The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search.
Open accessPreprint — published as 10.1038/s41467-022-33729-4
Abstract
AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥ 0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb.
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Citations
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
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Metadata
- Authors
- Elofsson, A., Bryant, P., Pozzati, G., Zhu, W., Shenoy, A., & Kundrotas, P.
- Affiliations
- Stockholm University (SE)
- University of Kansas (US)
- Corresponding author
- Bryant, Patrick
- Venue
- Nature Communications
- Published
- 2022-07-27
- Volume
- 13
- Issue
- 1
- Pages
- 6028-6028
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9556563
- OpenAlex
- W4304731451
- Cited by
- 226
- References
- 1
- Citations by year
- 2026: 28 · 2025: 56 · 2024: 74 · 2023: 57 · 2022: 9
- FWCI
- 15.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- KU ScholarWorks (University of Kansas) (submittedVersion, cc-by)
- Funders
- Swedish e-Science Research Centre
- Knut och Alice Wallenbergs Stiftelse (Berzelius-2021-29.)
- Vetenskapsrådet (2021-03979, 2016-06301, Berzelius-2021-29)
- Stockholms Universitet
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein complex structure prediction, AlphaFold, Monte Carlo tree search, protein symmetry, GPU memory constraints
- MeSH terms
- Monte Carlo Method, Proteins
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