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

ABSTRACT: 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 pK_a calculations on CPUs and GPUs. Here we benchmark the performance of GB-CpHMD for predictions of cysteine pK_a's and reactivities using a data set of 24 proteins with both down- and upshifted cysteine pK_a'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 pK_a tool that gives an accuracy less than 50%. We discuss simulation convergence, dependence on starting structures, common determinants of the pK_a 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.

SUBMITTER: Harris RC

PROVIDER: S-EPMC7772776 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Harris Robert C RC Liu Ruibin R Shen Jana J

Journal of chemical theory and computation 20200506 6

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 dy ...[more]

PMID: 32330035

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Project description:We present a GPU implementation of the continuous constant pH molecular dynamics (CpHMD) based on the most recent generalized Born implicit-solvent model in the pmemd engine of the Amber molecular dynamics package. To test the accuracy of the tool for rapid pKa predictions, a series of 2 ns single-pH simulations were performed for over 120 titratable residues in 10 benchmark proteins that were previously used to test the various continuous CpHMD methods. The calculated pKa's showed a root-mean-square deviation of 0.80 and correlation coefficient of 0.83 with respect to experiment. Also, 90% of the pKa's were converged with estimated errors below 0.1 pH units. Surprisingly, this level of accuracy is similar to our previous replica-exchange simulations with 2 ns per replica and an exchange attempt frequency of 2 ps-1 (Huang, Harris, and Shen J. Chem. Inf. Model. 2018 , 58 , 1372 - 1383 ). Interestingly, for the linked titration sites in two enzymes, although residue-specific protonation state sampling in the single-pH simulations was not converged within 2 ns, the protonation fraction of the linked residues appeared to be largely converged, and the experimental macroscopic pKa values were reproduced to within 1 pH unit. Comparison with replica-exchange simulations with different exchange attempt frequencies showed that the splitting between the two macroscopic pKa's is underestimated with frequent exchange attempts such as 2 ps-1, while single-pH simulations overestimate the splitting. The same trend is seen for the single-pH vs replica-exchange simulations of a hydrogen-bonded aspartyl dyad in a much larger protein. A 2 ns single-pH simulation of a 400-residue protein takes about 1 h on a single NVIDIA GeForce RTX 2080 graphics card, which is over 1000 times faster than a CpHMD run on a single CPU core of a high-performance computing cluster node. Thus, we envision that GPU-accelerated continuous CpHMD may be used in routine pKa predictions for a variety of applications, from assisting MD simulations with protonation state assignment to offering pH-dependent corrections of binding free energies and identifying reactive hot spots for covalent drug design.

Dataset Information

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

Publications

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

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