), 246 (1), 168-182, 15 pp.CODEN: IMRED2; ISSN:1600-065X. (Wiley-Blackwell)
    A review. Nuclear factor-κB (NF-κB) and mitogen-activated protein kinase (MAPK) activation play central roles in the induction of gene expression in innate immune cells following pathogen recognition. TPL-2 (tumor progression locus 2) is the MAP 3-kinase component of an ERK-1/2 (extracellular signal-regulated kinase 1/2) MAPK pathway activated by Toll-like receptor and tumor necrosis factor receptor family stimulation. In this review, we discuss results obtained from our lab. and others that show that TPL-2 signaling function is directly controlled by the inhibitor of NF-κB (IκB) kinase (IKK) complex. Significantly, this means that IKK controls both NF-κB and ERK activation. TPL-2 is stoichiometrically complexed with the NF-κB inhibitory protein, NF-κB1 p105, and the ubiquitin-binding protein ABIN-2, both of which are required to maintain TPL-2 protein stability. Binding to p105 also prevents TPL-2 from phosphorylating MEK (MAPK/ERK kinase), its downstream target. Agonist stimulation releases TPL-2 from p105-inhibition by IKK-mediated phosphorylation of p105, which triggers degrdn. of p105 by the proteasome. This facilitates TPL-2 phosphorylation of MEK, in addn. to liberating p105-assocd. Rel subunits to translocate into the nucleus. We also examine evidence that TPL-2 is crit. for the induction of inflammation and may play a role in development and/or progression of certain types of cancer. Finally, we consider the potential of TPL-2 as an anti-inflammatory drug target for treatment of certain types of inflammatory disease and cancer.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtVyku73F&md5=23185e836a6c7579a48c8c4e62448d8b
  105. 105
    Roberts, P. J.; Der, C. J. Oncogene 2007, 26, 3291
    Google Scholar
    There is no corresponding record for this reference.
  106. 106
    Geetha, N.; Mihaly, J.; Stockenhuber, A.; Blasi, F.; Uhrin, P.; Binder, B. R.; Freissmuth, M.; Breuss, J. M. J. Biol. Chem. 2011, 286, 25663
    Google Scholar
    There is no corresponding record for this reference.
  107. 107
    Shaul, Y. D.; Gibor, G.; Plotnikov, A.; Seger, R. Genes Dev. 2009, 23, 1779
    Google Scholar
    There is no corresponding record for this reference.
  108. 108
    Lee, J.; Natarajan, M.; Nashine, V. C.; Socolich, M.; Vo, T.; Russ, W. P.; Benkovic, S. J.; Ranganathan, R. Science 2008, 322, 438
    Google Scholar
    There is no corresponding record for this reference.
  109. 109
    Siryaporn, A.; Perchuk, B. S.; Laub, M. T.; Goulian, M. Mol. Syst. Biol. 2010, 6, 452
    Google Scholar
    There is no corresponding record for this reference.
  110. 110
    Nussinov, R.; Jang, H.; Tsai, C. J. Oncotarget 2014, 5, 7285
    Google Scholar
    There is no corresponding record for this reference.
  111. 111
    Huang, D. T.; Hunt, H. W.; Zhuang, M.; Ohi, M. D.; Holton, J. M.; Schulman, B. A. Nature 2007, 445, 394
    Google Scholar
    There is no corresponding record for this reference.
  112. 112
    Schulman, B. A.; Harper, J. W. Nat. Rev. Mol. Cell Biol. 2009, 10, 319
    Google Scholar
    112
    Ubiquitin-like protein activation by E1 enzymes: the apex for downstream signaling pathways
    Schulman, Brenda A.; Harper, J. Wade
    Nature Reviews Molecular Cell Biology (2009), 10 (5), 319-331CODEN: NRMCBP; ISSN:1471-0072. (Nature Publishing Group)
    A review. The attachment of ubiquitin or ubiquitin-like proteins (known as UBLs) to their targets through multienzyme cascades is a central mechanism in modulating protein functions. This process is initiated by a family of mechanistically and structurally related E1 (or activating) enzymes. These activate UBLs through C-terminal adenylylation and thiol transfer, and coordinate the use of UBLs in specific downstream pathways by charging cognate E2 (or conjugating) enzymes, which then interact with the downstream ubiquitination machinery to coordinate the modification of the target. A broad understanding of how E1 enzymes activate UBLs and how they selectively coordinate UBLs with downstream function has come from enzymic, structural, and genetic studies.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXktFGrsL8%253D&md5=ac268f8312c97c2663ce89b54ea3d1ac
  113. 113
    Das, R.; Mariano, J.; Tsai, Y. C.; Kalathur, R. C.; Kostova, Z.; Li, J.; Tarasov, S. G.; McFeeters, R. L.; Altieri, A. S.; Ji, X.; Byrd, R. A.; Weissman, A. M. Mol. Cell 2009, 34, 674
    Google Scholar
    There is no corresponding record for this reference.
  114. 114
    Liu, J.; Nussinov, R. Crit. Rev. Biochem. Mol. Biol. 2013, 48, 89
    Google Scholar
    114
    The role of allostery in the ubiquitin-proteasome system
    Liu, Jin; Nussinov, Ruth
    Critical Reviews in Biochemistry and Molecular Biology (2013), 48 (2), 89-97CODEN: CRBBEJ; ISSN:1040-9238. (Informa Healthcare)
    A review. The ubiquitin-proteasome system (UPS) is involved in many cellular processes including protein degrdn. Degrdn. of a protein via this system involves two successive steps: ubiquitination and degrdn. Ubiquitination tags the target protein with ubiquitin-like proteins (UBLs), such as ubiquitin, small ubiquitin-like modifier (SUMO) and NEDD8, via a cascade involving three enzymes: activating enzyme E1, conjugating enzyme E2 and E3 ubiquitin ligases. The proteasomes recognize the UBL-tagged substrate proteins and degrade them. Accumulating evidence indicates that allostery is a central player in the regulation of ubiquitination, as well as deubiquitination and degrdn. Here, we provide an overview of the key mechanistic roles played by allostery in all steps of these processes, and highlight allosteric drugs targeting them. Throughout the review, we emphasize the crucial mechanistic role played by linkers in allosterically controlling the UPS action by biasing the sampling of the conformational space, which facilitate the catalytic reactions of the ubiquitination and degrdn. Finally, we propose that allostery may similarly play key roles in the regulation of mol. machines in the cell, and as such allosteric drugs can be expected to be increasingly exploited in therapeutic regimes.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXkslWnur8%253D&md5=1a731256740b9cf1369149da33518417
  115. 115
    Liu, J.; Nussinov, R. J. Biol. Chem. 2011, 286, 40934
    Google Scholar
    There is no corresponding record for this reference.
  116. 116
    Liu, J.; Nussinov, R. Biophys. J. 2010, 99, 736
    Google Scholar
    There is no corresponding record for this reference.
  117. 117
    Liu, J.; Nussinov, R. J. Mol. Biol. 2010, 396, 1508
    Google Scholar
    There is no corresponding record for this reference.
  118. 118
    Liu, J.; Nussinov, R. PLoS Comput. Biol. 2009, 5e1000527
    Google Scholar
    There is no corresponding record for this reference.
  119. 119
    Liu, J.; Nussinov, R. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 901
    Google Scholar
    There is no corresponding record for this reference.
  120. 120
    Amemiya, T.; Koike, R.; Kidera, A.; Ota, M. Nucleic Acids Res. 2012, 40, D554
    Google Scholar
    There is no corresponding record for this reference.
  121. 121
    Purohit, P.; Gupta, S.; Jadey, S.; Auerbach, A. Nat. Commun. 2013, 4, 2984
    Google Scholar
    121
    Functional anatomy of an allosteric protein
    Purohit Prasad; Gupta Shaweta; Jadey Snehal; Auerbach Anthony
    Nature communications (2013), 4 (), 2984 ISSN:.
    Synaptic receptors are allosteric proteins that switch on and off to regulate cell signalling. Here, we use single-channel electrophysiology to measure and map energy changes in the gating conformational change of a nicotinic acetylcholine receptor. Two separated regions in the α-subunits--the transmitter-binding sites and αM2-αM3 linkers in the membrane domain--have the highest Φ-values (change conformation the earliest), followed by the extracellular domain, most of the membrane domain and the gate. Large gating-energy changes occur at the transmitter-binding sites, α-subunit interfaces, the αM1 helix and the gate. We hypothesize that rearrangements of the linkers trigger the global allosteric transition, and that the hydrophobic gate unlocks in three steps. The mostly local character of side-chain energy changes and the similarly high Φ-values of separated domains, both with and without ligands, suggest that gating is not strictly a mechanical process initiated by the affinity change for the agonist.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2c3oslGntw%253D%253D&md5=c52f141701b5b4cfd87c158b290e6325
  122. 122
    Hansen, D. F.; Vallurupalli, P.; Kay, L. E. J. Am. Chem. Soc. 2009, 131, 12745
    Google Scholar
    There is no corresponding record for this reference.
  123. 123
    Clore, G. M. Molecular bioSystems 2008, 4, 1058
    Google Scholar
    There is no corresponding record for this reference.
  124. 124
    Kumar, G.; Frantom, P. A. Biochemistry 2014, 53, 4847
    Google Scholar
    There is no corresponding record for this reference.
  125. 125
    Zhu, L.; Anslyn, E. V. Angew. Chem. 2006, 45, 1190
    Google Scholar
    There is no corresponding record for this reference.
  126. 126
    Yoon, H. J.; Kuwabara, J.; Kim, J. H.; Mirkin, C. A. Science 2010, 330, 66
    Google Scholar
    There is no corresponding record for this reference.
  127. 127
    Simon, A. J.; Vallee-Belisle, A.; Ricci, F.; Watkins, H. M.; Plaxco, K. W. Angew. Chem. 2014, 53, 9471
    Google Scholar
    There is no corresponding record for this reference.
  128. 128
    Porchetta, A.; Vallee-Belisle, A.; Plaxco, K. W.; Ricci, F. J. Am. Chem. Soc. 2013, 135, 13238
    Google Scholar
    128
    Allosterically Tunable, DNA-Based Switches Triggered by Heavy Metals
    Porchetta, Alessandro; Vallee-Belisle, Alexis; Plaxco, Kevin W.; Ricci, Francesco
    Journal of the American Chemical Society (2013), 135 (36), 13238-13241CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
    Here the authors demonstrate the rational design of allosterically controllable, metal-ion-triggered mol. switches. Specifically, the authors designed DNA sequences that adopt two low energy conformations, one of which does not bind to the target ion and the other of which contains mismatch sites serving as specific recognition elements for mercury-(II) or silver-(I) ions. Both switches contain multiple metal binding sites and thus exhibit homotropic allosteric (cooperative) responses. As heterotropic allosteric effectors the authors employ single-stranded DNA sequences that either stabilize or destabilize the nonbinding state, enabling dynamic range tuning over several orders of magnitude. The ability to rationally introduce these effects into target-responsive switches could be of value in improving the functionality of DNA-based nanomachines.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlamu7nO&md5=a87b4e57bbcad92375ba265c8e6c68d0
  129. 129
    Porchetta, A.; Vallee-Belisle, A.; Plaxco, K. W.; Ricci, F. J. Am. Chem. Soc. 2012, 134, 20601
    Google Scholar
    129
    Using Distal-Site Mutations and Allosteric Inhibition To Tune, Extend, and Narrow the Useful Dynamic Range of Aptamer-Based Sensors
    Porchetta, Alessandro; Vallee-Belisle, Alexis; Plaxco, Kevin W.; Ricci, Francesco
    Journal of the American Chemical Society (2012), 134 (51), 20601-20604CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
    Here the authors demonstrate multiple, complementary approaches by which to tune, extend, or narrow the dynamic range of aptamer-based sensors. Specifically, the authors employ both distal-site mutations and allosteric control to tune the affinity and dynamic range of a fluorescent aptamer beacon. Allosteric control, achieved by using a set of easily designed oligonucleotide inhibitors that competes against the folding of the aptamer, allows rational fine-tuning of the affinity of the authors' model aptamer across 3 orders of magnitude of target concn. with greater precision than that achieved using mutational approaches. Using these methods, the authors generate sets of aptamers varying significantly in target affinity and then combine them to recreate several of the mechanisms employed by nature to narrow or broaden the dynamic range of biol. receptors. Such ability to finely control the affinity and dynamic range of aptamers may find many applications in synthetic biol., drug delivery, and targeted therapies, fields in which aptamers are of rapidly growing importance.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xhsl2mu7rN&md5=384cebb953f3e28aa5f534ecf0546a00
  130. 130
    Kityk, R.; Kopp, J.; Sinning, I.; Mayer, M. P. Mol. Cell 2012, 48, 863
    Google Scholar
    There is no corresponding record for this reference.
  131. 131
    Bertelsen, E. B.; Chang, L.; Gestwicki, J. E.; Zuiderweg, E. R. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 8471
    Google Scholar
    There is no corresponding record for this reference.
  132. 132
    Wilbanks, S. M.; McKay, D. B. J. Biol. Chem. 1995, 270, 2251
    Google Scholar
    There is no corresponding record for this reference.

