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Predicting Reactive Cysteines with Implicit-Solvent-Based Continuous Constant pH Molecular Dynamics in Amber

Cite this: J. Chem. Theory Comput. 2020, 16, 6, 3689–3698
Publication Date (Web):April 24, 2020
https://doi.org/10.1021/acs.jctc.0c00258
Copyright © 2020 American Chemical Society
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    Abstract

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    Cysteines existing in the deprotonated thiolate form or having a tendency to become deprotonated are important players in enzymatic and cellular redox functions and frequently exploited in covalent drug design; however, most computational studies assume cysteines as protonated. Thus, developing an efficient tool that can make accurate and reliable predictions of cysteine protonation states is timely needed. We recently implemented a generalized Born (GB) based continuous constant pH molecular dynamics (CpHMD) method in Amber for protein pKa calculations on CPUs and GPUs. Here we benchmark the performance of GB-CpHMD for predictions of cysteine pKa’s and reactivities using a data set of 24 proteins with both down- and upshifted cysteine pKa’s. We found that 10 ns single-pH or 4 ns replica-exchange CpHMD titrations gave root-mean-square errors of 1.2–1.3 and correlation coefficients of 0.8–0.9 with respect to experiment. The accuracy of predicting thiolates or reactive cysteines at physiological pH with single-pH titrations is 86 or 81% with a precision of 100 or 90%, respectively. This performance well surpasses the traditional structure-based methods, particularly a widely used empirical pKa tool that gives an accuracy less than 50%. We discuss simulation convergence, dependence on starting structures, common determinants of the pKa downshifts and upshifts, and the origin of the discrepancies from the structure-based calculations. Our work suggests that CpHMD titrations can be performed on a desktop computer equipped with a single GPU card to predict cysteine protonation states for a variety of applications, from understanding biological functions to covalent drug design.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.0c00258.

    • Table of prediction rates and figures of parameter derivation and validation for model Cys, ualculated pKa values vs simulation time, unprotonated fractions vs pH, and overall performance of titrations (PDF)

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