Accelerating the identification of informative reduced representations of proteins with deep learning for graphs

TL;DR

This paper introduces a deep learning method using graph networks to rapidly compute the mapping entropy of proteins, significantly speeding up the analysis of molecular dynamics data and enabling broader applications in biomolecular studies.

Contribution

The authors develop a deep graph network approach that accelerates the calculation of mapping entropy, improving efficiency by up to 100,000 times compared to traditional algorithms.

Findings

Deep graph networks achieve high accuracy in computing mapping entropy.

The method provides a speedup factor of up to 10^5.

It is easily transferable to other molecular structure functions.

Abstract

The limits of molecular dynamics (MD) simulations of macromolecules are steadily pushed forward by the relentless developments of computer architectures and algorithms. This explosion in the number and extent (in size and time) of MD trajectories induces the need of automated and transferable methods to rationalise the raw data and make quantitative sense out of them. Recently, an algorithmic approach was developed by some of us to identify the subset of a protein's atoms, or mapping, that enables the most informative description of it. This method relies on the computation, for a given reduced representation, of the associated mapping entropy, that is, a measure of the information loss due to the simplification. Albeit relatively straightforward, this calculation can be time consuming. Here, we describe the implementation of a deep learning approach aimed at accelerating the calculation of the mapping entropy. The method relies on deep graph networks, which provide extreme flexibility in the input format. We show that deep graph networks are accurate and remarkably efficient, with a speedup factor as large as $10^5$ with respect to the algorithmic computation of the mapping entropy. Applications of this method, which entails a great potential in the study of biomolecules when used to reconstruct its mapping entropy landscape, reach much farther than this, being the scheme easily transferable to the computation of arbitrary functions of a molecule's structure.