Extending the Stochastic Titration CpHMD to CHARMM36m

Abstract

The impact of pH on proteins is significant but often neglected in molecular dynamics simulations. Constant-pH Molecular Dynamics (CpHMD) is the state-of-the-art methodology to deal with these effects. However, it still lacks widespread adoption by the scientific community. The stochastic titration CpHMD is one of such methods that, until now, only supported the GROMOS force field family. Here, we extend this method's implementation to include the CHARMM36m force field available in the GROMACS software package. We test this new implementation with a diverse group of proteins, namely, lysozyme, Staphylococcal nuclease, and human and E. coli thioredoxins. All proteins were conformationally stable in the simulations, even at extreme pH values. The RMSE values (pK a prediction vs experimental) obtained were very encouraging, in particular for lysozyme and human thioredoxin. We have also identified a few residues that challenged the CpHMD simulations, highlighting scenarios where the method still needs improvement independently of the force field. The CHARMM36m all-atom implementation was more computationally efficient when compared with the GROMOS 54A7, taking advantage of a shorter nonbonded interaction cutoff and a less frequent neighboring list update. The new extension will allow the study of pH effects in many systems for which this force field is particularly suited, i.e., proteins, membrane proteins, lipid bilayers, and nucleic acids.

Benchmarking in silico Tools for Cysteine pKa Prediction

“…A major limitation of traditional single structure-based pKa methods is that they are unable to capture dynamic changes in the local environment of ionizable residues. In some cases, proteins may be trapped in nonrepresentative conformations 72 or largely coupled between conformational and protonation states. 73 It is also important to note the variability in pKa measurements of titratable residues in proteins.…”

“…These methods have been shown to yield good estimates of experimental pKa's for titratable residues, particularly Asp and Glu residues. 72,76,77 To this effect, we applied the Amber and NAMD CpHMD codes to predict Cys pKa's for the protein model systems studied. Both implicit and explicit solvent models were used in our simulations.…”

Benchmarking In Silico Tools for Cysteine pKa Prediction

“…A major limitation of traditional single-structure-based p K a methods is that they are unable to capture dynamic changes in the local environment of ionizable residues. In some cases, proteins may be trapped in nonrepresentative conformations or largely coupled between conformational and protonation states . It is also important to note the variability in p K a measurements of titratable residues in proteins.…”

“…Constant-pH MD simulations can directly sample pH-induced conformational changes and its effect on the titratable residue p K a and protonation state. These methods have been shown to yield good estimates of experimental p K a ’s for titratable residues, particularly Asp and Glu residues. ,, …”

Biomolecular Electrostatic Phenomena: An Evergreen Field

“…One of the fields where electrostatic computations proved to be useful is p K a estimation. In this virtual special issue, several methodological contributions to this topic are presented: one concerns the characterization of the ensemble of protonation microstates of a protein at a given pH; another allows the adoption of the CHARMM36m force field in the stochastic titration Constant pH Molecular Dynamics (CpHMD) method; one deals with the problem of net charge variation upon (de)­protonation in constant-pH MD with periodic boundary conditions; and one deals with modifying the reactive molecular dynamics approach providing more accurate and explainable p K a values for some titratable residues . CpHMD was also compared with the nonlinear Poisson–Boltzmann equation in terms of the capability to generate accurate ion distributions around a protein at different pH values …”