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SPIRED-Fitness: an end-to-end framework for the prediction of protein structure and fitness from single sequence
Open accessPreprint — published as 10.1038/s41467-024-51776-x · Paper page
Abstract
A bstract: Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction accuracy, showing promise for many downstream prediction tasks. Here, we propose SPIRED, a novel single-sequence-based structure prediction model that exhibits comparable performance to the state-of-the-art methods but with approximately 5-fold acceleration in inference and at least one order of magnitude reduction in training consumption. By integrating SPIRED with downstream neural networks, we compose an end-to-end framework named SPIRED-Fitness for the rapid prediction of both protein structure and fitness from single sequence. SPIRED-Fitness and its derivative SPIRED-Stab achieve state-of-the-art performance in predicting the mutational effects on protein fitness and stability metrics, respectively.
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Metadata
- Authors
- Chen, Y., Xu, Y., Liu, D., Xing, Y., & Gong, H.
- Affiliations
- Tsinghua University (CN)
- Corresponding author
- Gong, Haipeng
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2024-02-02
- Volume
- 15
- Issue
- 1
- Pages
- 7400-7400
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11349999
- OpenAlex
- W4401900858
- Cited by
- 44
- References
- 2
- Citations by year
- 2026: 18 · 2025: 23 · 2024: 3
- FWCI
- 5.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (32171243)
- Ministry of Science and Technology of the People's Republic of China (2023YFF1204400)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein structure prediction, protein fitness prediction, end-to-end framework, inference acceleration, protein engineering, ESMFold
- MeSH terms
- Algorithms, Models, Molecular, Mutation, Protein Conformation, Proteins, Neural Networks, Computer, Computational Biology, Sequence Analysis, Protein, Protein Stability
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