Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy
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Abstract
Abstract Accurate prediction of the fitness and stability of a protein upon mutations is of high importance in protein engineering and design. Despite the rapid development of deep learning techniques and accumulation of experimental data, the multi-labeled nature of fitness data hinders the training of robust deep-learning-based models for the fitness and stability prediction tasks. Here, we propose three geometric-learning-based models, GeoFitness, GeoDDG and GeoDTm, for the prediction of the fitness score, ΔΔG and ΔTm of a protein upon mutations, respectively. In the optimization of GeoFitness, we designed a novel loss function to allow supervised training of a unified model using the large amount of multi-labeled fitness data in the deep mutational scanning (DMS) database. By this means, GeoFitness efficiently learns the general functional effects of protein mutations and achieves better performance over the other state-of-the-art methods. To further improve the downstream tasks of ΔΔG/ΔTm prediction, we re-utilized the encoder of GeoFitness as a pre-trained module in GeoDDG and GeoDTm to overcome the challenge of lack of sufficient amount of specifically labeled data. This pre-training strategy in combination with data expansion remarkably improves model performance and generalizability. When evaluated on the benchmark test sets (S669 for ΔΔG prediction and a newly collected set S571 for ΔTm prediction), GeoDDG and GeoDTm outperform the other state-of-the-art methods by at least 35% and 70%, respectively, in terms of the Spearman correlation coefficient between predicted and experimental values. An online server for the suite of these three predictors, GeoStab-suite, is available at http://structpred.life.tsinghua.edu.cn/server_geostab.html.
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Decoding the effects of mutation on protein interactions using machine learning
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Intrinsic dataset features drive mutational effect prediction by protein language modelsPreprint
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- Authors
- Xu, Y., Liu, D., & Gong, H.
- Affiliations
- Tsinghua University (CN)
- Venue
- Nature Computational Science
- Published
- 2024-10-25
- Volume
- 4
- Issue
- 11
- Pages
- 840-850
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W4403770916
- Cited by
- 41
- References
- 84
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 5
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Computational Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (32171243)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein stability prediction, point mutations, geometric learning, pre-training, data expansion, labeled data scarcity
- MeSH terms
- Machine Learning, Algorithms, Mutation, Proteins, Computational Biology, Databases, Protein, Protein Stability
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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