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  12. Shaoyong Lu, Qiancheng Shen, Jian Zhang. Allosteric Methods and Their Applications: Facilitating the Discovery of Allosteric Drugs and the Investigation of Allosteric Mechanisms. Accounts of Chemical Research 2019, 52 (2) , 492-500. https://doi.org/10.1021/acs.accounts.8b00570
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  32. Miguel A Maria-Solano, Sun Choi. Dynamic allosteric networks drive adenosine A1 receptor activation and G-protein coupling. eLife 2023, 12 https://doi.org/10.7554/eLife.90773
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  35. Ashley N. Hersey, Valerie E. Kay, Sumin Lee, Matthew J. Realff, Corey J. Wilson. Engineering allosteric transcription factors guided by the LacI topology. Cell Systems 2023, 14 (8) , 645-655. https://doi.org/10.1016/j.cels.2023.04.008
  36. Miguel A. Maria-Solano, Sun Choi. Dynamic allosteric networks drive adenosine A1 receptor activation and G-protein coupling. 2023https://doi.org/10.7554/eLife.90773.1
  37. Ruth Nussinov, Mingzhen Zhang, Yonglan Liu, Hyunbum Jang. AlphaFold, allosteric, and orthosteric drug discovery: Ways forward. Drug Discovery Today 2023, 28 (6) , 103551. https://doi.org/10.1016/j.drudis.2023.103551
  38. Yibo Wang, Xiaohui Wang. Where to next in the evolution of in silico design of modulators targeting protein–protein interactions?. Expert Opinion on Drug Discovery 2023, 18 (5) , 491-493. https://doi.org/10.1080/17460441.2023.2198699
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  40. Juan Xie, Weilin Zhang, Xiaolei Zhu, Minghua Deng, Luhua Lai. Coevolution-based prediction of key allosteric residues for protein function regulation. eLife 2023, 12 https://doi.org/10.7554/eLife.81850
  41. Matteo Castelli, Kaushik Bhattacharya, Ernest Abboud, Stefano A. Serapian, Didier Picard, Giorgio Colombo. Phosphorylation of the Hsp90 Co-Chaperone Hop Changes its Conformational Dynamics and Biological Function. Journal of Molecular Biology 2023, 435 (3) , 167931. https://doi.org/10.1016/j.jmb.2022.167931
  42. Kavita Kumari Kakarala, Kaiser Jamil. Identification of novel allosteric binding sites and multi-targeted allosteric inhibitors of receptor and non-receptor tyrosine kinases using a computational approach. Journal of Biomolecular Structure and Dynamics 2022, 40 (15) , 6889-6909. https://doi.org/10.1080/07391102.2021.1891140
  43. Alexey V. Melkikh. Viruses, immunity and evolution. Biosystems 2022, 220 , 104761. https://doi.org/10.1016/j.biosystems.2022.104761
  44. Jayanti Singh, Mohammad Sahil, Shamayeeta Ray, Criss Dcosta, Santosh Panjikar, G. Krishnamoorthy, Jagannath Mondal, Ruchi Anand. Phenol sensing in nature is modulated via a conformational switch governed by dynamic allostery. Journal of Biological Chemistry 2022, 298 (10) , 102399. https://doi.org/10.1016/j.jbc.2022.102399
  45. Karla Martinez Pomier, Madoka Akimoto, Jung Ah Byun, Mariia Khamina, Giuseppe Melacini. Allosteric regulation of cyclic nucleotide-dependent protein kinases. Canadian Journal of Chemistry 2022, 100 (9) , 649-659. https://doi.org/10.1139/cjc-2021-0359
  46. Igor N. Berezovsky, Ruth Nussinov. Multiscale Allostery: Basic Mechanisms and Versatility in Diagnostics and Drug Design. Journal of Molecular Biology 2022, 434 (17) , 167751. https://doi.org/10.1016/j.jmb.2022.167751
  47. Fangyun Bai, Kin Ming Puk, Jin Liu, Hongyu Zhou, Peng Tao, Wenyong Zhou, Shouyi Wang. Sparse group selection and analysis of function‐related residue for protein‐state recognition. Journal of Computational Chemistry 2022, 43 (20) , 1342-1354. https://doi.org/10.1002/jcc.26937
  48. Hebatallah Mohamed, Ubaidullah Baryar, Amir Bashiri, Rajeevan Selvaratnam, Bryan VanSchouwen, Giuseppe Melacini. Identification of core allosteric sites through temperature- and nucleus-invariant chemical shift covariance. Biophysical Journal 2022, 121 (11) , 2035-2045. https://doi.org/10.1016/j.bpj.2022.05.004
  49. Ruth Nussinov, Chung-Jung Tsai, Hyunbum Jang. Allostery, and how to define and measure signal transduction. Biophysical Chemistry 2022, 283 , 106766. https://doi.org/10.1016/j.bpc.2022.106766
  50. Hanna Baltrukevich, Sabina Podlewska. From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output. Frontiers in Pharmacology 2022, 13 https://doi.org/10.3389/fphar.2022.844293
  51. Ashfaq Ur Rehman, Shaoyong Lu, Abdul Aziz Khan, Beenish Khurshid, Salman Rasheed, Abdul Wadood, Jian Zhang. Hidden allosteric sites and De-Novo drug design. Expert Opinion on Drug Discovery 2022, 17 (3) , 283-295. https://doi.org/10.1080/17460441.2022.2017876
  52. György Abrusán, David B. Ascher, Michael Inouye, . Known allosteric proteins have central roles in genetic disease. PLOS Computational Biology 2022, 18 (2) , e1009806. https://doi.org/10.1371/journal.pcbi.1009806
  53. Ruth Nussinov, Chung-Jung Tsai, Hyunbum Jang. Signaling in the crowded cell. Current Opinion in Structural Biology 2021, 71 , 43-50. https://doi.org/10.1016/j.sbi.2021.05.009
  54. Youri Timsit, Sergeant-Perthuis Grégoire. Towards the Idea of Molecular Brains. International Journal of Molecular Sciences 2021, 22 (21) , 11868. https://doi.org/10.3390/ijms222111868
  55. David T.J. Morris, Steven M. Wales, David P. Tilly, Elliot H.E. Farrar, Matthew N. Grayson, John W. Ward, Jonathan Clayden. A molecular communication channel consisting of a single reversible chain of hydrogen bonds in a conformationally flexible oligomer. Chem 2021, 7 (9) , 2460-2472. https://doi.org/10.1016/j.chempr.2021.06.022
  56. Shanshan Wang, Zhe Huang, Anyang Li, Yanxia Zhao, Wei Zuo, Yawen Li, Haohao Miao, Jiacheng Ma, Wei Sun, Xiaoqing Wang, Liping Cao, Biao Wu, Chuandong Jia. Crown Ether Functionalized Potassium‐Responsive Anionocages for Cascaded Guest Delivery. Angewandte Chemie 2021, 133 (17) , 9659-9665. https://doi.org/10.1002/ange.202100441
  57. Shanshan Wang, Zhe Huang, Anyang Li, Yanxia Zhao, Wei Zuo, Yawen Li, Haohao Miao, Jiacheng Ma, Wei Sun, Xiaoqing Wang, Liping Cao, Biao Wu, Chuandong Jia. Crown Ether Functionalized Potassium‐Responsive Anionocages for Cascaded Guest Delivery. Angewandte Chemie International Edition 2021, 60 (17) , 9573-9579. https://doi.org/10.1002/anie.202100441
  58. Ying Wang, Zhengtian Yu, Wen Xiao, Shaoyong Lu, Jian Zhang. Allosteric binding sites at the receptor–lipid bilayer interface: novel targets for GPCR drug discovery. Drug Discovery Today 2021, 26 (3) , 690-703. https://doi.org/10.1016/j.drudis.2020.12.001
  59. Somjai Teanphonkrang, Wipa Suginta, Jeerus Sucharitakul, Tamo Fukamizo, Pimchai Chaiyen, Albert Schulte. An electrochemical method for detecting the biomarker 4-HPA by allosteric activation of Acinetobacter baumannii reductase C1 subunit. Journal of Biological Chemistry 2021, 296 , 100467. https://doi.org/10.1016/j.jbc.2021.100467
  60. Shuaiwei Qi, Chenyang Zhang, Tengfei Yan, Feihu Yang, Jing Zhang, Shizhong Mao, Zeyuan Dong. Hybrid Helical Polymer Nanochannels Constructed by Combining Aromatic Amide and Pyridine‐Oxadiazole Structural Sequences. Macromolecular Rapid Communications 2020, 41 (24) https://doi.org/10.1002/marc.202000099
  61. Kristina N. Woods, Jürgen Pfeffer. Conformational perturbation, allosteric modulation of cellular signaling pathways, and disease in P23H rhodopsin. Scientific Reports 2020, 10 (1) https://doi.org/10.1038/s41598-020-59583-2
  62. Sinu C. Rajappan, Dillon R. McCarthy, Joseph P. Campbell, Jonathon B. Ferrell, Mona Sharafi, Ona Ambrozaite, Jianing Li, Severin T. Schneebeli. Selective Monofunctionalization Enabled by Reaction‐History‐Dependent Communication in Catalytic Rotaxanes. Angewandte Chemie 2020, 132 (38) , 16811-16817. https://doi.org/10.1002/ange.202006305
  63. Sinu C. Rajappan, Dillon R. McCarthy, Joseph P. Campbell, Jonathon B. Ferrell, Mona Sharafi, Ona Ambrozaite, Jianing Li, Severin T. Schneebeli. Selective Monofunctionalization Enabled by Reaction‐History‐Dependent Communication in Catalytic Rotaxanes. Angewandte Chemie International Edition 2020, 59 (38) , 16668-16674. https://doi.org/10.1002/anie.202006305
  64. Jun Zhang, Gui-cen Liu, Xiao-lu Dai, Juan Wang, Mu-hua Jin, Nan-nan Mi, Shu-qin Wang. The N-terminus of MTRR plays a role in MTR reactivation cycle beyond electron transfer. Bioorganic Chemistry 2020, 100 , 103836. https://doi.org/10.1016/j.bioorg.2020.103836
  65. Qi Yan, Yaqi Wang, Jile Shi, Bryan Wei. Allostery of DNA nanostructures controlled by enzymatic modifications. Nucleic Acids Research 2020, 153 https://doi.org/10.1093/nar/gkaa488
  66. Tushar Modi, S. Banu Ozkan, Steve Pressé. Information propagation in time through allosteric signaling. Physical Review Research 2020, 2 (2) https://doi.org/10.1103/PhysRevResearch.2.023367
  67. Caio B. Castro, Rafael G. Silveira, Felippe M. Colombari, André Farias de Moura, Otaciro R. Nascimento, Caterina G. C. Marques Netto. Solvent Effect on the Regulation of Urea Hydrolysis Reactions by Copper Complexes. Chemistry 2020, 2 (2) , 525-544. https://doi.org/10.3390/chemistry2020032
  68. Paul Campitelli, S. Banu Ozkan. Allostery and Epistasis: Emergent Properties of Anisotropic Networks. Entropy 2020, 22 (6) , 667. https://doi.org/10.3390/e22060667
  69. Paul Campitelli, Tushar Modi, Sudhir Kumar, S. Banu Ozkan. The Role of Conformational Dynamics and Allostery in Modulating Protein Evolution. Annual Review of Biophysics 2020, 49 (1) , 267-288. https://doi.org/10.1146/annurev-biophys-052118-115517
  70. Stefano A. Serapian, Giorgio Colombo. Designing Molecular Spanners to Throw in the Protein Networks. Chemistry – A European Journal 2020, 26 (21) , 4656-4670. https://doi.org/10.1002/chem.201904523
  71. Carlos Sanchez-Martin, Elisabetta Moroni, Mariarosaria Ferraro, Claudio Laquatra, Giuseppe Cannino, Ionica Masgras, Alessandro Negro, Paolo Quadrelli, Andrea Rasola, Giorgio Colombo. Rational Design of Allosteric and Selective Inhibitors of the Molecular Chaperone TRAP1. Cell Reports 2020, 31 (3) , 107531. https://doi.org/10.1016/j.celrep.2020.107531
  72. Vivitri Dewi Prasasty, Sandra Cindana, Fransiskus Xaverius Ivan, Hilyatuz Zahroh, Ernawati Sinaga. Structure-based discovery of novel inhibitors of Mycobacterium tuberculosis CYP121 from Indonesian natural products. Computational Biology and Chemistry 2020, 85 , 107205. https://doi.org/10.1016/j.compbiolchem.2020.107205
  73. Shaoyong Lu, Yuran Qiu, Duan Ni, Xinheng He, Jun Pu, Jian Zhang. Emergence of allosteric drug-resistance mutations: new challenges for allosteric drug discovery. Drug Discovery Today 2020, 25 (1) , 177-184. https://doi.org/10.1016/j.drudis.2019.10.006
  74. Chan Yul Yoo, Noel Mano, Aliza Finkler, Hua Weng, Irene S. Day, Anireddy S. N. Reddy, B. W. Poovaiah, Hillel Fromm, Paul M. Hasegawa, Michael V. Mickelbart. A Ca2+/CaM-regulated transcriptional switch modulates stomatal development in response to water deficit. Scientific Reports 2019, 9 (1) https://doi.org/10.1038/s41598-019-47529-2
  75. Duan Ni, Shaoyong Lu, Jian Zhang. Emerging roles of allosteric modulators in the regulation of protein‐protein interactions (PPIs): A new paradigm for PPI drug discovery. Medicinal Research Reviews 2019, 39 (6) , 2314-2342. https://doi.org/10.1002/med.21585
  76. Xinyi Liu, Shaoyong Lu, Kun Song, Qiancheng Shen, Duan Ni, Qian Li, Xinheng He, Hao Zhang, Qi Wang, Yingyi Chen, Xinyi Li, Jing Wu, Chunquan Sheng, Guoqiang Chen, Yaqin Liu, Xuefeng Lu, Jian Zhang. Unraveling allosteric landscapes of allosterome with ASD. Nucleic Acids Research 2019, 10 https://doi.org/10.1093/nar/gkz958
  77. Lucinda K. Batchelor, Louis De Falco, Thibaud von Erlach, Deepti Sharma, Zenita Adhireksan, Ursula Roethlisberger, Curt A. Davey, Paul J. Dyson. Crosslinking Allosteric Sites on the Nucleosome. Angewandte Chemie 2019, 131 (44) , 15807-15811. https://doi.org/10.1002/ange.201906423
  78. Lucinda K. Batchelor, Louis De Falco, Thibaud von Erlach, Deepti Sharma, Zenita Adhireksan, Ursula Roethlisberger, Curt A. Davey, Paul J. Dyson. Crosslinking Allosteric Sites on the Nucleosome. Angewandte Chemie International Edition 2019, 58 (44) , 15660-15664. https://doi.org/10.1002/anie.201906423
  79. Chenyang Zhang, Xiaoli Deng, Chenxi Wang, Chunyan Bao, Bing Yang, Houyu Zhang, Shuaiwei Qi, Zeyuan Dong. Helical supramolecular polymer nanotubes with wide lumen for glucose transport: towards the development of functional membrane-spanning channels. Chemical Science 2019, 10 (37) , 8648-8653. https://doi.org/10.1039/C9SC02336D
  80. Pedram Mehrabi, Eike C. Schulz, Raison Dsouza, Henrike M. Müller-Werkmeister, Friedjof Tellkamp, R. J. Dwayne Miller, Emil F. Pai. Time-resolved crystallography reveals allosteric communication aligned with molecular breathing. Science 2019, 365 (6458) , 1167-1170. https://doi.org/10.1126/science.aaw9904
  81. William M. Marsiglia, Joseph Katigbak, Sijin Zheng, Moosa Mohammadi, Yingkai Zhang, Nathaniel J. Traaseth. A Conserved Allosteric Pathway in Tyrosine Kinase Regulation. Structure 2019, 27 (8) , 1308-1315.e3. https://doi.org/10.1016/j.str.2019.05.002
  82. Kun Song, Qian Li, Wei Gao, Shaoyong Lu, Qiancheng Shen, Xinyi Liu, Yongyan Wu, Binquan Wang, Houwen Lin, Guoqiang Chen, Jian Zhang. AlloDriver: a method for the identification and analysis of cancer driver targets. Nucleic Acids Research 2019, 47 (W1) , W315-W321. https://doi.org/10.1093/nar/gkz350
