Machine learning for protein folding and dynamics

TL;DR

Recent advances in machine learning have significantly impacted protein folding and dynamics by improving structure prediction, simulation methods, and data analysis, promising further integration despite ongoing challenges.

Contribution

This paper reviews recent progress in applying machine learning to protein folding and dynamics, highlighting new methods and future challenges.

Findings

Machine learning enhances protein structure prediction accuracy.

ML-driven force-fields are transforming simulation approaches.

Data analysis and sampling in protein simulations are improved by ML techniques.

Abstract

Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way simulations are performed to explore the energy landscape of protein systems is also changing as force-fields are started to be designed by means of machine learning methods. These methods are also used to extract the essential information from large simulation datasets and to enhance the sampling of rare events such as folding/unfolding transitions. While significant challenges still need to be tackled, we expect these methods to play an important role on the study of protein folding and dynamics in the near future. We discuss here the recent advances on all these fronts and the questions that need to be addressed for machine learning approaches to become mainstream in protein simulation.