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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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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Principles that Govern the Folding of Protein Chains
Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096
Biomolecular condensates: organizers of cellular biochemistry
Banani, S. F., Lee, H. O., Hyman, A. A., & Rosen, M. K. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.7 · Cited by 6,058
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Liquid phase condensation in cell physiology and disease
Shin, Y., & Brangwynne, C. P. (2017) · Science · doi:10.1126/science.aaf4382 · Cited by 4,143
Svergun, D. I., Barberato, C., & Koch, M. (1995) · Journal of Applied Crystallography · doi:10.1107/s0021889895007047 · Cited by 3,388
Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378
Phase transitions in the assembly of multivalent signalling proteins
Li, P., Banjade, S., Cheng, H., Kim, S., Chen, B., Guo, L., Llaguno, M. C., Hollingsworth, J. V., King, D., Banani, S. F., et al. (2012) · Nature · doi:10.1038/nature10879 · Cited by 2,565
Wang, J., Choi, J.-M., Holehouse, A., Lee, H. O., Zhang, X., Jahnel, M., Maharana, S., Lemaitre, R., Pozniakovsky, A., Drechsel, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.006 · Cited by 1,876
Nott, T. J., Petsalaki, E., Farber, P., Jervis, D., Fussner, E., Plochowietz, A., Craggs, T. D., Bazett-Jones, D. P., Pawson, T., Forman-Kay, J. D., et al. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.01.013 · Cited by 1,803
Research works printed but not linked: 5
- Rao, 2021, Msa transformer, bioRxiv
- Perspective, 2013, Coarse-grained models for biomolecular systems, J. Chem. Phys., 139, 09B201_1
- Groth, 2001, Determination of conformational equilibrium of peptides in solution by NMR spectroscopy and theoretical conformational analysis: Application to the calibration of mean-field solvation models, Peptide Sci. Original Res. Biomol., 60, 79
- Tesei, 2021, Accurate model of liquid-liquid phase behaviour of intrinsically-disordered proteins from data-driven optimization of single-chain properties, bioRxiv
- Staller, 2021, Design principles of acidic transcriptional activation domains, bioRxiv
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Conformational ensembles of the human intrinsically disordered proteome
Tesei, G., Trolle, A. I., Jonsson, N., Betz, J., Knudsen, F. E., Pesce, F., Johansson, K. E., & Lindorff‐Larsen, K. (2024) · Nature · doi:10.1038/s41586-023-07004-5 · Cited by 331
Self-citationAlphaFold2 models indicate that protein sequence determines both structure and dynamics
Guo, H.-B., Perminov, A., Bekele, S., Kedziora, G., Farajollahi, S., Varaljay, V., Hinkle, K., Molinero, V., Meister, K., Hung, C., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-14382-9 · Cited by 248
Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240
AlphaFold2: A Role for Disordered Protein/Region Prediction?
Wilson, C. J., Choy, W.-Y., & Karttunen, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094591 · Cited by 173
The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
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
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