  83. Mayra A. Marques, Michelle S. Parvatiyar, Wei Yang, Guilherme A.P. de Oliveira, Jose R. Pinto. The missing links within troponin. Archives of Biochemistry and Biophysics 2019, 663 , 95-100. https://doi.org/10.1016/j.abb.2018.12.026
  84. Miao Yu, Yixin Chen, Zi-Le Wang, Zhirong Liu. Fluctuation correlations as major determinants of structure- and dynamics-driven allosteric effects. Physical Chemistry Chemical Physics 2019, 21 (9) , 5200-5214. https://doi.org/10.1039/C8CP07859A
  85. Ruth Nussinov, Chung-Jung Tsai, Amarda Shehu, Hyunbum Jang. Computational Structural Biology: Successes, Future Directions, and Challenges. Molecules 2019, 24 (3) , 637. https://doi.org/10.3390/molecules24030637
  86. Resmi C. Panicker, Souvik Chattopadhaya, Anthony G. Coyne, Rajavel Srinivasan. Allosteric Small-Molecule Serine/Threonine Kinase Inhibitors. 2019, 253-278. https://doi.org/10.1007/978-981-13-8719-7_11
  87. Weilin Zhang, Juan Xie, Luhua Lai. Correlation Between Allosteric and Orthosteric Sites. 2019, 89-105. https://doi.org/10.1007/978-981-13-8719-7_5
  88. Xinheng He, Duan Ni, Shaoyong Lu, Jian Zhang. Characteristics of Allosteric Proteins, Sites, and Modulators. 2019, 107-139. https://doi.org/10.1007/978-981-13-8719-7_6
  89. Giulia Morra, Giorgio Colombo. Understanding Allostery to Design New Drugs. 2018, 281-300. https://doi.org/10.1002/9783527806836.ch11
  90. Rebecca B. Berlow, H. Jane Dyson, Peter E. Wright. Expanding the Paradigm: Intrinsically Disordered Proteins and Allosteric Regulation. Journal of Molecular Biology 2018, 430 (16) , 2309-2320. https://doi.org/10.1016/j.jmb.2018.04.003
  91. Min Huang, Kun Song, Xinyi Liu, Shaoyong Lu, Qiancheng Shen, Renxiao Wang, Jingze Gao, Yuanyuan Hong, Qian Li, Duan Ni, Jianrong Xu, Guoqiang Chen, Jian Zhang. AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses. Nucleic Acids Research 2018, 46 (W1) , W451-W458. https://doi.org/10.1093/nar/gky374
  92. Silvia Rinaldi, Giorgio Colombo. Designing Chemical Tools with Computational Chemistry. 2017, 69-86. https://doi.org/10.1039/9781788010139-00069
  93. Antonella Paladino, Filippo Marchetti, Silvia Rinaldi, Giorgio Colombo. Protein design: from computer models to artificial intelligence. WIREs Computational Molecular Science 2017, 7 (5) https://doi.org/10.1002/wcms.1318
  94. Ko On Lee, Young‐Joo Yun, Iktae Kim, Jeong‐Yong Suh. Dimerization facilitates the conformational transitions for bacterial phosphotransferase enzyme I autophosphorylation in an allosteric manner. FEBS Open Bio 2017, 7 (8) , 1208-1216. https://doi.org/10.1002/2211-5463.12260
  95. Amit Kumawat, Suman Chakrabarty. Hidden electrostatic basis of dynamic allostery in a PDZ domain. Proceedings of the National Academy of Sciences 2017, 114 (29) https://doi.org/10.1073/pnas.1705311114
  96. Shawn Johnstone, Jeffrey S. Albert. Pharmacological property optimization for allosteric ligands: A medicinal chemistry perspective. Bioorganic & Medicinal Chemistry Letters 2017, 27 (11) , 2239-2258. https://doi.org/10.1016/j.bmcl.2017.03.084
  97. Francis G. A. Lister, Bryden A. F. Le Bailly, Simon J. Webb, Jonathan Clayden. Ligand-modulated conformational switching in a fully synthetic membrane-bound receptor. Nature Chemistry 2017, 9 (5) , 420-425. https://doi.org/10.1038/nchem.2736
  98. Sunil Laxman. Conceptualizing Eukaryotic Metabolic Sensing and Signaling. Journal of the Indian Institute of Science 2017, 97 (1) , 59-77. https://doi.org/10.1007/s41745-016-0013-1
  99. Melis Onel, Fidan Sumbul, Jin Liu, Ruth Nussinov, Turkan Haliloglu. Cullin neddylation may allosterically tune polyubiquitin chain length and topology. Biochemical Journal 2017, 474 (5) , 781-795. https://doi.org/10.1042/BCJ20160748
  100. Jiang Shan Zhan, Kai Gao, Rui Chao Chai, Xi Hua Jia, Dao Peng Luo, Guo Ge, Yu Wu Jiang, Yin-wan Wendy Fung, Lina Li, Albert Cheung Hoi Yu. Astrocytes in Migration. Neurochemical Research 2017, 42 (1) , 272-282. https://doi.org/10.1007/s11064-016-2089-4
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  • Abstract

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    Figure 1

    Figure 1. Extent of population shift, rather than binding affinity, determines allosteric efficacy. Here we use the distinct mutations of E3 ubiquitin ligase as an example. The population of the protein conformations is dominated by either the active form or the inactive form of the E3:E2∼Ub complex. The relative population of the inactive state (I state) and active state (A* state) depends on the relative energy of the two states. The free energy landscape is defined as ΔG = G(A*) – G(I). For the wild type (green curve), the dominant population is the active conformational state, allosterically facilitating the ubiquitin transfer process. Both destabilizing inactive-state mutants (blue curve) and stabilizing active-state mutants (red curve) can enhance the allosteric activity, here illustrated from the standpoint of population shift. The extent of enhancement is expressed by the free energy change due to the mutations, ΔΔGWT→M. The destabilizing inactive-state mutants may destabilize the inactive state but stabilize the active state to shift population. The stabilizing active-state mutants stabilize both the inactive and active states, to different extents, resulting in a shift of the population. The binding affinity, or the mechanism of population shift, does not determine the allosteric efficacy. It is the extent of population shift, or the free energy change ΔΔG, that determines the allosteric efficacy.

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    Figure 2

    Figure 2. Key residue mutations may switch the population shift. The energy landscape diagram of the WT VHL protein is shown on the left. The key residue mutations (middle), such as VHL Y98N, elicit the allosteric event by shifting the free energy landscape. The rescue mutations (right), such as VHL G123F and D179N, may reverse the population shift to one similar to the WT.

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    Figure 3

    Figure 3. Schematic illustration of the definition of anchor and driver in hsp70 allosteric activation. (A) A structural comparison of Escherichia coli hsp70 DnaK in the ATP-bound form (left panel, orange) (PDB code: 4B9Q (130)) and ADP-bound form (right panel, green) (PDB code: 2KHO (131)). (B) Comparison of ATP and ADP at the catalytic center of hsp70. The left and right panels show the hsp70 catalytic center residues coordinated with the ATP structure (left, DnaK.ATP complex, PDB code: 4B9Q) and ADP structure (right, Hsc70.ADP.Pi complex, PDB code: 1HPM (132)). The middle panel is the superimposition of the left and right panels. The circled driver atoms (γ-phosphate) of ATP “pull” the Lys70 and Glu171 by more than 2 Å in the low-substrate-affinity conformation. The circled anchor atoms induced little conformational change in hsp70 residues in both states.

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    Figure 4

    Figure 4. Cell signaling pathways are allosterically elicited by the activated receptor tyrosine kinase (RTK). The illustration includes the two major signaling pathways, Ras-RAF-MEK-ERK and PI3K-AKT, for protein synthesis, cell proliferation, and cell survival, and the ubiquitin-proteasome pathway for allosteric regulation and signaling of protein degradation.

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    This article references 132 other publications.

    1. 1
      Tsai, C.-J.; Nussinov, R. PLoS Comput. Biol. 2014, 10e1003394
      1
      A unified view of "how allostery works"
      Tsai, Chung-Jung; Nussinov, Ruth
      PLoS Computational Biology (2014), 10 (2), e1003394/1-e1003394/12, 12 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
      The question of how allostery works was posed almost 50 years ago. Since then it has been the focus of much effort. This is for two reasons: first, the intellectual curiosity of basic science and the desire to understand fundamental phenomena, and second, its vast practical importance. Allostery is at play in all processes in the living cell, and increasingly in drug discovery. Many models have been successfully formulated, and are able to describe allostery even in the absence of a detailed structural mechanism. However, conceptual schemes designed to qual. explain allosteric mechanisms usually lack a quant. math. model, and are unable to link its thermodn. and structural foundations. This hampers insight into oncogenic mutations in cancer progression and biased agonists' actions. Here, we describe how allostery works from three different standpoints: thermodn., free energy landscape of population shift, and structure; all with exactly the same allosteric descriptors. This results in a unified view which not only clarifies the elusive allosteric mechanism but also provides structural grasp of agonist-mediated signaling pathways, and guides allosteric drug discovery. Of note, the unified view reasons that allosteric coupling (or communication) does not det. the allosteric efficacy; however, a communication channel is what makes potential binding sites allosteric.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXkvVCmtLo%253D&md5=3912bf89813eeed254b3e3e4e1937f99
    2. 2
      Gunasekaran, K.; Ma, B. Y.; Nussinov, R. Proteins: Struct., Funct. Bioinf. 2004, 57, 433
      There is no corresponding record for this reference.
    3. 3
      Nussinov, R.; Tsai, C. J.; Xin, F.; Radivojac, P. Trends Biochem. Sci. 2012, 37, 447
      There is no corresponding record for this reference.
    4. 4
      Gao, X.; Xie, X.; Pashkov, I.; Sawaya, M. R.; Laidman, J.; Zhang, W.; Cacho, R.; Yeates, T. O.; Tang, Y. Chem. Biol. 2009, 16, 1064
      4
      Directed Evolution and Structural Characterization of a Simvastatin Synthase
      Gao, Xue; Xie, Xinkai; Pashkov, Inna; Sawaya, Michael R.; Laidman, Janel; Zhang, Wenjun; Cacho, Ralph; Yeates, Todd O.; Tang, Yi
      Chemistry & Biology (Cambridge, MA, United States) (2009), 16 (10), 1064-1074CODEN: CBOLE2; ISSN:1074-5521. (Cell Press)
      Enzymes from natural product biosynthetic pathways are attractive candidates for creating tailored biocatalysts to produce semisynthetic pharmaceutical compds. LovD is an acyltransferase that converts the inactive monacolin J acid (MJA) into the cholesterol-lowering lovastatin. LovD can also synthesize the blockbuster drug simvastatin using MJA and a synthetic α-dimethylbutyryl thioester, albeit with suboptimal properties as a biocatalyst. Here we used directed evolution to improve the properties of LovD toward semisynthesis of simvastatin. Mutants with improved catalytic efficiency, soly., and thermal stability were obtained, with the best mutant displaying an ∼11-fold increase in an Escherichia coli-based biocatalytic platform. To understand the structural basis of LovD enzymol., seven X-ray crystal structures were detd., including the parent LovD, an improved mutant G5, and G5 cocrystd. with ligands. Comparisons between the structures reveal that beneficial mutations stabilize the structure of G5 in a more compact conformation that is favorable for catalysis.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtlCnt7fM&md5=1b9a7341c930be855884d27dc91100f1
    5. 5
      Jiménez-Osés, G.; Osuna, S.; Gao, X.; Sawaya, M. R.; Gilson, L.; Collier, S. J.; Huisman, G. W.; Yeates, T. O.; Tang, Y.; Houk, K. N. Nat. Chem. Biol. 2014, 10, 431
      5
      The role of distant mutations and allosteric regulation on LovD active site dynamics
      Jimenez-Oses, Gonzalo; Osuna, Silvia; Gao, Xue; Sawaya, Michael R.; Gilson, Lynne; Collier, Steven J.; Huisman, Gjalt W.; Yeates, Todd O.; Tang, Yi; Houk, K. N.
      Nature Chemical Biology (2014), 10 (6), 431-436CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
      Natural enzymes have evolved to perform their cellular functions under complex selective pressures, which often require their catalytic activities to be regulated by other proteins. We contrasted a natural enzyme, LovD, which acts on a protein-bound (LovF) acyl substrate, with a lab.-generated variant that was transformed by directed evolution to accept instead a small free acyl thioester and no longer requires the acyl carrier protein. The resulting 29-mutant variant is 1000-fold more efficient in the synthesis of the drug simvastatin than the wild-type LovD. This is to our knowledge the first nonpatent report of the enzyme currently used for the manuf. of simvastatin as well as the intermediate evolved variants. Crystal structures and microsecond-scale mol. dynamics simulations revealed the mechanism by which the lab.-generated mutations free LovD from dependence on protein-protein interactions. Mutations markedly altered conformational dynamics of the catalytic residues, obviating the need for allosteric modulation by the acyl carrier LovF.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXmtlaht7Y%253D&md5=235f9c2bc5fe1574523a375dfd518bfa
    6. 6
      Tzeng, S. R.; Kalodimos, C. G. Curr. Opin. Struct. Biol. 2011, 21, 62
      6
      Protein dynamics and allostery: An NMR view
      Tzeng, Shiou-Ru; Kalodimos, Charalampos G.
