Effective harmonic potentials: insights into the internal cooperativity and sequence-specificity of protein dynamics

TL;DR

This paper introduces a method to derive residue- and distance-specific harmonic potentials from NMR data, enhancing elastic network models to better capture protein dynamics and sequence-specific effects.

Contribution

It presents a novel approach to incorporate chemical specificity into elastic network models using statistical analysis of NMR ensembles.

Findings

Improved description of residue motion cooperativity

Enhanced modeling of sequence-specific effects on dynamics

Potential for systematic exploration of sequence influence

Abstract

The proper biological functioning of proteins often relies on the occurrence of coordinated fluctuations around their native structure, or of wider and sometimes highly elaborated motions. Coarse-grained elastic-network descriptions are known to capture essential aspects of conformational dynamics in proteins, but have so far remained mostly phenomenological, and unable to account for the chemical specificities of amino acids. Here, we propose a method to derive residue- and distance-specific effective harmonic potentials from the statistical analysis of an extensive dataset of NMR conformational ensembles. These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures. In the context of the elastic network model, they yield a strongly improved description of the cooperative aspects of residue motions, and give the opportunity to systematically explore the influence of sequence details on protein dynamics.