Accurate and efficient description of protein vibrational dynamics: comparing molecular dynamics and Gaussian models

TL;DR

This paper introduces an extended Gaussian Network model with effective sidechain centroids, offering a fast and accurate way to describe protein vibrational dynamics, validated against extensive molecular dynamics simulations.

Contribution

The authors develop a more realistic Gaussian Network model that incorporates sidechain effects, maintaining computational efficiency and improving accuracy in protein motion predictions.

Findings

Model accurately describes correlated residue motions.

Validation against 14 ns MD simulation of HIV-1 protease.

Offers a fast alternative to extensive molecular dynamics.

Abstract

Current all-atom potential based molecular dynamics (MD) allow the identification of a protein's functional motions on a wide-range of time-scales, up to few tens of ns. However, functional large scale motions of proteins may occur on a time-scale currently not accessible by all-atom potential based molecular dynamics. To avoid the massive computational effort required by this approach several simplified schemes have been introduced. One of the most satisfactory is the Gaussian Network approach based on the energy expansion in terms of the deviation of the protein backbone from its native configuration. Here we consider an extension of this model which captures in a more realistic way the distribution of native interactions due to the introduction of effective sidechain centroids. Since their location is entirely determined by the protein backbone, the model is amenable to the same exact and computationally efficient treatment as previous simpler models. The ability of the model to describe the correlated motion of protein residues in thermodynamic equilibrium is established through a series of successful comparisons with an extensive (14 ns) MD simulation based on the AMBER potential of HIV-1 protease in complex with a peptide substrate. Thus, the model presented here emerges as a powerful tool to provide preliminary, fast yet accurate characterizations of proteins near-native motion.