      Current Opinion in Structural Biology (2011), 21 (1), 62-67CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
      A review. Allostery, the process by which distant sites within a protein system are energetically coupled, is an efficient and ubiquitous mechanism for activity regulation. A purely mech. view of allostery invoking only structural changes has developed over the decades as the classical view of the phenomenon. However, a fast growing list of examples illustrate the intimate link between internal motions over a wide range of time scales and function in protein-ligand interactions. Proteins respond to perturbations by redistributing their motions and they use fluctuating conformational states for binding and conformational entropy as a carrier of allosteric energy to modulate assocn. with ligands. In several cases allosteric interactions proceed with minimal or no structural changes. We discuss emerging paradigms for the central role of protein dynamics in allostery.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhvFKhuro%253D&md5=7ee2b2686e0c6a8ca4d337a33fd322ce
    7. 7
      Zhuravlev, P. I.; Papoian, G. A. Q. Rev. Biophys. 2010, 43, 295
      7
      Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework
      Zhuravlev, Pavel I.; Papoian, Garegin A.
      Quarterly Reviews of Biophysics (2010), 43 (3), 295-332CODEN: QURBAW; ISSN:0033-5835. (Cambridge University Press)
      A review. Energy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein - the native landscape - is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase vol. is much smaller, allowing a protein to fully sample its native energy landscape on the biol. timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a topog. map' of the native landscape that can be used for subsequent anal. Several major approaches to representing this topog. map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode anal. can provide such variables in cases where functional motions are dictated by the mol.'s architecture. Principal component anal. is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long mol. dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, preexisting equil.' and induced fit', are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some exptl. evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXht1Onu77F&md5=69a48379dd351943c953e41a949681fa
    8. 8
      Kenakin, T. P. Trends Pharmacol. Sci. 2009, 30, 460
      There is no corresponding record for this reference.
    9. 9
      Reiter, E.; Ahn, S.; Shukla, A. K.; Lefkowitz, R. J. Annu. Rev. Pharmacol. Toxicol. 2012, 52, 179
      There is no corresponding record for this reference.
    10. 10
      del Sol, A.; Tsai, C. J.; Ma, B. Y.; Nussinov, R. Structure 2009, 17, 1042
      10
      The Origin of Allosteric Functional Modulation: Multiple Pre-existing Pathways
      del Sol, Antonio; Tsai, Chung-Jung; Ma, Buyong; Nussinov, Ruth
      Structure (Cambridge, MA, United States) (2009), 17 (8), 1042-1050CODEN: STRUE6; ISSN:0969-2126. (Cell Press)
      A review. Although allostery draws increasing attention, not much is known about allosteric mechanisms. Here we argue that in all proteins, allosteric signals transmit through multiple, pre-existing pathways; which pathways dominate depend on protein topologies, specific binding events, covalent modifications, and cellular (environmental) conditions. Further, perturbation events at any site on the protein surface (or in the interior) will not create new pathways but only shift the pre-existing ensemble of pathways. Drugs binding at different sites or mutational events in disease shift the ensemble toward the same conformations; however, the relative populations of the different states will change. Consequently the obsd. functional, conformational, and dynamic effects will be different. This is the origin of allosteric functional modulation in dynamic proteins: allostery does not necessarily need to invoke conformational rearrangements to control protein activity and pre-existing pathways are always defaulted to during allostery regardless of the stimulant and perturbation site in the protein.
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    11. 11
      Zocchi, G. Annu. Rev. Biophys. 2009, 38, 75
      There is no corresponding record for this reference.
    12. 12
      Whitley, M. J.; Lee, A. L. Curr. Protein & Pept. Sci. 2009, 10, 116
      12
      Frameworks for understanding long-range intra-protein communication
      Whitley, Matthew J.; Lee, Andrew L.
      Current Protein and Peptide Science (2009), 10 (2), 116-127CODEN: CPPSCM; ISSN:1389-2037. (Bentham Science Publishers Ltd.)
      A review. The phenomenon of intraprotein communication is fundamental to such processes as allostery and signaling, yet comparatively little is understood about its phys. origins despite notable progress in recent years. This review introduces contemporary but distinct frameworks for understanding intraprotein communication by presenting both the ideas behind them and a discussion of their successes and shortcomings. The first framework holds that intraprotein communication is accomplished by the sequential mech. linkage of residues spanning a gap between distal sites. According to the second framework, proteins are best viewed as ensembles of distinct structural microstates, the dynamical and thermodn. properties of which contribute to the exptl. observable macroscale properties. NMR spectroscopy is a powerful method for studying intraprotein communication, and the insights into both frameworks it provides are presented through a discussion of numerous examples from the literature. Distinct from mech. and thermodn. considerations of intraprotein communication are recently applied graph and network theoretic analyses. These computational methods reduce complex 3-dimensional protein architectures to simple maps comprised of nodes (residues) connected by edges (inter-residue interactions). Anal. of these graphs yields a characterization of the protein's topol. and network characteristics. These methods have shown proteins to be small world networks with moderately high local residue connectivities existing concurrently with a small but significant no. of long range connectivities. However, exptl. studies of the tantalizing idea that these putative long-range interaction pathways facilitate one or several macroscopic protein characteristics are unfortunately lacking at present. This review concludes by comparing and contrasting the presented frameworks and methodologies for studying intraprotein communication and suggests a manner in which they can be brought to bear simultaneously to further enhance the understanding of this important fundamental phenomenon.
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    13. 13
      Tsai, C. J.; Del Sol, A.; Nussinov, R. Mol. Biosyst. 2009, 5, 207
      13
      Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms
      Tsai, Chung-Jung; del Sol, Antonio; Nussinov, Ruth
      Molecular BioSystems (2009), 5 (3), 207-216CODEN: MBOIBW; ISSN:1742-206X. (Royal Society of Chemistry)
      Allostery has come of age; the no., breadth and functional roles of documented protein allostery cases are rising quickly. Since all dynamic proteins are potentially allosteric and allostery plays crucial roles in all cellular pathways, sorting and classifying allosteric mechanisms in proteins should be extremely useful in understanding and predicting how the signals are regulated and transmitted through the dynamic multi-mol. cellular organizations. Classification organizes the complex information thereby unraveling relationships and patterns in mol. activation and repression. In signaling, current classification schemes consider classes of mols. according to their functions; for example, epinephrine and norepinephrine secreted by the central nervous system are classified as neurotransmitters. Other schemes would account for epinephrine when secreted by the adrenal medulla to be hormone-like. Yet, such classifications account for the global function of the mol.; not for the mol. mechanism of how the signal transmission initiates and how it is transmitted. Here we provide a unified view of allostery and the first classification framework. We expect that a classification scheme would assist in comprehension of allosteric mechanisms, in prediction of signaling on the mol. level, in better comprehension of pathways and regulation of the complex signals, in translating them to the cascading events, and in allosteric drug design. We further provide a range of examples illustrating mechanisms in protein allostery and their classification from the cellular functional standpoint.
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    14. 14
      Goodey, N. M.; Benkovic, S. J. Nat. Chem. Biol. 2008, 4, 474
      14
      Allosteric regulation and catalysis emerge via a common route
      Goodey, Nina M.; Benkovic, Stephen J.
      Nature Chemical Biology (2008), 4 (8), 474-482CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
      A review. Allosteric regulation of protein function is a mechanism by which an event in one place of a protein structure causes an effect at another site, much like the behavior of a telecommunications network in which a collection of transmitters, receivers and transceivers communicate with each other across long distances. For example, ligand binding or an amino acid mutation at an allosteric site can alter enzymic activity or binding affinity in a distal region such as the active site or a second binding site. The mechanism of this site-to-site communication is of great interest, esp. since allosteric effects must be considered in drug design and protein engineering. In this review, conformational mobility as the common route between allosteric regulation and catalysis is discussed. We summarize recent exptl. data and the resulting insights into allostery within proteins, and we discuss the nature of future studies and the new applications that may result from increased understanding of this regulatory mechanism.
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    15. 15
      Cui, Q.; Karplus, M. Protein Sci. 2008, 17, 1295
      15
      Allostery and cooperativity revisited
      Cui, Qiang; Karplus, Martin
      Protein Science (2008), 17 (8), 1295-1307CODEN: PRCIEI; ISSN:0961-8368. (Cold Spring Harbor Laboratory Press)
      A review. Although phenomenol. models that account for cooperativity in allosteric systems date back to the early and mid-60's [e.g., the D. E. Koshland, G. Nemethy, and D. Filmer, 1966 (KNF) and J. Momod, J. Wyman, and J. P. Changeux, 1965 (MWC) models], there is resurgent interest in the topic due to the recent exptl. and computational studies that attempted to reveal, at an atomistic level, how allostery actually works. Here, using systems for which atomistic simulations have been carried out in their groups as examples, the authors describe the current understanding of allostery and how the mechanisms go beyond the classical MWC/Pauling-KNF descriptions, and point out that the "new view" of allostery, emphasizing "population shifts," is, in fact, an "old view.". The presentation offers not only an up-to-date description of allostery from a theor./computational perspective, but also helps to resolve several outstanding issues concerning allostery.
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    16. 16
      Leitner, D. M. Annu. Rev. Phys. Chem. 2008, 59, 233
      There is no corresponding record for this reference.
    17. 17
      Fuxreiter, M.; Simon, I.; Bondos, S. Trends Biochem. Sci. 2011, 36, 415
      There is no corresponding record for this reference.
    18. 18
      Ma, B.; Tsai, C.-J.; Haliloğlu, T.; Nussinov, R. Structure 2011, 19, 907
      There is no corresponding record for this reference.
    19. 19
      Dixit, A.; Verkhivker, G. M. PLoS Comput. Biol. 2011, 7e1002179
      There is no corresponding record for this reference.
    20. 20
      Nussinov, R.; Ma, B.; Tsai, C.-J. Biophys. Chem. 2014, 186, 22
      There is no corresponding record for this reference.
    21. 21
      Nussinov, R.; Tsai, C.-J. Cell 2013, 153, 293
      There is no corresponding record for this reference.
    22. 22
      Nussinov, R.; Tsai, C.-J. Trends Pharmacol. Sci. 2014, 35, 256
      There is no corresponding record for this reference.
    23. 23
      Nussinov, R.; Tsai, C. J.; Ma, B. Annu. Rev. Biophys. 2013, 42, 169
      23
      The underappreciated role of allostery in the cellular network
      Nussinov, Ruth; Tsai, Chung-Jung; Ma, Buyong
      Annual Review of Biophysics (2013), 42 (), 169-189CODEN: ARBNCV; ISSN:1936-122X. (Annual Reviews)
      A review. Allosteric propagation results in communication between distinct sites in the protein structure; it also encodes specific effects on cellular pathways, and in this way it shapes cellular response. One example of long-range effects is binding of morphogens to cell surface receptors, which initiates a cascade of protein interactions that leads to genome activation and specific cellular action. Allosteric propagation results from combinations of multiple factors, takes place through dynamic shifts of conformational ensembles, and affects the equil. of macromol. interactions. Here, we (a) emphasize the well-known yet still underappreciated role of allostery in conveying explicit signals across large multimol. assemblies and distances to specify cellular action; (b) stress the need for quantitation of the allosteric effects; and finally, (c) propose that each specific combination of allosteric effectors along the pathway spells a distinct function. The challenges are colossal; the inspiring reward will be predicting function, misfunction, and outcomes of drug regimes.
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    24. 24
      Liu, J.; Nussinov, R. Crit. Rev. Biochem. Mol. Biol. 2013, 48, 89
      24
      The role of allostery in the ubiquitin-proteasome system
      Liu, Jin; Nussinov, Ruth
      Critical Reviews in Biochemistry and Molecular Biology (2013), 48 (2), 89-97CODEN: CRBBEJ; ISSN:1040-9238. (Informa Healthcare)
      A review. The ubiquitin-proteasome system (UPS) is involved in many cellular processes including protein degrdn. Degrdn. of a protein via this system involves two successive steps: ubiquitination and degrdn. Ubiquitination tags the target protein with ubiquitin-like proteins (UBLs), such as ubiquitin, small ubiquitin-like modifier (SUMO) and NEDD8, via a cascade involving three enzymes: activating enzyme E1, conjugating enzyme E2 and E3 ubiquitin ligases. The proteasomes recognize the UBL-tagged substrate proteins and degrade them. Accumulating evidence indicates that allostery is a central player in the regulation of ubiquitination, as well as deubiquitination and degrdn. Here, we provide an overview of the key mechanistic roles played by allostery in all steps of these processes, and highlight allosteric drugs targeting them. Throughout the review, we emphasize the crucial mechanistic role played by linkers in allosterically controlling the UPS action by biasing the sampling of the conformational space, which facilitate the catalytic reactions of the ubiquitination and degrdn. Finally, we propose that allostery may similarly play key roles in the regulation of mol. machines in the cell, and as such allosteric drugs can be expected to be increasingly exploited in therapeutic regimes.
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    25. 25
      Palazzesi, F.; Barducci, A.; Tollinger, M.; Parrinello, M. Proc. Natl. Acad. Sci. U.S.A. 2013, 110, 14237
      There is no corresponding record for this reference.
    26. 26
      Korkmaz, E. N.; Nussinov, R.; Haliloglu, T. PLoS Comput. Biol. 2012, 8e1002420
      There is no corresponding record for this reference.
    27. 27
      Nussinov, R.; Ma, B.; Tsai, C.-J.; Csermely, P. Structure 2013, 21, 1509
      There is no corresponding record for this reference.
    28. 28
      Tsai, C.-J.; Nussinov, R. Biochem. J. 2011, 439, 15
      There is no corresponding record for this reference.
    29. 29
      Laine, E.; Auclair, C.; Tchertanov, L. PLoS Comput. Biol. 2012, 8e1002661
      There is no corresponding record for this reference.
