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Multiple conformational states assembly of multidomain proteins using evolutionary algorithm based on structural analogues and sequential homologues
Open accessPreprint — published as 10.1016/j.fmre.2024.05.003
Abstract
With the breakthrough of AlphaFold2, nearly all single-domain protein structures can be built at experimental resolution. However, accurate modelling of full-chain structures of multidomain proteins, particularly all relevant conformations for those with multiple states remain challenging. In this study, we develop a multidomain protein assembly method, M-SADA, for assembling multiple conformational states. In M-SADA, a multiple population-based evolutionary algorithm is proposed to sample multiple conformational states under the guidance of multiple energy functions constructed by combining homologous and analogous templates with inter-domain distances predicted by deep learning. On a developed benchmark dataset containing 72 multidomain proteins with multiple conformational states, the performance of M-SADA is significantly better than that of AlphaFold2 on multiple conformational states modelling, where 29/72 (40.3%) of proteins can be assembled with a TM-score >0.90 for highly distinct conformational states with M-SADA while AlphaFold2 does so in only 2/72 (2.8%) of proteins. Furthermore, M-SADA is tested on a developed benchmark dataset containing 296 multidomain proteins with single conformational state, and results show that the average TM-score of M-SADA on the best models is 0.913, which is 5.2% higher than that of AlphaFold2 models (0.868).
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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
- Peng, C., Zhou, X., Liu, J., Hou, M., Li, S. Z., & Zhang, G.
- Affiliations
- Zhejiang University of Technology (CN)
- Westlake University (CN)
- Corresponding author
- Zhang, Guijun
- Venue
- Fundamental Research
- Publisher
- openRxiv
- Published
- 2023-01-18
- Volume
- 6
- Issue
- 1
- Pages
- 77-87
- ISSN
- 2096-9457 (ISSN-L)
- PMC
- PMC12869773
- OpenAlex
- W4396953527
- Cited by
- 7
- References
- 1
- Citations by year
- 2025: 5 · 2024: 1 · 2023: 1
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Fundamental Research (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (62173304, 62203389)
- Ministry of Science and Technology of the People's Republic of China (2022ZD0115100)
- National Science and Technology Major Project
- National Major Science and Technology Projects of China (2022ZD0115103)
- Natural Science Foundation of Zhejiang Province (LZ20F030002)
- 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 assembly, evolutionary algorithms, structural analogues, energy function
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