Development of a pH stat to properly control solution pH in biomolecular simulations has been a long-standing goal in the community. Towards this goal recent years have witnessed the emergence of the so-called constant pH molecular dynamics methods. However, the accuracy and generality of these methods have been hampered by the use of implicit-solvent models or truncation-based electrostatic schemes. Here we report the implementation of the particle mesh Ewald (PME) scheme into the all-atom continuous constant pH molecular dynamics (CpHMD) method, enabling CpHMD to be performed with a standard MD engine at a fractional added computational cost. We demonstrate the performance using pH replica-exchange CpHMD simulations with titratable water for a stringent test set of proteins, HP36, BBL, HEWL and SNase. With the sampling time of 10 ns per replica, most pKa’s are converged, yielding the average absolute and root-mean-square deviations of 0.61 and 0.77, respectively, from experiment. Linear regression of the calculated vs experimental pKa shifts gives a correlation coefficient of 0.79, a slope of 1 and an intercept near 0. Analysis reveals inadequate sampling of structure relaxation accompanying a protonation-state switch as a major source of the remaining errors, which are reduced as simulation prolongs. These data suggest PME-based CpHMD can be used as a general tool for pH-controlled simulations of macromolecular systems in various environments, enabling atomic insights into pH-dependent phenomena involving not only soluble proteins but also transmembrane proteins, nucleic acids, surfactants and polysaccharides.

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 side chains in 4 miniproteins and 7 enzymes with experimentally known p K'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 K'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 K's of the coupled carboxylic dyads. We quantified the sampling errors, which revealed that prolonged simulation is needed to converge p K'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 K predictions.

Escherichia coli NhaA is a prototype sodium-proton antiporter, which has been extensively characterized by X-ray crystallography, biochemical and biophysical experiments. However, the identities of proton carriers and details of pH-regulated mechanism remain controversial. Here we report constant pH molecular dynamics data, which reveal that NhaA activation involves a net charge switch of a pH sensor at the entrance of the cytoplasmic funnel and opening of a hydrophobic gate at the end of the funnel. The latter is triggered by charging of Asp164, the first proton carrier. The second proton carrier Lys300 forms a salt bridge with Asp163 in the inactive state, and releases a proton when a sodium ion binds Asp163. These data reconcile current models and illustrate the power of state-of-the-art molecular dynamics simulations in providing atomic details of proton-coupled transport across membrane which is challenging to elucidate by experimental techniques.

Despite the relevance of understanding structure-function relationships, robust prediction of proton donors and nucleophiles in enzyme active sites remains challenging. Here we tested three types of state-of-the-art computational methods to calculate the p K's of the buried and hydrogen bonded catalytic dyads in five enzymes. We asked the question what determines the p K order, i.e., what makes a residue proton donor vs a nucleophile. The continuous constant pH molecular dynamics simulations captured the experimental p K orders and revealed that the negative nucleophile is stabilized by increased hydrogen bonding and solvent exposure as compared to the proton donor. Surprisingly, this simple trend is not apparent from crystal structures and the static structure-based calculations. While the generality of the findings awaits further testing via a larger set of data, they underscore the role of dynamics in bridging enzyme structures and functions.

A facile approach was developed to prepare positively charged and red-emitting lysozyme-stabilized Ag nanoclusters (Lys-AgNCs) using NaBH₄ as a reducing agent at room temperature. The Lys-AgNCs can be applied in the highly selective detection of Hg²⁺.