Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

TL;DR

This paper reviews recent AI and machine learning advances that enhance the understanding of intrinsically disordered proteins by integrating diverse experimental data and simulations to uncover their complex structure-function relationships.

Contribution

It introduces scalable statistical inference methods that combine experimental and simulation data to better characterize IDP conformational ensembles.

Findings

AI techniques improve IDP structural analysis

Integration of experimental and simulation data reveals new insights

Scalable inference methods facilitate atomistic understanding

Abstract

We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.