Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity

Zi-Lin Li, Shuxin Pei, Ziying Chen, Teng-Yu Huang, Xu-Dong Wang, Lin Shen (), Xuebo Chen (), Qi-Qiang Wang, Wang De-Xian and Yu-Fei Ao ()
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Zi-Lin Li: Chinese Academy of Sciences
Shuxin Pei: Beijing Normal University
Ziying Chen: Beijing Normal University
Teng-Yu Huang: Chinese Academy of Sciences
Xu-Dong Wang: Chinese Academy of Sciences
Lin Shen: Beijing Normal University
Xuebo Chen: Beijing Normal University
Qi-Qiang Wang: Chinese Academy of Sciences
Wang De-Xian: Chinese Academy of Sciences
Yu-Fei Ao: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research.

Date: 2024
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DOI: 10.1038/s41467-024-53048-0

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