On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins

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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.

OriginalsprogEngelsk
Artikelnummer167196
TidsskriftJournal of Molecular Biology
Vol/bind433
Udgave nummer20
Antal sider22
ISSN0022-2836
DOI
StatusUdgivet - 2021

Bibliografisk note

Funding Information:
We acknowledge many discussions with our colleagues at the Structural Biology and NMR Laboratory and Linderstrøm-Lang Centre for Protein Science, and thank Tanja Mittag for comments on the manuscript. Asta B. Andersen is thanked for graphics support. Our research is supported by the Novo Nordisk Challenge Programmes REPIN (NNF18OC0033926; BBK) and PRISM (NNF18OC0033950; KLL), and the Lundbeck Foundation BRAINSTRUC initiative in structural biology (R155-2015-2666; KLL & BBK).

Publisher Copyright:
© 2021 The Author(s)

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