Rapid and accurate prediction of protein homo-oligomer symmetry using Seq2Symm
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Abstract
The majority of proteins must form higher-order assemblies to perform their biological functions, yet few machine learning models can accurately and rapidly predict the symmetry of assemblies involving multiple copies of the same protein chain. Here, we address this gap by finetuning several classes of protein foundation models, to predict homo-oligomer symmetry. Our best model named Seq2Symm, which utilizes ESM2, outperforms existing template-based and deep learning methods achieving an average AUC-PR of 0.47, 0.44 and 0.49 across homo-oligomer symmetries on three held-out test sets compared to 0.24, 0.24 and 0.25 with template-based search. Seq2Symm uses a single sequence as input and can predict at the rate of ~80,000 proteins/hour. We apply this method to 5 proteomes and ~3.5 million unlabeled protein sequences, showing its promise to be used in conjunction with downstream computationally intensive all-atom structure generation methods such as RoseTTAFold2 and AlphaFold2-multimer. Code, datasets, model are available at: https://github.com/microsoft/seq2symm .
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Cites
Highly accurate protein structure prediction with AlphaFold
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Self-citationDe novo design of protein structure and function with RFdiffusion
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- Authors
- Kshirsagar, M., Meller, A., Humphreys, I. R., Sledzieski, S., Xu, Y., Dodhia, R., Horvitz, E., Berger, B., Bowman, G. R., 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
- 2025-02-27
- Volume
- 16
- Issue
- 1
- Pages
- 2017-2017
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11868566
- OpenAlex
- W4408010982
- Cited by
- 23
- References
- 8
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 3
- FWCI
- 5.98
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein oligomerization, protein assembly, ESM-2, sequence-based prediction, protein structure prediction, AlphaFold2-Multimer
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