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Knot or Not? Sequence-Based Identification of Knotted Proteins With Machine Learning
Open accessPreprint — published as 10.1002/pro.4998 · Paper page
Abstract
Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles remain a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold, 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 model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences, with our best-performing model demonstrating a 98.5% overall accuracy. Unveiling the sequence factors that contribute to knot formation, we discover that proteins predicted to be unknotted from known knotted families are typically non-functional fragments missing a significant portion of the knot core. The study further explores the significance of the substrate binding site in knot formation, particularly within the SPOUT protein family. Our findings spotlight the potential of machine learning in enhancing our understanding of protein topology and propose further investigation into the role of knotted structures across other protein families. TOC Graphic:
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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
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Metadata
- Authors
- Šrámková, D., Sikora, M., Uchal, D., Klimentová, E., Perlinska, A. P., Nguyen, M. L., Korpacz, M., Malinowska, R., Rubach, P., Šimeček, P., & Sulkowska, J. I.
- Affiliations
- Masaryk University (CZ)
- University of Warsaw (PL)
- Central European Institute of Technology – Masaryk University (CZ)
- Corresponding author
- Šimeček, Petr
- Sulkowska, Joanna I.
- ORCID
- Venue
- Protein Science
- Publisher
- openRxiv
- Published
- 2023-09-07
- Volume
- 33
- Issue
- 7
- Pages
- e4998-e4998
- ISSN
- 0961-8368 (ISSN-L)
- PMC
- PMC11184937
- OpenAlex
- W4399776685
- Cited by
- 13
- References
- 1
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 5
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- 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
- Grantová Agentura České Republiky (GF23-04260L)
- Narodowe Centrum Nauki (#2021/43/I/NZ1/03341, UMO-2018/31/B/NZ1/04016)
- Masarykova Univerzita (LM2018140, 90254, CZ LM2018140)
- 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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