Protein multiple conformations prediction using multi-objective evolution algorithm
Open accessPreprint — published as 10.1007/s12539-023-00597-5
Abstract
Motivation: With the breakthrough of AlphaFold2 and the publication of AlphaFold DB, the protein structure prediction has made remarkable progress, which may further promote many potential applications of proteomics in all areas of life. However, it should be noted that AlphaFold2 models tend to represent only a single static structure, and accurately predicting multiple conformations remains a challenge. Therefore, it is essential to develop methods for predicting multiple conformations, which enable us to gain knowledge of multiple conformational states and the broader conformational landscape to better understand the mechanism of action. Results: In this work, we proposed a multiple conformational states folding method using the distance-based multi-objective evolutionary algorithm framework, named MultiSFold. First, a multi-objective energy landscape with multiple competing constraints generated by deep learning is constructed. Then, an iterative modal exploration and exploitation strategy based on multi-objective optimization, geometric optimization and structural similarity clustering is designed to perform conformational sampling. Finally, the final population is generated using a loop-specific perturbation strategy to adjust the spatial orientations. MultiSFold was compared with state-of-the-art methods on a developed benchmark testset containing 81 proteins with two representative conformational states. Based on the proposed metric, the success ratio of MultiSFold predicting multiple conformations was 70.4% while that of AlphaFold2 was 9.88%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to produce conformations spanned the range between two experimental structures. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate that the TM-score of MultiSFold is 2.97% and 7.72% higher than that of AlphaFold2 and RoseTTAFold, respectively, supporting our hypothesis that multiple competing optimization objectives can further assist conformational search to improve prediction accuracy.
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- Authors
- Hou, M.-H., Jin, S.-R., Cui, X.-Y., Peng, C.-X., Zhao, K.-L., Song, L., & Zhang, G.-J.
- Affiliations
- Zhejiang University of Technology (CN)
- Corresponding author
- Song, Le
- ORCID
- Song, Le
- Venue
- Interdisciplinary Sciences Computational Life Sciences
- Publisher
- openRxiv
- Published
- 2023-04-21
- Volume
- 16
- Issue
- 3
- Pages
- 519-531
- ISSN
- 1867-1462 (ISSN-L)
- OpenAlex
- W4390663686
- Cited by
- 29
- References
- 51
- Citations by year
- 2026: 2 · 2025: 15 · 2024: 9 · 2023: 3
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- green
- dx.doi.org
- Licence
- cc-by-nc-nd
- Locations
- Interdisciplinary Sciences Computational Life Sciences (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (LZ20F030002, 62173304)
- National Key Research and Development Program 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 › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- multi-objective evolutionary algorithms, protein energy landscape, protein structure prediction
- MeSH terms
- Deep Learning, Algorithms, Humans, Models, Molecular, Protein Conformation, Proteins, Computational Biology
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