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Knot or not? Identifying unknotted proteins in knotted families with sequence‐based Machine Learning model
Open accessPublished version of a preprint
Abstract
Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (AF), this study computationally examines the entire UniProt database to create a robust dataset of knotted and unknotted proteins. Utilizing this dataset, we develop a machine learning (ML) model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences. We tested the model's capabilities on 100 proteins whose structures had not yet been predicted by AF and found agreement with our local prediction in 92% cases. From the point of view of structural biology, we found that all potentially knotted proteins predicted by AF can be classified only into 17 families. This allows us to discover the presence of unknotted proteins in families with a highly conserved knot. We found only three new protein families: UCH, DUF4253, and DUF2254, that contain both knotted and unknotted proteins, and demonstrate that deletions within the knot core could potentially account for the observed unknotted (trivial) topology. Finally, we have shown that in the majority of knotted families (11 out of 15), the knotted topology is strictly conserved in functional proteins with very low sequence similarity. We have conclusively demonstrated that proteins AF predicts as unknotted are structurally accurate in their unknotted configurations. However, these proteins often represent nonfunctional fragments, lacking significant portions of the knot core (amino acid sequence).
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Cites
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
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329
Alternative splicing and evolution: diversification, exon definition and function
Keren, H., Lev-Maor, G., & Ast, G. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2776 · Cited by 1,082
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Metadata
- Authors
- Sikora, M., Klimentova, E., Uchal, D., Sramkova, D., Perlinska, A. P., Nguyen, M. L., Korpacz, M., Malinowska, R., Nowakowski, S., Rubach, P., Simecek, P., & Sulkowska, J. I.
- Affiliations
- University of Warsaw (PL)
- Central European Institute of Technology, Masaryk University Brno Czech Republic
- Centre of New Technologies, University of Warsaw Warsaw Poland
- Corresponding author
- Sulkowska, Joanna I.
- ORCID
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2024-06-18
- Volume
- 33
- Issue
- 7
- Pages
- e4998-e4998
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC11184937
- OpenAlex
- W4399776685
- Cited by
- 13
- References
- 3
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 5
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Protein Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ReIn UW (University of Warsaw) (submittedVersion, cc-by-nc-nd)
- Funders
- Narodowe Centrum Nauki (UMO‐2018/31/B/NZ1/04016, 2021/43/I/NZ1/03341)
- Grantová Agentura České Republiky (23‐04260L)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Mathematics › Geometry and Topology › Geometric and Algebraic Topology
- Keywords
- knotted proteins, sequence-based prediction, protein topology, AlphaFold, UniProt
- MeSH terms
- Machine Learning, Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins, Databases, Protein
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