    30. 30
      Endres, N. F.; Engel, K.; Das, R.; Kovacs, E.; Kuriyan, J. Curr. Opin. Struct. Biol. 2011, 21, 777
      30
      Regulation of the catalytic activity of the EGF receptor
      Endres, Nicholas F.; Engel, Kate; Das, Rahul; Kovacs, Erika; Kuriyan, John
      Current Opinion in Structural Biology (2011), 21 (6), 777-784CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
      A review. The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase involved in cell growth that is often misregulated in cancer. Several recent studies highlight the unique structural mechanisms involved in its regulation. Some elucidate the important role that the juxtamembrane segment and the transmembrane helix play in stabilizing the activating asym. kinase dimer, and suggest that its activation mechanism is likely to be conserved among the other human EGFR-related receptors. Other studies provide new explanations for two long obsd., but poorly understood phenomena, the apparent heterogeneity in ligand binding and the formation of ligand-independent dimers. New insights into the allosteric mechanisms utilized by intracellular regulators of EGFR provide hope that allosteric sites could be used as targets for drug development.
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    31. 31
      Collavin, L.; Lunardi, A.; Del Sal, G. Cell Death Differ. 2010, 17, 901
      31
      p53-family proteins and their regulators: hubs and spokes in tumor suppression
      Collavin, L.; Lunardi, A.; Del Sal, G.
      Cell Death and Differentiation (2010), 17 (6), 901-911CODEN: CDDIEK; ISSN:1350-9047. (Nature Publishing Group)
      A review. The tumor suppressor p53 is a central hub in a mol. network controlling cell proliferation and death in response to potentially oncogenic conditions, and a wide array of covalent modifications and protein interactions modulate the nuclear and cytoplasmic activities of p53. The p53 relatives, p73 and p63, are entangled in the same regulatory network, being subject at least in part to the same modifications and interactions that convey signals on p53, and actively contributing to the resulting cellular output. The emerging picture is that of an interconnected pathway, in which all p53-family proteins are involved in the response to oncogenic stress and physiol. inputs. Therefore, common and specific interactors of p53-family proteins can have a wide effect on function and dysfunction of this pathway. Many years of research have uncovered an impressive no. of p53-interacting proteins, but much less is known about protein interactions of p63 and p73. Yet, many interactors may be shared by multiple p53-family proteins, with similar or different effects. In this study we review shared interactors of p53-family proteins with the aim to encourage research into this field; this knowledge promises to unveil regulatory elements that could be targeted by a new generation of mols., and allow more efficient use of currently available drugs for cancer treatment. Cell Death and Differentiation (2010) 17, 901-911; doi:10.1038/cdd.2010.35; published online 9 Apr. 2010.
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    32. 32
      Gronemeyer, H.; Bourguet, W. Sci. Signaling 2009, 2pe34
      32
      Allosteric effects govern nuclear receptor action: DNA appears as a player
      Gronemeyer Hinrich; Bourguet William
      Science signaling (2009), 2 (73), pe34 ISSN:.
      Nuclear receptors (NRs) are a family of transcription factors that regulate cognate gene networks, resulting in profound physiological and pathophysiological changes. Dysfunctional NR signaling leads to proliferative, reproductive, and metabolic diseases such as cancer, infertility, obesity, or diabetes. Indeed, NR-based pharmaceuticals are among the most commonly used drugs. NRs function by communicating with the intracellular and extracellular environment, thereby both sensing and modulating the status of cells. They respond to incoming signals by orchestrating transcriptional as well as nongenomic effects. They do so through an ability to respond to various effectors, such as the cognate ligand, by allosteric structural alterations that are the basis of further signal propagation. A mechanism has now been revealed by which DNA could act as an allosteric effector to modulate glucocorticoid receptor activity. This is a new regulatory paradigm for NR action that may help to explain how a receptor fine-tunes its target gene network.
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    33. 33
      Gupta, M.; Hendrickson, A. E. W.; Yun, S. S.; Han, J. J.; Schneider, P. A.; Koh, B. D.; Stenson, M. J.; Wellik, L. E.; Shing, J. C.; Peterson, K. L.; Flatten, K. S.; Hess, A. D.; Smith, B. D.; Karp, J. E.; Barr, S.; Witzig, T. E.; Kaufmann, S. H. Blood 2012, 119, 476
      There is no corresponding record for this reference.
    34. 34
      Johannessen, C. M.; Boehm, J. S.; Kim, S. Y.; Thomas, S. R.; Wardwell, L.; Johnson, L. A.; Emery, C. M.; Stransky, N.; Cogdill, A. P.; Barretina, J.; Caponigro, G.; Hieronymus, H.; Murray, R. R.; Salehi-Ashtiani, K.; Hill, D. E.; Vidal, M.; Zhao, J. J.; Yang, X. P.; Alkan, O.; Kim, S.; Harris, J. L.; Wilson, C. J.; Myer, V. E.; Finan, P. M.; Root, D. E.; Roberts, T. M.; Golub, T.; Flaherty, K. T.; Dummer, R.; Weber, B. L.; Sellers, W. R.; Schlegel, R.; Wargo, J. A.; Hahn, W. C.; Garraway, L. A. Nature 2010, 468, 968
      There is no corresponding record for this reference.
    35. 35
      Joseph, R. E.; Xie, Q. A.; Andreotti, A. H. J. Mol. Biol. 2010, 403, 231
      There is no corresponding record for this reference.
    36. 36
      Kar, G.; Keskin, O.; Gursoy, A.; Nussinov, R. Curr. Opin. Pharmacol. 2010, 10, 715
      36
      Allostery and population shift in drug discovery
      Kar, Gozde; Keskin, Ozlem; Gursoy, Attila; Nussinov, Ruth
      Current Opinion in Pharmacology (2010), 10 (6), 715-722CODEN: COPUBK; ISSN:1471-4892. (Elsevier Ltd.)
      A review. Proteins can exist in a large no. of conformations around their native states that can be characterized by an energy landscape. The landscape illustrates individual valleys, which are the conformational substates. From the functional standpoint, there are two key points: first, all functionally relevant substates pre-exist; and second, the landscape is dynamic and the relative populations of the substates will change following allosteric events. Allosteric events perturb the structure, and the energetic strain propagates and shifts the population. This can lead to changes in the shapes and properties of target binding sites. Here we present an overview of dynamic conformational ensembles focusing on allosteric events in signaling. We propose that combining equil. fluctuation concepts with genomic screens could help drug discovery.
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    37. 37
      Marti, M. A.; Estrin, D. A.; Roitberg, A. E. J. Phys. Chem. B 2009, 113, 2135
      There is no corresponding record for this reference.
    38. 38
      Martin-Montalvo, A.; Villalba, J. M.; Navas, P.; de Cabo, R. Oncogene 2011, 30, 505
      There is no corresponding record for this reference.
    39. 39
      Meijsing, S. H.; Pufall, M. A.; So, A. Y.; Bates, D. L.; Chen, L.; Yamamoto, K. R. Science 2009, 324, 407
      There is no corresponding record for this reference.
    40. 40
      Meyer, K. D.; Lin, S. C.; Bernecky, C.; Gao, Y. F.; Taatjes, D. J. Nat. Struct. Mol. Biol. 2010, 17, 753
      40
      p53 activates transcription by directing structural shifts in Mediator
      Meyer, Krista D.; Lin, Shih-chieh; Bernecky, Carrie; Gao, Yuefeng; Taatjes, Dylan J.
      Nature Structural & Molecular Biology (2010), 17 (6), 753-760CODEN: NSMBCU; ISSN:1545-9993. (Nature Publishing Group)
      It is not well understood how the human Mediator complex, transcription factor IIH and RNA polymerase II (Pol II) work together with activators to initiate transcription. Activator binding alters Mediator structure, yet the functional consequences of such structural shifts remain unknown. The p53 C terminus and its activation domain interact with different Mediator subunits, and we find that each interaction differentially affects Mediator structure; strikingly, distinct p53-Mediator structures differentially affect Pol II activity. Only the p53 activation domain induces the formation of a large pocket domain at the Mediator-Pol II interaction site, and this correlates with activation of stalled Pol II to a productively elongating state. Moreover, we define a Mediator requirement for TFIIH-dependent Pol II C-terminal domain phosphorylation and identify substantial differences in Pol II C-terminal domain processing that correspond to distinct p53-Mediator structural states. Our results define a fundamental mechanism by which p53 activates transcription and suggest that Mediator structural shifts trigger activation of stalled Pol II complexes.
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    41. 41
      Nussinov, R. Br. J. Pharmacol. 2012, 165, 2110
      41
      Allosteric modulators can restore function in an amino acid neurotransmitter receptor by slightly altering intra-molecular communication pathways
      Nussinov, Ruth
      British Journal of Pharmacology (2012), 165 (7), 2110-2112CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)
      A review. Mutations, even if not directly in the ligand binding sites of proteins, can lead to disease. In cell surface receptors, this can happen if they uncouple conformational changes that take place upon agonist (or antagonist) binding to the extracellular domain and the intracellular response. Uncoupling can take place by disrupting a major allosteric propagation pathway between the extra- and intracellular domains. The author provides a mechanistic explanation: first describing how propagation takes place; second, what can happen in the presence of a disease-related mutation which is distant from the binding site; and finally, how drugs may overcome this disruption and rescue function. The mutations in the glycine receptor α1 subunit (α1R271Q/L) which cause the neuromotor disorder hyperekplexia are on example of such allosteric mutations. In this issue of the BJP, Shan et al. show that normal function was restored to these mutant receptors by substitution of the segment which contained the mutated position, by a homologous one. An allosteric drug could mimic the effects of such substitution. Within this framework, the author highlights the advantages of allosteric drugs and the challenges in their design.
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    42. 42
      Nussinov, R.; Tsai, C.-J.; Csermely, P. Trends Pharmacol. Sci. 2011, 32, 686
      There is no corresponding record for this reference.
    43. 43
      Pfaff, S. J.; Fletterick, R. J. J. Biol. Chem. 2010, 285, 15256
      There is no corresponding record for this reference.
    44. 44
      Pan, Y. P.; Tsai, C. J.; Ma, B. Y.; Nussinov, R. Trends Genet. 2010, 26, 75
      44
      Mechanisms of transcription factor selectivity
      Pan, Yongping; Tsai, Chung-Jung; Ma, Buyong; Nussinov, Ruth
      Trends in Genetics (2010), 26 (2), 75-83CODEN: TRGEE2; ISSN:0168-9525. (Elsevier B.V.)
      A review. The initiation of transcription is regulated by transcription factors (TFs) binding to DNA response elements (REs). How do TFs recognize specific binding sites among the many similar ones available in the genome Recent research has illustrated that even a single nucleotide substitution can alter the selective binding of TFs to coregulators, that prior binding events can lead to selective DNA binding, and that selectivity is influenced by the availability of binding sites in the genome. Here, we combine structural insights with recent genomics screens to address the problem of TF-DNA interaction specificity. The emerging picture of selective binding site sequence recognition and TF activation involves three major factors: the cellular network, protein and DNA as dynamic conformational ensembles and the tight packing of multiple TFs and coregulators on stretches of regulatory DNA. The classification of TF recognition mechanisms based on these factors impacts our understanding of how transcription initiation is regulated.
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    45. 45
      Tsai, C. J.; del Sol, A.; Nussinov, R. J. Mol. Biol. 2008, 378, 1
      There is no corresponding record for this reference.
    46. 46
      Nussinov, R. Phys. Biol. 2013, 10045004
      There is no corresponding record for this reference.
    47. 47
      Nussinov, R.; Tsai, C.-J. Chem. Biol. 2014, 21, 311
      47
      Free Energy Diagrams for Protein Function
      Nussinov, Ruth; Tsai, Chung-Jung
      Chemistry & Biology (Oxford, United Kingdom) (2014), 21 (3), 311-318CODEN: CBOLE2; ISSN:1074-5521. (Elsevier Ltd.)
      A review. Simplified representations can be powerful. Two common examples are sequence logos and ribbon diagrams. Both have been extraordinarily successful in capturing complex static features of sequences and structures. Capturing function is challenging, since activation involves triggered dynamic shifts between ON and OFF states. Here, we show that simple funnel drawings can capture and usefully portray proteins by their cellular triggering mechanism. The funnel shape around the proteins' native states can describe mechanisms of upstream signal integration and downstream response. "Function diagrams" are important: they can combine diverse biochem. data to visually distinguish among activation (or recruitment) mechanisms and tag proteins in cellular networks, clarifying their mechanism at a glance. We create templates for function classification and suggest that they can extend signaling pathway maps. Of note, the diagrams describe free energy landscapes; thus, they can be quantified. We name our dynamic free-energy diagrams dFEDs.
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    48. 48
      Russell, R.; Zhuang, X. W.; Babcock, H. P.; Millett, I. S.; Doniach, S.; Chu, S.; Herschlag, D. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 155
      There is no corresponding record for this reference.
    49. 49
      Kim, S.; Brostromer, E.; Xing, D.; Jin, J. S.; Chong, S. S.; Ge, H.; Wang, S. Y.; Gu, C.; Yang, L. J.; Gao, Y. Q.; Su, X. D.; Sun, Y. J.; Xie, X. S. Science 2013, 339, 816
      There is no corresponding record for this reference.
    50. 50
      Piwonski, H. M.; Goomanovsky, M.; Bensimon, D.; Horovitz, A.; Haran, G. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, E1437
      There is no corresponding record for this reference.
