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Improving AlphaFold2-based Protein Tertiary Structure Prediction with MULTICOM in CASP15
Open accessPreprint — published as 10.1038/s42004-023-00991-6 · Paper page
Abstract
Since CASP14, AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge in the field is to further improve the accuracy of AlphaFold2-based protein structure prediction. To address this challenge, we developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are further refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure prediction together to account for tertiary structural changes induced by protein-protein interaction in the assembly. The MULTICOM system participated in the tertiary structure prediction in the 15 th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as server and human predictors. Our best server predictor (MULTICOM_refine) ranked 3 rd among 47 CASP15 server predictors and our best human predictor (MULTICOM) ranked 7 th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ∼0.80 and ∼0.92, 9.6% and 8.2% and higher than ∼0.73 and 0.85 of the standard AlphaFold2 predictor respectively. The results demonstrate that our approach can significantly improve the accuracy of the AlphaFold2-based protein tertiary structure prediction. The source code of MULTICOM is available at: https://github.com/BioinfoMachineLearning/MULTICOM3 .
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Highly accurate protein structure prediction with AlphaFold
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Metadata
- Authors
- Liu, J., Guo, Z., Wu, T., Roy, R. S., Chen, C., & Cheng, J.
- Affiliations
- University of Missouri (US)
- Corresponding author
- Cheng, Jianlin
- Venue
- Communications Chemistry
- Publisher
- openRxiv
- Published
- 2023-05-01
- Volume
- 6
- Issue
- 1
- Pages
- 188-188
- ISSN
- 2399-3669 (ISSN-L)
- PMC
- PMC10484931
- OpenAlex
- W4386525930
- Cited by
- 31
- References
- 1
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 11 · 2023: 4
- FWCI
- 2.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Communications Chemistry (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1763246, 2308699, 1759934)
- U.S. Department of Energy (DESC0021303, DESC0020400)
- U.S. Department of Health and Human Services (R01GM093123 and R01GM146340)
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM093123, R01GM146340)
- 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 tertiary structure prediction, AlphaFold2, structural templates, model ranking, Foldseek, quaternary structure prediction, protein-protein interaction, protein complexes, CASP15
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