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Coupling Constant pH Molecular Dynamics with Accelerated Molecular Dynamics.

ABSTRACT: An extension of the constant pH method originally implemented by Mongan et al. (J. Comput. Chem.2004, 25, 2038-2048) is proposed in this study. This adapted version of the method couples the constant pH methodology with the enhanced sampling technique of accelerated molecular dynamics, in an attempt to overcome the sampling issues encountered with current standard constant pH molecular dynamics methods. Although good results were reported by Mongan et al. on application of the standard method to the hen egg-white lysozyme (HEWL) system, residues which possess strong interactions with neighboring groups tend to converge slowly, resulting in the reported inconsistencies for predicted pK(a) values, as highlighted by the authors. The application of the coupled method described in this study to the HEWL system displays improvements over the standard version of the method, with the improved sampling leading to faster convergence and producing pK(a) values in closer agreement to those obtained experimentally for the more slowly converging residues.

SUBMITTER: Williams SL

PROVIDER: S-EPMC2817915 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Coupling Constant pH Molecular Dynamics with Accelerated Molecular Dynamics.

Williams Sarah L SL de Oliveira César Augusto F CA McCammon J Andrew JA

Journal of chemical theory and computation 20100114 2

An extension of the constant pH method originally implemented by Mongan et al. (J. Comput. Chem.2004, 25, 2038-2048) is proposed in this study. This adapted version of the method couples the constant pH methodology with the enhanced sampling technique of accelerated molecular dynamics, in an attempt to overcome the sampling issues encountered with current standard constant pH molecular dynamics methods. Although good results were reported by Mongan et al. on application of the standard method to ...[more]

PMID: 20148176

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Project description:Constant pH molecular dynamics (MD) simulations sample protonation states on the fly according to the conformational environment and user specified pH conditions; however, the current accuracy is limited due to the use of implicit-solvent models or a hybrid solvent scheme. Here, we report the first GPU-accelerated implementation, parametrization, and validation of the all-atom continuous constant pH MD (CpHMD) method with particle-mesh Ewald (PME) electrostatics in the Amber22 pmemd.cuda engine. The titration parameters for Asp, Glu, His, Cys, and Lys were derived for the CHARMM c22 and Amber ff14sb and ff19sb force fields. We then evaluated the PME-CpHMD method using the asynchronous pH replica-exchange titration simulations with the c22 force field for six benchmark proteins, including BBL, hen egg white lysozyme (HEWL), staphylococcal nuclease (SNase), thioredoxin, ribonuclease A (RNaseA), and human muscle creatine kinase (HMCK). The root-mean-square deviation from the experimental pKa's of Asp, Glu, His, and Cys is 0.76 pH units, and the Pearson's correlation coefficient for the pKa shifts with respect to model values is 0.80. We demonstrated that a finite-size correction or much enlarged simulation box size can remove a systematic error of the calculated pKa's and improve agreement with experiment. Importantly, the simulations captured the relevant biology in several challenging cases, e.g., the titration order of the catalytic dyad Glu35/Asp52 in HEWL and the coupled residues Asp19/Asp21 in SNase, the large pKa upshift of the deeply buried catalytic Asp26 in thioredoxin, and the large pKa downshift of the deeply buried catalytic Cys283 in HMCK. We anticipate that PME-CpHMD will offer proper pH control to improve the accuracies of MD simulations and enable mechanistic studies of proton-coupled dynamical processes that are ubiquitous in biology but remain poorly understood due to the lack of experimental tools and limitation of current MD simulations.

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

Coupling Constant pH Molecular Dynamics with Accelerated Molecular Dynamics.

Publications

Coupling Constant pH Molecular Dynamics with Accelerated Molecular Dynamics.

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