    51. 51
      Perez, Y.; Maffei, M.; Igea, A.; Amata, I.; Gairi, M.; Nebreda, A. R.; Bernado, P.; Pons, M. Sci. Rep. 2013, 31295
      51
      Lipid binding by the Unique and SH3 domains of c-Src suggests a new regulatory mechanism
      Perez, Yolanda; Maffei, Mariano; Igea, Ana; Amata, Irene; Gairi, Margarida; Nebreda, Angel R.; Bernado, Pau; Pons, Miquel
      Scientific Reports (2013), 3 (), 1295, 9 pp.CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)
      C-Src is a non-receptor tyrosine kinase involved in numerous signal transduction pathways. The kinase, SH3 and SH2 domains of c-Src are attached to the membrane-anchoring SH4 domain through the flexible Unique domain. Here we show intra- and inter-mol. interactions involving the Unique and SH3 domains suggesting the presence of a previously unrecognized addnl. regulation layer in c-Src. We have characterized lipid binding by the Unique and SH3 domains, their intramol. interaction and its allosteric modulation by a SH3-binding peptide or by Calcium-loaded calmodulin binding to the Unique domain. We also show reduced lipid binding following phosphorylation at conserved sites of the Unique domain. Finally, we show that injection of full-length c-Src with mutations that abolish lipid binding by the Unique domain causes a strong in vivo phenotype distinct from that of wild-type c-Src in a Xenopus oocyte model system, confirming the functional role of the Unique domain in c-Src regulation.
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    52. 52
      Liu, W.; Chun, E.; Thompson, A. A.; Chubukov, P.; Xu, F.; Katritch, V.; Han, G. W.; Roth, C. B.; Heitman, L. H.; Ijzerman, A. P.; Cherezov, V.; Stevens, R. C. Science 2012, 337, 232
      52
      Structural Basis for Allosteric Regulation of GPCRs by Sodium Ions
      Liu, Wei; Chun, Eugene; Thompson, Aaron A.; Chubukov, Pavel; Xu, Fei; Katritch, Vsevolod; Han, Gye Won; Roth, Christopher B.; Heitman, Laura H.; IJzerman, Adriaan P.; Cherezov, Vadim; Stevens, Raymond C.
      Science (Washington, DC, United States) (2012), 337 (6091), 232-236CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Pharmacol. responses of G protein-coupled receptors (GPCRs) can be fine-tuned by allosteric modulators. Structural studies of such effects have been limited due to the medium resoln. of GPCR structures. We reengineered the human A2A adenosine receptor by replacing its third intracellular loop with apocytochrome b562RIL and solved the structure at 1.8 angstrom resoln. The high-resoln. structure allowed us to identify 57 ordered water mols. inside the receptor comprising three major clusters. The central cluster harbors a putative sodium ion bound to the highly conserved aspartate residue Asp2.50. Addnl., two cholesterols stabilize the conformation of helix VI, and one of 23 ordered lipids intercalates inside the ligand-binding pocket. These high-resoln. details shed light on the potential role of structured water mols., sodium ions, and lipids/cholesterol in GPCR stabilization and function.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XpvVKqtbs%253D&md5=782b2235b76ef77b11b6456f8e4485e1
    53. 53
      Tsai, C. J.; Kumar, S.; Ma, B. Y.; Nussinov, R. Protein Sci. 1999, 8, 1181
      53
      Folding funnels, binding funnels, and protein function
      Tsai, Chung-Jung; Kumar, Sandeep; Ma, Buyong; Nussinov, Ruth
      Protein Science (1999), 8 (6), 1181-1190CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
      A review with 66 refs. Folding funnels have been the focus of considerable attention during the last few years. These have mostly been discussed in the general context of the theory of protein folding. Here we extend the utility of the concept of folding funnels, relating them to biol. mechanisms and function. In particular, here we describe the shape of the funnels in light of protein synthesis and folding; flexibility, conformational diversity, and binding mechanisms; and the assocd. binding funnels, illustrating the multiple routes and the range of complexed conformers. Specifically, the walls of the folding funnels, their crevices, and bumps are related to the complexity of protein folding, and hence to sequential vs. non-sequential folding. Whereas the former is more frequently obsd. in eukaryotic proteins, where the rate of protein synthesis is slower, the latter is more frequent in prokaryotes, with faster translation rates. The bottoms of the funnels reflect the extent of the flexibility of the proteins. Rugged floors imply a range of conformational isomers, which may be close on the energy landscape. Rather than undergoing an induced fit binding mechanism, the conformational ensembles around the rugged bottoms argue that the conformers, which are most complementary to the ligand, will bind to it with the equil. shifting in their favor. Furthermore, depending on the extent of the ruggedness, or of the smoothness with only a few min., we may infer nonspecific, broad range vs. specific binding. In particular, folding and binding are similar processes, with similar underlying principles. Hence, the shape of the folding funnel of the monomer enables making reasonable guesses regarding the shape of the corresponding binding funnel. Proteins having a broad range of binding, such as proteolytic enzymes or relatively nonspecific endonucleases, may be expected to have not only rugged floors in their folding funnels, but their binding funnels will also behave similarly, with a range of complexed conformations. Hence, knowledge of the shape of the folding funnels is biol. very useful. The converse also holds: if kinetic and thermodn. data are available, hints regarding the role of the protein and its binding selectivity may be obtained. Thus, the utility of the concept of the funnel carries over to the origin of the protein and to its function.
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    54. 54
      Ma, B. Y.; Kumar, S.; Tsai, C. J.; Nussinov, R. Protein Eng. 1999, 12, 713
      54
      Folding funnels and binding mechanisms
      Ma, Buyong; Kumar, Sandeep; Tsai, Chung-Jung; Nussinov, Ruth
      Protein Engineering (1999), 12 (9), 713-720CODEN: PRENE9; ISSN:0269-2139. (Oxford University Press)
      The long-held views on lock-and-key vs. induced fit in binding arose from the notion that a protein exists in a single, most stable conformation, dictated by its sequence. However, in soln., proteins exist in a range of conformations, which may be described by statistical mech. laws, and their populations follow statistical distributions. Upon binding, the equil. will shift in favor of the bound conformation, from the ensemble of conformations around the bottom of the folding funnel. Hence, here we extend the implications and the usefulness of the folding funnel concept to explain fundamental binding mechanisms.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1MXmvVagsbY%253D&md5=8c229c85bad0fa20e90c459f90b4a760
    55. 55
      Tsai, C. J.; Ma, B. Y.; Nussinov, R. Proc. Natl. Acad. Sci. U.S.A. 1999, 96, 9970
      There is no corresponding record for this reference.
    56. 56
      Kumar, S.; Ma, B. Y.; Tsai, C. J.; Sinha, N.; Nussinov, R. Protein Sci. 2000, 9, 10
      56
      Folding and binding cascades: dynamic landscapes and population shifts
      Kumar, Sandeep; Ma, Buyong; Tsai, Chung-Jung; Sinha, Neeti; Nussinov, Ruth
      Protein Science (2000), 9 (1), 10-19CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
      A review, with ∼56 refs. Whereas previously we have successfully utilized the folding funnels concept to rationalize binding mechanisms and to describe binding, here we further extend the concept of folding funnels, illustrating its utility in explaining enzyme pathways, multimol. assocns., and allostery. This extension is based on the recognition that funnels are not stationary; rather, they are dynamic, depending on the phys. or binding conditions. Different binding states change the surrounding environment of proteins. The changed environment is in turn expressed in shifted energy landscapes, with different shapes and distributions of populations of conformers. Hence, the function of a protein and its properties are not only decided by the static folded three-dimensional structure; they are detd. by the distribution of its conformational substates, and in particular, by the redistributions of the populations under different environments. That is, protein function derives from its dynamic energy landscape, caused by changes in its surroundings.
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    57. 57
      Tsai, C. J.; Ma, B. Y.; Sham, Y. Y.; Kumar, S.; Nussinov, R. Proteins: Struct., Funct. Genet. 2001, 44, 418
      There is no corresponding record for this reference.
    58. 58
      Ma, B. Y.; Shatsky, M.; Wolfson, H. J.; Nussinov, R. Protein Sci. 2002, 11, 184
      58
      Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations
      Ma, Buyong; Shatsky, Maxim; Wolfson, Haim J.; Nussinov, Ruth
      Protein Science (2002), 11 (2), 184-197CODEN: PRCIEI; ISSN:0961-8368. (Cold Spring Harbor Laboratory Press)
      A review. Here, we comment on the steadily increasing body of data showing that proteins with specificity actually bind ligands of diverse shapes, sizes, and compn. Such a phenomenon is not surprising when one considers that binding is a dynamic process with populations in equil. and that the shape of the binding site is strongly influenced by the mol. partner. It derives implicity from the concept of populations. All proteins, specific and nonspecific, exist in ensembles of substates. If the library of ligands in soln. is large enough, favorably matching ligands with altered shapes and sizes can be expected to bind, with a redistribution of the protein populations. Point mutations at spatially distant sites may exert large conformational rearrangements and hinge effects, consistent with mutations away from the binding site leading to population shifts and (cross-)drug resistance. A similar effect is obsd. in protein superfamilies, in which different sequences with similar topologies display similar large-scale dynamic motions. The hinges are frequently at analogous sites, yet with different substrate specificity. Similar topologies yield similar conformational isomers, although with different distributions of population times, owing to the change in the conditions, i.e., the change in the sequences. In turn, different distributions relate to binding of different sizes and shapes. Hence, the binding site shape and size are defined by the ligand. They are not independent entities of fixed proportions and cannot be analyzed independently of the binding partner. Such a proposition derives from viewing proteins as dynamic distributions, presenting to the incoming ligands a range of binding site shapes. It illustrates how presumably specific binding mols. can bind multiple ligands. In terms of drug design, the ability of a single receptor to recognize many dissimilar ligands shows the need to consider more diverse mols. It provides a rationale for higher affinity inhibitors that are not derived from substrates at their transition states and indicates flexible docking schemes.
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    59. 59
      Boehr, D. D.; Nussinov, R.; Wright, P. E. Nat. Chem. Biol. 2009, 5, 789
      59
      The role of dynamic conformational ensembles in biomolecular recognition
      Boehr, David D.; Nussinov, Ruth; Wright, Peter E.
      Nature Chemical Biology (2009), 5 (11), 789-796CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
      A review. Mol. recognition by biomols. is central to all biol. processes. For the past 50 yr, the 'induced fit' hypothesis of D. E. Koshland (1958) has been the textbook explanation for mol. recognition events. However, recent exptl. evidence supports an alternative mechanism. The 'conformational selection' model postulates that all conformations pre-exist, and that the ligand selects the most favored conformation. Following binding, the ensemble undergoes a population shift, redistributing the conformational states. Both conformational selection and induced fit appear to play roles. Following binding by a primary conformational selection event, optimization of side-chain and backbone interactions is likely to proceed by an induced fit mechanism. Conformational selection has been obsd. for protein-ligand, protein-protein, protein-DNA, protein-RNA and RNA-ligand interactions. These data support a new mol. recognition paradigm for processes as diverse as signaling, catalysis, gene regulation, and protein aggregation in disease, which has the potential to significantly impact researchers' views and strategies in drug design, biomol. engineering, and mol. evolution.
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    60. 60
      Bosshard, H. R. News Physiol. Sci. 2001, 16, 171
      60
      Molecular recognition by induced fit: how fit is the concept?
      Bosshard, Hans Rudolf
      News in Physiological Sciences (2001), 16 (Aug.), 171-174CODEN: NEPSEY; ISSN:0886-1714. (International Union of Physiological Sciences)
      A review. Induced fit explains why biomols. can bind together even if they are not optimized for binding. However, induced fit can lead to a kinetic bottleneck and does not describe every interaction in the absence of prior complementarity. Preselection of a fitting conformer is an alternative to induced fit. Topics discussed include the expt. demonstration of binding by conformational selection and the energy landscape model of protein conformation.
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    61. 61
      Csermely, P.; Palotai, R.; Nussinov, R. Trends Biochem. Sci. 2010, 35, 539
      61
      Induced fit, conformational selection and independent dynamic segments: an extended view of binding events
      Csermely, Peter; Palotai, Robin; Nussinov, Ruth
      Trends in Biochemical Sciences (2010), 35 (10), 539-546CODEN: TBSCDB; ISSN:0968-0004. (Elsevier Ltd.)
      A review. Single mol. and NMR measurements of protein dynamics increasingly uncover the complexity of binding scenarios. Here, we describe an extended conformational selection model that embraces a repertoire of selection and adjustment processes. Induced fit can be viewed as a subset of this repertoire, whose contribution is affected by the bond types stabilizing the interaction and the differences between the interacting partners. We argue that protein segments whose dynamics are distinct from the rest of the protein (discrete breathers) can govern conformational transitions and allosteric propagation that accompany binding processes and, as such, might be more sensitive to mutational events. Addnl., we highlight the dynamic complexity of binding scenarios as they relate to events such as aggregation and signalling, and the crowded cellular environment.
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    62. 62
      Gardino, A. K.; Villali, J.; Kivenson, A.; Lei, M.; Liu, C. F.; Steindel, P.; Eisenmesser, E. Z.; Labeikovsky, W.; Wolf-Watz, M.; Clarkson, M. W.; Kern, D. Cell 2009, 139, 1109
      There is no corresponding record for this reference.
    63. 63
      Fraser, J. S.; Clarkson, M. W.; Degnan, S. C.; Erion, R.; Kern, D.; Alber, T. Nature 2009, 462, 669
      There is no corresponding record for this reference.
    64. 64
      Boehr, D. D. Cell 2009, 139, 1049
      There is no corresponding record for this reference.
    65. 65
      Korzhnev, D. M.; Kay, L. E. Acc. Chem. Res. 2008, 41, 442
      There is no corresponding record for this reference.
    66. 66
      Ziarek, J. J.; Getschman, A. E.; Butler, S. J.; Taleski, D.; Stephens, B.; Kufareva, I.; Handel, T. M.; Payne, R. J.; Volkman, B. F. ACS Chem. Biol. 2013, 8, 1955
      There is no corresponding record for this reference.
    67. 67
      Long, D.; Brueschweiler, R. J. Am. Chem. Soc. 2011, 133, 18999
      There is no corresponding record for this reference.
    68. 68
      Aykaç Fas, B.; Tutar, Y.; Haliloğlu, T. PLoS Comput. Biol. 2013, 9e1003141
      There is no corresponding record for this reference.
    69. 69
      Leff, P. Trends Pharmacol. Sci. 1995, 16, 89
      There is no corresponding record for this reference.
    70. 70
      Hall, D. A. Mol. Pharmacol. 2000, 58, 1412
      There is no corresponding record for this reference.
    71. 71
      Deupi, X.; Kobilka, B. K. Physiology 2010, 25, 293
      71
      Energy landscapes as a tool to integrate GPCR structure, dynamics, and function
      Deupi, Xavier; Kobilka, Brian K.
