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

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 <i>pmemd</i> 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 side chains in 4 miniproteins and 7 enzymes with experimentally known p<i>K</i>_a’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 p<i>K</i>_a’s using 2 ns per replica sampling yielded a mean unsigned error of 0.70, a root-mean-squared error of 0.91, and a linear correlation coefficient of 0.79. Though 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 p<i>K</i>_a’s of the coupled carboxylic dyads. We quantified the sampling errors, which revealed that prolonged simulation is needed to converge p<i>K</i>_a’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 p<i>K</i>_a predictions