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On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins
Open accessAbstract
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.
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“Native conformations of proteins are often described as time averaged ensembles of conformations with Boltzmann-type distributions, especially IDPs and proteins with IDRs100,101.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [100]
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Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155
“Preliminary studies suggest that the majority of targets with very low confidence score (pLDDT 50) assigned by AF2 are likely to be IDPs or IDRs rather than well-folded structures that AF2 fails to predict 42–44 .”
BackgroundInfluential
Metadata
- Authors
- Lindorff-Larsen, K., & Kragelund, B. B.
- Affiliations
- University of Copenhagen (DK)
- Corresponding author
- Kragelund, Birthe B.
- ORCID
- Kragelund, Birthe B.
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 433
- Issue
- 20
- Pages
- 167196
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W3170848616
- Cited by
- 85
- References
- 293
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 21 · 2023: 25 · 2022: 12 · 2021: 4
- FWCI
- 1.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Molecular Biology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Lundbeck Foundation (R155-2015-2666)
- Novo Nordisk (NNF18OC0033926)
- Novo Nordisk Foundation (NNF18OC0033950)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- intrinsically disordered proteins, intrinsically disordered regions, machine learning, structure prediction, integrative structural biology, protein dynamics, protein-protein interaction, linear motifs, biomolecular condensates, disease-associated variants, structure-function relationship
- MeSH terms
- Machine Learning, Amino Acid Sequence, Animals, Humans, Protein Conformation, Protein Folding, Intrinsically Disordered Proteins
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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