      Physiology (2010), 25 (Oct.), 293-303CODEN: PHYSCI; ISSN:1548-9213. (International Union of Physiological Sciences)
      A review. G protein-coupled receptors (GPCRs) are versatile signaling mols. that mediate the majority of physiol. responses to hormones and neurotransmitters. Recent: high-resoln. structural insight into GPCR structure and dynamics are beginning to shed light on the mol. basis of this versatility. We use energy landscapes to conceptualize the link between structure and function.
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    72. 72
      Starita, L. M.; Pruneda, J. N.; Lo, R. S.; Fowler, D. M.; Kim, H. J.; Hiatt, J. B.; Shendure, J.; Brzovic, P. S.; Fields, S.; Klevit, R. E. Proc. Natl. Acad. Sci. U.S.A. 2013, 110, E1263
      There is no corresponding record for this reference.
    73. 73
      Marlow, M. S.; Dogan, J.; Frederick, K. K.; Valentine, K. G.; Wand, A. J. Nat. Chem. Biol. 2010, 6, 352
      73
      The role of conformational entropy in molecular recognition by calmodulin
      Marlow, Michael S.; Dogan, Jakob; Frederick, Kendra K.; Valentine, Kathleen G.; Wand, A. Joshua
      Nature Chemical Biology (2010), 6 (5), 352-358CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
      The phys. basis for high-affinity interactions involving proteins is complex and potentially involves a range of energetic contributions. Among these are changes in protein conformational entropy, which cannot yet be reliably computed from mol. structures. We have recently used changes in conformational dynamics as a proxy for changes in conformational entropy of calmodulin upon assocn. with domains from regulated proteins. The apparent change in conformational entropy was linearly related to the overall binding entropy. This view warrants a more quant. foundation. Here we calibrate an 'entropy meter' using an exptl. dynamical proxy based on NMR relaxation and show that changes in the conformational entropy of calmodulin are a significant component of the energetics of binding. Furthermore, the distribution of motion at the interface between the target domain and calmodulin is surprisingly noncomplementary. These observations promote modification of our understanding of the energetics of protein-ligand interactions.
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    74. 74
      Wand, A. J. Curr. Opin. Struct. Biol. 2013, 23, 75
      74
      The dark energy of proteins comes to light: conformational entropy and its role in protein function revealed by NMR relaxation
      Wand, A. Joshua
      Current Opinion in Structural Biology (2013), 23 (1), 75-81CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
      A review. Historically it has been virtually impossible to exptl. det. the contribution of residual protein entropy to fundamental protein activities such as the binding of ligands. Recent progress has illuminated the possibility of employing NMR relaxation methods to quant. det. the role of changes in conformational entropy in mol. recognition by proteins. The method rests on using fast internal protein dynamics as a proxy. Initial results reveal a large and variable role for conformational entropy in the binding of ligands by proteins. Such a role for conformational entropy in mol. recognition has significant implications for enzymol., signal transduction, allosteric regulation and the development of protein-directed pharmaceuticals.
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    75. 75
      Kasinath, V.; Sharp, K. A.; Wand, A. J. J. Am. Chem. Soc. 2013, 135, 15092
      There is no corresponding record for this reference.
    76. 76
      Gerber, S. H.; Rah, J. C.; Min, S. W.; Liu, X.; de Wit, H.; Dulubova, I.; Meyer, A. C.; Rizo, J.; Arancillo, M.; Hammer, R. E.; Verhage, M.; Rosenmund, C.; Sudhof, T. C. Science 2008, 321, 1507
      There is no corresponding record for this reference.
    77. 77
      Manley, G.; Loria, J. P. Arch. Biochem. Biophys. 2012, 519, 223
      There is no corresponding record for this reference.
    78. 78
      Selvaratnam, R.; Chowdhury, S.; VanSchouwen, B.; Melacini, G. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 6133
      There is no corresponding record for this reference.
    79. 79
      Lockless, S. W.; Ranganathan, R. Science 1999, 286, 295
      There is no corresponding record for this reference.
    80. 80
      De Los Rios, P.; Cecconi, F.; Pretre, A.; Dietler, G.; Michielin, O.; Piazza, F.; Juanico, B. Biophys. J. 2005, 89, 14
      There is no corresponding record for this reference.
    81. 81
      Gerek, Z. N.; Keskin, O.; Ozkan, S. B. Proteins 2009, 77, 796
      There is no corresponding record for this reference.
    82. 82
      Gerek, Z. N.; Ozkan, S. B. PLoS Comput. Biol. 2011, 7e1002154
      There is no corresponding record for this reference.
    83. 83
      Ota, N.; Agard, D. A. J. Mol. Biol. 2005, 351, 345
      There is no corresponding record for this reference.
    84. 84
      Sharp, K.; Skinner, J. J. Proteins 2006, 65, 347
      There is no corresponding record for this reference.
    85. 85
      Kalescky, R.; Liu, J.; Tao, P. J. Phys. Chem. A. 2014,  DOI: 10.1021/jp5083455
      There is no corresponding record for this reference.
    86. 86
      Halabi, N.; Rivoire, O.; Leibler, S.; Ranganathan, R. Cell 2009, 138, 774
      There is no corresponding record for this reference.
    87. 87
      Reynolds, K. A.; McLaughlin, R. N.; Ranganathan, R. Cell 2011, 147, 1564
      There is no corresponding record for this reference.
    88. 88
      Hilser, V. J.; Thompson, E. B. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 8311
      There is no corresponding record for this reference.
    89. 89
      Wolynes, P. G.; Onuchic, J. N.; Thirumalai, D. Science 1995, 267, 1619
      89
      Navigating the folding routes
      Wolynes, Peter G.; Onuchic, Jose N.; Thirumalai, D.
      Science (Washington, D. C.) (1995), 267 (5204), 1619-20CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      A review and discussion with 11 refs. Computer models and conclusions from these models of protein folding are described.
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    90. 90
      Dill, K. A.; Chan, H. S. Nat. Struct. Biol. 1997, 4, 10
      There is no corresponding record for this reference.
    91. 91
      Palmer, A. G.; Kroenke, C. D.; Loria, J. P. Nucl. Magn. Reson. Biol. Macromol., B 2001, 339, 204
      91
      Nuclear magnetic resonance methods for quantifying microsecond-to-millisecond motions in biological macromolecules
      Palmer, Arthur G., III; Kroenke, Christopher D.; Loria, J. Patrick
      Methods in Enzymology (2001), 339 (Nuclear Magnetic Resonance of Biological Macromolecules, Part B), 204-238CODEN: MENZAU; ISSN:0076-6879. (Academic Press)
      A review. A review focuses on a subset of 13C and 15N heteronuclear ZZ-exchange, Carr-Purcell-Meiboom-Gill, and R[tρ] relaxation techniques that are sensitive to mol. motions or chem. kinetic processes on μs-ms time scales. The theor. and exptl. aspects of homonuclear and heteronuclear rotating frame relaxation are also discussed. (c) 2001 Academic Press.
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    92. 92
      Wan, P. T. C.; Garnett, M. J.; Roe, S. M.; Lee, S.; Niculescu-Duvaz, D.; Good, V. M.; Jones, C. M.; Marshall, C. J.; Springer, C. J.; Barford, D.; Marais, R.; Cancer Genome, P. Cell 2004, 116, 855
      There is no corresponding record for this reference.
    93. 93
      Reid, K. J.; Hendy, S. C.; Saito, J.; Sorensen, P.; Nelson, C. C. J. Biol. Chem. 2001, 276, 2943
      There is no corresponding record for this reference.
    94. 94
      Wood, J. R.; Greene, G. L.; Nardulli, A. M. Mol. Cell. Biol. 1998, 18, 1927
      94
      Estrogen response elements function as allosteric modulators of estrogen receptor conformation
      Wood, Jennifer R.; Greene, Geoffrey L.; Nardulli, Ann M.
      Molecular and Cellular Biology (1998), 18 (4), 1927-1934CODEN: MCEBD4; ISSN:0270-7306. (American Society for Microbiology)
      The estrogen receptor (ER) is a ligand-dependent transcription factor that regulates the expression of estrogen-responsive genes. ER-mediated transcriptional changes are brought about by interaction of the ER with the estrogen response element (ERE). In this study, the authors examd. the interaction of the Xenopus laevis ER DNA binding domain (DBD) and the intact ER with the X. laevis vitellogenin A2 ERE and the human pS2 ERE. Using gel mobility shift, DNase I footprinting, and methylation interference assays, the authors demonstrated that the DBD bound only as a dimer to the A2 ERE. However, the DBD bound as a monomer to the consensus pS2 ERE half site at lower DBD concns. and then as a homodimer to the consensus and imperfect pS2 ERE half site at higher DBD concns. Antibody supershift expts. carried out with partially purified, yeast expressed full-length ER demonstrated that three ER-specific antibodies interacted differentially with A2 and pS2 ERE-bound ER, indicating that receptor epitopes were differentially exposed. Furthermore, partial digestion of the A2 and pS2 ERE-bound ER with chymotrypsin or trypsin produced distinct protease cleavage patterns. Taken together, these data provide evidence that differential interaction of the DBD with the A2 and pS2 EREs brings about global changes in ER conformation. The conformational changes in ER induced by individual ERE sequences could lead to assocn. of the receptor with different transcription factors and assist in the differential modulation of estrogen-responsive genes in target cells.
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    95. 95
      Geserick, C.; Meyer, H. A.; Haendler, B. Mol. Cell. Endocrinol. 2005, 236, 1
      95
      The role of DNA response elements as allosteric modulators of steroid receptor function
      Geserick, Christoph; Meyer, Hellmuth-Alexander; Haendler, Bernard
      Molecular and Cellular Endocrinology (2005), 236 (1-2), 1-7CODEN: MCEND6; ISSN:0303-7207. (Elsevier Ltd.)
      A review. Steroid receptors are ligand-activated transcription factors which control the expression of their target genes by binding to specific DNA elements. Consensus response elements have been delineated for the glucocorticoid, androgen, progesterone and mineralocorticoid receptors on one hand (steroid response element, SRE) and for the estrogen receptor on the other hand (estrogen response element, ERE). Small variations in these sequences not only affect the binding but may also have a dramatic impact on the transcriptional activity of steroid receptors. It has now become obvious that DNA response elements do not merely tether regulatory proteins to control regions of target genes but may addnl. impart conformational changes onto the DNA-binding domain as well as to neighboring domains of steroid receptors. This in turn will create unique platforms for selective recruitment of cofactors and possibly for induction of modifications in local chromatin architecture. An addnl. level of complexity is added by the frequent presence of multiple response elements in gene promoter regions. The allosteric effects of DNA response elements on steroid receptors may be essential for differential gene expression and this offers interesting perspectives for the identification of selective modulators.
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    96. 96
      Nagy, L.; Schwabe, J. W. R. Trends Biochem. Sci. 2004, 29, 317
      There is no corresponding record for this reference.
    97. 97
      Whittle, C. M.; Lazakovitch, E.; Gronostajski, R. M.; Lieb, J. D. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 12049
      There is no corresponding record for this reference.
    98. 98
      Chan, T. O.; Zhang, J.; Rodeck, U.; Pascal, J. M.; Armen, R. S.; Spring, M.; Dumitru, C. D.; Myers, V.; Li, X.; Cheung, J. Y.; Feldman, A. M. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, E1120
      There is no corresponding record for this reference.
    99. 99
      Lin, K.; Lin, J.; Wu, W. I.; Ballard, J.; Lee, B. B.; Gloor, S. L.; Vigers, G. P. A.; Morales, T. H.; Friedman, L. S.; Skelton, N.; Brandhuber, B. J. Sci. Signaling 2012, 5, ra37
      99
      An ATP-site on-off switch that restricts phosphatase accessibility of Akt
      Lin Kui; Lin Jie; Wu Wen-I; Ballard Joshua; Lee Brian B; Gloor Susan L; Vigers Guy P A; Morales Tony H; Friedman Lori S; Skelton Nicholas; Brandhuber Barbara J
      Science signaling (2012), 5 (223), ra37 ISSN:.
      The protein serine-threonine kinase Akt undergoes a substantial conformational change upon activation, which is induced by the phosphorylation of two critical regulatory residues, threonine 308 and serine 473. Paradoxically, treating cells with adenosine 5'-triphosphate (ATP)-competitive inhibitors of Akt results in increased phosphorylation of both residues. We show that binding of ATP-competitive inhibitors stabilized a conformation in which both phosphorylated sites were inaccessible to phosphatases. ATP binding also produced this protection of the phosphorylated sites, whereas interaction with its hydrolysis product adenosine 5'-diphosphate (ADP) or allosteric Akt inhibitors resulted in increased accessibility of these phosphorylated residues. ATP-competitive inhibitors mimicked ATP by targeting active Akt. Forms of Akt activated by an oncogenic mutation or myristoylation were more potently inhibited by the ATP-competitive inhibitors than was wild-type Akt. These data support a new model of kinase regulation, wherein nucleotides modulate an on-off switch in Akt through conformational changes, which is disrupted by ATP-competitive inhibitors.
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    100. 100
      Okuzumi, T.; Fiedler, D.; Zhang, C.; Gray, D. C.; Aizenstein, B.; Hoffman, R.; Shokat, K. M. Nat. Chem. Biol. 2009, 5, 484
      100
      Inhibitor hijacking of Akt activation
      Okuzumi, Tatsuya; Fiedler, Dorothea; Zhang, Chao; Gray, Daniel C.; Aizenstein, Brian; Hoffman, Randy; Shokat, Kevan M.
      Nature Chemical Biology (2009), 5 (7), 484-493CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
      The kinase Akt plays a central role as a regulator of multiple growth factor input signals, thus making it an attractive anticancer drug target. A-443654 is an ATP-competitive Akt inhibitor. Unexpectedly, treatment of cells with A-443654 causes paradoxical hyperphosphorylation of Akt at its two regulatory sites (Thr308 and Ser473). We explored whether inhibitor-induced hyperphosphorylation of Akt by A-443654 is a consequence of disrupted feedback regulation at a pathway level or whether it is a direct consequence of inhibitor binding to the ATP binding site of Akt. Catalytically inactive mutants of Akt revealed that binding of an inhibitor to the ATP site of Akt is sufficient to directly cause hyperphosphorylation of the kinase in the absence of any pathway feedback effects. We conclude that ATP-competitive Akt inhibitors impart regulatory phosphorylation of their target kinase Akt. These results provide new insights into both natural regulation of Akt activation and Akt inhibitors entering the clinic.
