Characterizing Protein Protonation Microstates Using Monte Carlo Sampling

Abstract

Proteins are polyelectrolytes with acidic and basic amino acids Asp, Glu, Arg, Lys, and His, making up ≈25% of the residues. The protonation state of residues, cofactors, and ligands defines a “protonation microstate”. In an ensemble of proteins some residues will be ionized and others neutral, leading to a mixture of protonation microstates rather than in a single one as is often assumed. The microstate distribution changes with pH. The protein environment also modifies residue proton affinity so microstate distributions change in different reaction intermediates or as ligands are bound. Particular protonation microstates may be required for function, while others exist simply because there are many states with similar energy. Here, the protonation microstates generated in Monte Carlo sampling in MCCE are characterized in HEW lysozyme as a function of pH and bacterial photosynthetic reaction centers (RCs) in different reaction intermediates. The lowest energy and highest probability microstates are compared. The ΔG, ΔH, and ΔS between the four protonation states of Glu35 and Asp52 in lysozyme are shown to be calculated with reasonable precision. At pH 7 the lysozyme charge ranges from 6 to 10, with 24 accepted protonation microstates, while RCs have ≈50,000. A weighted Pearson correlation analysis shows coupling between residue protonation states in RCs and how they change when the quinone in the QB site is reduced. Protonation microstates can be used to define input MD parameters and provide insight into the motion of protons coupled to reactions.

Simulation of high‐concentration self‐interactions for monoclonal antibodies from well‐behaved to poorly‐behaved systems

“…This would allow for explicit representation of His residues dependent on the local environment in a given configuration. Although the explicit charge equilibria approach has been attempted by others, it was out of the scope of this work and could prove to be computationally expensive at high‐ 58–61 …”

Electrostatics in Computational Biophysics and Its Implications for Disease Effects

“…Thus, the pKa values of ionizable groups may undergo pKa shift as proteins insert into the membrane [ 85 ]. In parallel, a significant amount of work has been completed using either predefined conformational space [ 86 ] or mimicking the conformational stability via Gaussian-based smooth dielectric function [ 87 ]. Furthermore, some methods use coarse-grained lattice-based models of proteins and train the model on existing experimental data [ 88 ] and the treecode-accelerated boundary integral solver [ 89 ].…”

AMOEBA Force Field Trajectories Improve Predictions of Accurate pKa Values of the GFP Fluorophore: The Importance of Polarizability and Water Interactions

“…However, knowing that a hydrophobic environment stabilizes the neutral form of an acid or base does not provide the specific magnitude and direction of electric fields or indicate which atoms over which distance range are involved, a level of detail necessary for understanding the effect of electric field on all aspects of protein structure and function. To obtain this, many attempts have been made to theoretically predict the p K a values of different targeted residues using quantum mechanics, combining quantum mechanics with classical molecular mechanics (QM/MM), , different approaches of continuum electrostatics, − ,− and the more recent constant pH molecular dynamics (MD) simulations. − Among these techniques, the more conventional and available electrostatic calculation methods could accurately predict the p K a values of protein residues that are exposed to solvent, but the p K a values of residues that are buried in a complicated environment such as a protein interior remain challenging. , The p K a values of buried residues could possibly be accurately calculated by constant pH MD simulations; , however, this is not currently available with polarizable force fields, the focus of the current work.…”