Native state of natural proteins optimises local entropy

TL;DR

This paper investigates how local entropy, a concept from statistical mechanics, can describe the native state of proteins, demonstrating that high local entropy states correlate with enhanced stability and faster folding in models.

Contribution

It introduces a novel application of local entropy to model protein native states, providing a general sampling method that improves understanding of protein stability and folding.

Findings

High local entropy states are associated with native protein stability.

Sampling methods based on local entropy can efficiently explore protein conformations.

Enhanced stability and folding rates are observed in model proteins using these methods.

Abstract

The differing ability of polypeptide conformations to act as the native state of proteins has long been rationalized in terms of differing kinetic accessibility or thermodynamic stability. Building on the successful applications of physical concepts and sampling algorithms recently introduced in the study of disordered systems, in particular artificial neural networks, we quantitatively explore how well a quantity known as the local entropy describes the native state of model proteins. In lattice models and all-atom representations of proteins, we are able to efficiently sample high local entropy states and to provide a proof of concept of enhanced stability and folding rate. Our methods are based on simple and general statistical--mechanics arguments, and thus we expect that they are of very general use.