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

Autor: Xu Y; MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.; Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China., Liu D; MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.; Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China., Gong H; MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China. hgong@tsinghua.edu.cn.; Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China. hgong@tsinghua.edu.cn.
Jazyk: angličtina
Zdroj: Nature computational science [Nat Comput Sci] 2024 Nov; Vol. 4 (11), pp. 840-850. Date of Electronic Publication: 2024 Oct 25.
DOI: 10.1038/s43588-024-00716-2
Abstrakt: 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.)
Databáze: MEDLINE
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