Network measures for protein folding state discrimination

TL;DR

This paper introduces novel network-based observables derived from protein structures that effectively classify proteins' folding mechanisms with high accuracy, providing physical insights into folding kinetics.

Contribution

The study presents new network measures with clear physical interpretations that improve classification of protein folding states over previous methods.

Findings

Achieved up to 90% classification accuracy.

Introduced observables linked to vibrational modes and folding cooperativity.

Validated effectiveness with simple classifiers.

Abstract

Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there isn't a first-principle model to explain this different behaviour. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as disciminant analysis.