Constant pH molecular dynamics simulations of membrane proteins.

Abstract

Environment-driven dynamic protonation of titratable residues in a protein plays a key role in membrane transport and pH response in various biological processes. The dominant protonation state of a titratable amino-acid residue depends on environmental conditions like pH, dielectric environment (ionic strength, proximity to water-membrane interfaces), other amino-acid residues in its vicinity, etc. Constant pH Molecular Dynamics (CpHMD) techniques enable the study of dynamic protonation events in a molecular simulation and can be regarded as an in-silico titration process. In CpHMD techniques, the protonation state of a titratable residue is allowed to propagate between the different ionization states. As the protonation states of different residues change, the charge of the total system fluctuates. In hybrid solvent-based schemes, where the changes in the protonation states are handled using an implicit solvent representation, these charge fluctuations do not pose a problem. However, in case of an explicit-solvent representation, where the electrostatics are handled using a particle mesh ewald (PME) scheme, the varying charge poses a problem in reliable evaluation of energetics and thereby the pKa of the titratable residues. A large aqueous fraction in the simulation box is known to mitigate the charge artifacts. However, compared to soluble proteins, membrane protein simulation systems have a large non-aqueous volume fraction, owing to the lipid membrane. In this study, we compare different CpHMD methodologies for estimation of pKa in membrane proteins. These methods include both an explicit solvent-based CpHMD as well as hybrid solvent-based CpHMD. We present the pKa values of the acidic residues (aspartic acid, glutamic acid) from these simulations. In general, the hybrid solvent-based scheme showed a faster convergence compared to the explicit solvent-based scheme. However, in case of buried residues, the hybrid solvent-based scheme took a longer time to converge.