Information-theoretical measures identify accurate low-resolution representations of protein configurational space

TL;DR

This paper introduces an information-theoretical framework to evaluate and optimize clustering methods for simplifying protein conformational space in molecular dynamics simulations, revealing that ultrametric clustering best captures the self-similar organization of protein landscapes.

Contribution

It presents a novel application of information theory to assess and identify optimal low-resolution representations of protein conformational space, emphasizing ultrametric clustering.

Findings

Ultrametric clustering best captures the protein conformational landscape.

The framework balances simplicity and informativeness in data reduction.

The approach is applicable beyond biophysics to large datasets in various fields.

Abstract

A steadily growing computational power is employed to perform molecular dynamics simulations of biological macromolecules, which represents at the same time an immense opportunity and a formidable challenge. In fact, large amounts of data are produced, from which useful, synthetic, and intelligible information has to be extracted to make the crucial step from knowing to understanding. Here we tackled the problem of coarsening the conformational space sampled by proteins in the course of molecular dynamics simulations. We applied different schemes to cluster the frames of a dataset of protein simulations; we then employed an information-theoretical framework, based on the notion of resolution and relevance, to gauge how well the various clustering methods accomplish this simplification of the configurational space. Our approach allowed us to identify the level of resolution that optimally balances simplicity and informativeness; furthermore, we found that the most physically accurate clustering procedures are those that induce an ultrametric structure of the low-resolution space, consistently with the hypothesis that the protein conformational landscape has a self-similar organisation. The proposed strategy is general and its applicability extends beyond that of computational biophysics, making it a valuable tool to extract useful information from large datasets.