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Rapid and accurate prediction of protein homo-oligomer symmetry with Seq2Symm
Open accessPreprint — published as 10.1038/s41467-025-57148-3 · Paper page
Abstract
The majority of proteins must form higher-order assemblies to perform their biological functions. Despite the importance of protein quaternary structure, there are few machine learning models that can accurately and rapidly predict the symmetry of assemblies involving multiple copies of the same protein chain. Here, we address this gap by training several classes of protein foundation models, including ESM-MSA, ESM2, and RoseTTAFold2, to predict homo-oligomer symmetry. Our best model named Seq2Symm, which utilizes ESM2, outperforms existing template-based and deep learning methods. It achieves an average PR-AUC of 0.48 and 0.44 across homo-oligomer symmetries on two different held-out test sets compared to 0.32 and 0.23 for the template-based method. Because Seq2Symm can rapidly predict homo-oligomer symmetries using a single sequence as input (~ 80,000 proteins/hour), we have applied it to 5 entire proteomes and ~ 3.5 million unlabeled protein sequences to identify patterns in protein assembly complexity across biological kingdoms and species.
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Highly accurate protein structure prediction with AlphaFold
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Metadata
- Authors
- Kshirsagar, M., Meller, A., Humphreys, I., Sledzieski, S., Xu, Y., Dodhia, R., Horvitz, E., Berger, B., Bowman, G., Ferres, J. L., Baker, D., & Baek, M.
- Affiliations
- Microsoft (United States) (US)
- Washington University in St. Louis (US)
- University of Washington (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- University of Pennsylvania (US)
- Howard Hughes Medical Institute (US)
- Seoul National University (KR)
- Corresponding author
- Kshirsagar, Meghana
- Baek, Minkyung
- ORCID
- Venue
- Nature Communications
- Published
- 2024-04-26
- Volume
- 16
- Issue
- 1
- Pages
- 2017-2017
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11868566
- OpenAlex
- W4408010982
- Cited by
- 22
- References
- 1
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 3
- FWCI
- 5.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (2141064)
- Bill and Melinda Gates Foundation (OPP1156262)
- Seoul National University
- Ministry of Science and ICT, South Korea (RS‐2023‐00220628)
- National Institutes of Health (7f30hl162431-03, OPP1156262, 1F30HL162431-01A1)
- Institute for Information and Communications Technology Promotion (RS-2023-00220628)
- National Heart, Lung, and Blood Institute (F30 HL162431)
- National Institute of General Medical Sciences (R35GM141861, R35GM152085)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein oligomerization, protein assembly, ESM-2, sequence-based prediction, protein structure prediction, AlphaFold2-Multimer
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