Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation

文献类型： 外文期刊

第一作者： Li, Guangyue

作者： Li, Guangyue;Qin, Youcai;Fontaine, Nicolas T.;Ng Fuk Chong, Matthieu;Cadet, Xavier F.;Pandjaitan, Rudy;Cadet, Frederic;Maria-Solano, Miguel A.;Feixas, Ferran;Garcia-Borras, Marc;Maria-Solano, Miguel A.;Feixas, Ferran;Garcia-Borras, Marc;Reetz, Manfred T.;Reetz, Manfred T.;Reetz, Manfred T.

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关键词： machine learning; innov' SAR; epistasis; artificial intelligence; epoxide hydrolase; molecular dynamics simulations

期刊名称：CHEMBIOCHEM （ 影响因子：3.164； 五年影响因子：3.013 ）

ISSN： 1439-4227

年卷期： 2021 年 22 卷 5 期

页码：

收录情况： SCI

摘要： Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside-the-box, predictions not found in other state-of-the-art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.

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