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    101. 101
      Zhuravleva, A.; Clerico, E. M.; Gierasch, L. M. Cell 2012, 151, 1296
      There is no corresponding record for this reference.
    102. 102
      Blackmore, N. J.; Reichau, S.; Jiao, W.; Hutton, R. D.; Baker, E. N.; Jameson, G. B.; Parker, E. J. J. Mol. Biol. 2013, 425, 1582
      There is no corresponding record for this reference.
    103. 103
      Lemmon, M. A.; Schlessinger, J. Cell 2010, 141, 1117
      103
      Cell signaling by receptor tyrosine kinases
      Lemmon, Mark A.; Schlessinger, Joseph
      Cell (Cambridge, MA, United States) (2010), 141 (7), 1117-1134CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
      A review. Recent structural studies of receptor tyrosine kinases (RTKs) have revealed unexpected diversity in the mechanisms of their activation by growth factor ligands. Strategies for inducing dimerization by ligand binding are surprisingly diverse, as are mechanisms that couple this event to activation of the intracellular tyrosine kinase domains. As the understanding of these details becomes increasingly sophisticated, it provides an important context for therapeutically countering the effects of pathogenic RTK mutations in cancer and other diseases. Much remains to be learned, however, about the complex signaling networks downstream from RTKs and how alterations in these networks are translated into cellular responses.
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    104. 104
      Gantke, T.; Sriskantharajah, S.; Sadowski, M.; Ley, S. C. Immunol. Rev. 2012, 246, 168
      104
      IκB kinase regulation of the TPL-2/ERK MAPK pathway
      Gantke, Thorsten; Sriskantharajah, Srividya; Sadowski, Michael; Ley, Steven C.
      Immunological Reviews (2012), 246 (1), 168-182, 15 pp.CODEN: IMRED2; ISSN:1600-065X. (Wiley-Blackwell)
      A review. Nuclear factor-κB (NF-κB) and mitogen-activated protein kinase (MAPK) activation play central roles in the induction of gene expression in innate immune cells following pathogen recognition. TPL-2 (tumor progression locus 2) is the MAP 3-kinase component of an ERK-1/2 (extracellular signal-regulated kinase 1/2) MAPK pathway activated by Toll-like receptor and tumor necrosis factor receptor family stimulation. In this review, we discuss results obtained from our lab. and others that show that TPL-2 signaling function is directly controlled by the inhibitor of NF-κB (IκB) kinase (IKK) complex. Significantly, this means that IKK controls both NF-κB and ERK activation. TPL-2 is stoichiometrically complexed with the NF-κB inhibitory protein, NF-κB1 p105, and the ubiquitin-binding protein ABIN-2, both of which are required to maintain TPL-2 protein stability. Binding to p105 also prevents TPL-2 from phosphorylating MEK (MAPK/ERK kinase), its downstream target. Agonist stimulation releases TPL-2 from p105-inhibition by IKK-mediated phosphorylation of p105, which triggers degrdn. of p105 by the proteasome. This facilitates TPL-2 phosphorylation of MEK, in addn. to liberating p105-assocd. Rel subunits to translocate into the nucleus. We also examine evidence that TPL-2 is crit. for the induction of inflammation and may play a role in development and/or progression of certain types of cancer. Finally, we consider the potential of TPL-2 as an anti-inflammatory drug target for treatment of certain types of inflammatory disease and cancer.
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    105. 105
      Roberts, P. J.; Der, C. J. Oncogene 2007, 26, 3291
      There is no corresponding record for this reference.
    106. 106
      Geetha, N.; Mihaly, J.; Stockenhuber, A.; Blasi, F.; Uhrin, P.; Binder, B. R.; Freissmuth, M.; Breuss, J. M. J. Biol. Chem. 2011, 286, 25663
      There is no corresponding record for this reference.
    107. 107
      Shaul, Y. D.; Gibor, G.; Plotnikov, A.; Seger, R. Genes Dev. 2009, 23, 1779
      There is no corresponding record for this reference.
    108. 108
      Lee, J.; Natarajan, M.; Nashine, V. C.; Socolich, M.; Vo, T.; Russ, W. P.; Benkovic, S. J.; Ranganathan, R. Science 2008, 322, 438
      There is no corresponding record for this reference.
    109. 109
      Siryaporn, A.; Perchuk, B. S.; Laub, M. T.; Goulian, M. Mol. Syst. Biol. 2010, 6, 452
      There is no corresponding record for this reference.
    110. 110
      Nussinov, R.; Jang, H.; Tsai, C. J. Oncotarget 2014, 5, 7285
      There is no corresponding record for this reference.
    111. 111
      Huang, D. T.; Hunt, H. W.; Zhuang, M.; Ohi, M. D.; Holton, J. M.; Schulman, B. A. Nature 2007, 445, 394
      There is no corresponding record for this reference.
    112. 112
      Schulman, B. A.; Harper, J. W. Nat. Rev. Mol. Cell Biol. 2009, 10, 319
      112
      Ubiquitin-like protein activation by E1 enzymes: the apex for downstream signaling pathways
      Schulman, Brenda A.; Harper, J. Wade
      Nature Reviews Molecular Cell Biology (2009), 10 (5), 319-331CODEN: NRMCBP; ISSN:1471-0072. (Nature Publishing Group)
      A review. The attachment of ubiquitin or ubiquitin-like proteins (known as UBLs) to their targets through multienzyme cascades is a central mechanism in modulating protein functions. This process is initiated by a family of mechanistically and structurally related E1 (or activating) enzymes. These activate UBLs through C-terminal adenylylation and thiol transfer, and coordinate the use of UBLs in specific downstream pathways by charging cognate E2 (or conjugating) enzymes, which then interact with the downstream ubiquitination machinery to coordinate the modification of the target. A broad understanding of how E1 enzymes activate UBLs and how they selectively coordinate UBLs with downstream function has come from enzymic, structural, and genetic studies.
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    113. 113
      Das, R.; Mariano, J.; Tsai, Y. C.; Kalathur, R. C.; Kostova, Z.; Li, J.; Tarasov, S. G.; McFeeters, R. L.; Altieri, A. S.; Ji, X.; Byrd, R. A.; Weissman, A. M. Mol. Cell 2009, 34, 674
      There is no corresponding record for this reference.
    114. 114
      Liu, J.; Nussinov, R. Crit. Rev. Biochem. Mol. Biol. 2013, 48, 89
      114
      The role of allostery in the ubiquitin-proteasome system
      Liu, Jin; Nussinov, Ruth
      Critical Reviews in Biochemistry and Molecular Biology (2013), 48 (2), 89-97CODEN: CRBBEJ; ISSN:1040-9238. (Informa Healthcare)
      A review. The ubiquitin-proteasome system (UPS) is involved in many cellular processes including protein degrdn. Degrdn. of a protein via this system involves two successive steps: ubiquitination and degrdn. Ubiquitination tags the target protein with ubiquitin-like proteins (UBLs), such as ubiquitin, small ubiquitin-like modifier (SUMO) and NEDD8, via a cascade involving three enzymes: activating enzyme E1, conjugating enzyme E2 and E3 ubiquitin ligases. The proteasomes recognize the UBL-tagged substrate proteins and degrade them. Accumulating evidence indicates that allostery is a central player in the regulation of ubiquitination, as well as deubiquitination and degrdn. Here, we provide an overview of the key mechanistic roles played by allostery in all steps of these processes, and highlight allosteric drugs targeting them. Throughout the review, we emphasize the crucial mechanistic role played by linkers in allosterically controlling the UPS action by biasing the sampling of the conformational space, which facilitate the catalytic reactions of the ubiquitination and degrdn. Finally, we propose that allostery may similarly play key roles in the regulation of mol. machines in the cell, and as such allosteric drugs can be expected to be increasingly exploited in therapeutic regimes.
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    115. 115
      Liu, J.; Nussinov, R. J. Biol. Chem. 2011, 286, 40934
      There is no corresponding record for this reference.
    116. 116
      Liu, J.; Nussinov, R. Biophys. J. 2010, 99, 736
      There is no corresponding record for this reference.
    117. 117
      Liu, J.; Nussinov, R. J. Mol. Biol. 2010, 396, 1508
      There is no corresponding record for this reference.
    118. 118
      Liu, J.; Nussinov, R. PLoS Comput. Biol. 2009, 5e1000527
      There is no corresponding record for this reference.
    119. 119
      Liu, J.; Nussinov, R. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 901
      There is no corresponding record for this reference.
    120. 120
      Amemiya, T.; Koike, R.; Kidera, A.; Ota, M. Nucleic Acids Res. 2012, 40, D554
      There is no corresponding record for this reference.
    121. 121
      Purohit, P.; Gupta, S.; Jadey, S.; Auerbach, A. Nat. Commun. 2013, 4, 2984
      121
      Functional anatomy of an allosteric protein
      Purohit Prasad; Gupta Shaweta; Jadey Snehal; Auerbach Anthony
      Nature communications (2013), 4 (), 2984 ISSN:.
      Synaptic receptors are allosteric proteins that switch on and off to regulate cell signalling. Here, we use single-channel electrophysiology to measure and map energy changes in the gating conformational change of a nicotinic acetylcholine receptor. Two separated regions in the α-subunits--the transmitter-binding sites and αM2-αM3 linkers in the membrane domain--have the highest Φ-values (change conformation the earliest), followed by the extracellular domain, most of the membrane domain and the gate. Large gating-energy changes occur at the transmitter-binding sites, α-subunit interfaces, the αM1 helix and the gate. We hypothesize that rearrangements of the linkers trigger the global allosteric transition, and that the hydrophobic gate unlocks in three steps. The mostly local character of side-chain energy changes and the similarly high Φ-values of separated domains, both with and without ligands, suggest that gating is not strictly a mechanical process initiated by the affinity change for the agonist.
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    122. 122
      Hansen, D. F.; Vallurupalli, P.; Kay, L. E. J. Am. Chem. Soc. 2009, 131, 12745
      There is no corresponding record for this reference.
    123. 123
      Clore, G. M. Molecular bioSystems 2008, 4, 1058
      There is no corresponding record for this reference.
    124. 124
      Kumar, G.; Frantom, P. A. Biochemistry 2014, 53, 4847
      There is no corresponding record for this reference.
    125. 125
      Zhu, L.; Anslyn, E. V. Angew. Chem. 2006, 45, 1190
      There is no corresponding record for this reference.
    126. 126
      Yoon, H. J.; Kuwabara, J.; Kim, J. H.; Mirkin, C. A. Science 2010, 330, 66
      There is no corresponding record for this reference.
    127. 127
      Simon, A. J.; Vallee-Belisle, A.; Ricci, F.; Watkins, H. M.; Plaxco, K. W. Angew. Chem. 2014, 53, 9471
      There is no corresponding record for this reference.
    128. 128
      Porchetta, A.; Vallee-Belisle, A.; Plaxco, K. W.; Ricci, F. J. Am. Chem. Soc. 2013, 135, 13238
      128
      Allosterically Tunable, DNA-Based Switches Triggered by Heavy Metals
      Porchetta, Alessandro; Vallee-Belisle, Alexis; Plaxco, Kevin W.; Ricci, Francesco
      Journal of the American Chemical Society (2013), 135 (36), 13238-13241CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
      Here the authors demonstrate the rational design of allosterically controllable, metal-ion-triggered mol. switches. Specifically, the authors designed DNA sequences that adopt two low energy conformations, one of which does not bind to the target ion and the other of which contains mismatch sites serving as specific recognition elements for mercury-(II) or silver-(I) ions. Both switches contain multiple metal binding sites and thus exhibit homotropic allosteric (cooperative) responses. As heterotropic allosteric effectors the authors employ single-stranded DNA sequences that either stabilize or destabilize the nonbinding state, enabling dynamic range tuning over several orders of magnitude. The ability to rationally introduce these effects into target-responsive switches could be of value in improving the functionality of DNA-based nanomachines.
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    129. 129
      Porchetta, A.; Vallee-Belisle, A.; Plaxco, K. W.; Ricci, F. J. Am. Chem. Soc. 2012, 134, 20601
      129
      Using Distal-Site Mutations and Allosteric Inhibition To Tune, Extend, and Narrow the Useful Dynamic Range of Aptamer-Based Sensors
      Porchetta, Alessandro; Vallee-Belisle, Alexis; Plaxco, Kevin W.; Ricci, Francesco
      Journal of the American Chemical Society (2012), 134 (51), 20601-20604CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
      Here the authors demonstrate multiple, complementary approaches by which to tune, extend, or narrow the dynamic range of aptamer-based sensors. Specifically, the authors employ both distal-site mutations and allosteric control to tune the affinity and dynamic range of a fluorescent aptamer beacon. Allosteric control, achieved by using a set of easily designed oligonucleotide inhibitors that competes against the folding of the aptamer, allows rational fine-tuning of the affinity of the authors' model aptamer across 3 orders of magnitude of target concn. with greater precision than that achieved using mutational approaches. Using these methods, the authors generate sets of aptamers varying significantly in target affinity and then combine them to recreate several of the mechanisms employed by nature to narrow or broaden the dynamic range of biol. receptors. Such ability to finely control the affinity and dynamic range of aptamers may find many applications in synthetic biol., drug delivery, and targeted therapies, fields in which aptamers are of rapidly growing importance.
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    130. 130
      Kityk, R.; Kopp, J.; Sinning, I.; Mayer, M. P. Mol. Cell 2012, 48, 863
      There is no corresponding record for this reference.
    131. 131
      Bertelsen, E. B.; Chang, L.; Gestwicki, J. E.; Zuiderweg, E. R. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 8471
      There is no corresponding record for this reference.
    132. 132
      Wilbanks, S. M.; McKay, D. B. J. Biol. Chem. 1995, 270, 2251
      There is no corresponding record for this reference.
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