On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins

TL;DR

This paper reviews how machine learning and computational methods can advance understanding of intrinsically disordered proteins, their structures, dynamics, interactions, and roles in disease.

Contribution

It highlights recent computational approaches and open questions in studying IDPs and IDRs, emphasizing machine learning's potential to predict structures and functions.

Findings

Machine learning aids in predicting transient structures of IDPs.

Computational methods help understand IDP interactions and phase separation.

Insights into IDP roles in disease and genomic variant interpretation.

Abstract

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.