Generalized Born Based Continuous Constant pH Molecular Dynamics in Amber: Implementation Benchmarking and Analysis

摘要

Solution pH plays an important role in structure and dynamics of biomolecular systems; however, pH effects cannot be accurately accounted for in conventional molecular dynamics simulations based on fixed protonation states. Continuous constant pH molecular dynamics (CpHMD) based on the λ-dynamics framework calculates protonation states on the fly during dynamical simulation at a specified pH condition. Here we report the CPU-based implementation of the CpHMD method based on the GBNeck2 generalized Born (GB) implicit-solvent model in the pmemd engine of the Amber molecular dynamics package. The performance of the method was tested using pH replica-exchange titration simulations of Asp, Glu and His sidechains in 4 miniproteins and 7 enzymes with experimentally known pKa’s, some of which are significantly shifted from the model values. The added computational cost due to CpHMD titration ranges from 11 to 33% for the data set and scales roughly linearly as the ratio between the titrable sites and number of solute atoms. Comparison of the experimental and calculated pKa’s using 2 ns per replica sampling yielded a mean unsigned error of 0.65, a root-mean-squared error of 0.90, and a linear correlation coefficient of 0.78. While this level of accuracy is similar to the GBSW-based CpHMD in CHARMM, in contrast to the latter, the current implementation was able to reproduce the experimental orders of the pKa’s of the coupled carboxylic dyads. We quantified the sampling errors, which revealed that prolonged simulation is needed to converge pKa’s of several titratable groups involved in salt-bridge-like interactions or deeply buried in the protein interior. Our benchmark data demonstrate that GBNeck2-CpHMD is an attractive tool for protein pKa predictions.