Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy.

Publisher: Springer Nature Country of Publication: United States NLM ID: 101775476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2662-8457 (Electronic) Linking ISSN: 26628457 NLM ISO Abbreviation: Nat Comput Sci Subsets: MEDLINE

Original Publication: [New York, N.Y.] : Springer Nature, [2021]-

Mutation* ; Protein Stability* ; Proteins*/genetics ; Proteins*/chemistry; Computational Biology/methods ; Computational Biology/education ; Databases, Protein ; Algorithms ; Machine Learning

Accurate prediction of protein mutation effects is of great importance in protein engineering and design. Here we propose GeoStab-suite, a suite of three geometric learning-based models-GeoFitness, GeoDDG and GeoDTm-for the prediction of fitness score, ΔΔG and ΔT m of a protein upon mutations, respectively. GeoFitness engages a specialized loss function to allow supervised training of a unified model using the large amount of multi-labeled fitness data in the deep mutational scanning database. To further improve the downstream tasks of ΔΔG and ΔT m prediction, the encoder of GeoFitness is reutilized as a pre-trained module in GeoDDG and GeoDTm to overcome the challenge of lacking sufficient labeled data. This pre-training strategy, in combination with data expansion, markedly improves model performance and generalizability. In the benchmark test, GeoDDG and GeoDTm outperform the other state-of-the-art methods by at least 30% and 70%, respectively, in terms of the Spearman correlation coefficient.
Competing Interests: Competing interests: The authors declare no competing interests.
(© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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32171243 National Natural Science Foundation of China (National Science Foundation of China)

0 (Proteins)

Date Created: 20241025 Date Completed: 20241120 Latest Revision: 20241121

20241122

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