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CC BY 4.0 · Pharmaceutical Fronts 2024; 06(03): e252-e264
DOI: 10.1055/s-0044-1788317
Review Article

Deep Learning-Based Self-Adaptive Evolution of Enzymes

Shuiqin Jiang
1   Research Center for Systems Biosynthesis, China State Institute of Pharmaceutical Industry, Shanghai, People's Republic of China
,
Dong Yi
1   Research Center for Systems Biosynthesis, China State Institute of Pharmaceutical Industry, Shanghai, People's Republic of China
› Institutsangaben Funding This work was supported by the National Natural Science Foundation of China (Grant No. 22208217) for Jiang, and the Shanghai Pujiang Program (Grant No. 21PJ1423200) and the Program of Shanghai Academic/Technology Research Leader (Grant No. 23XD1435000) for Yi.
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Abstract

Biocatalysis has been widely used to prepare drug leads and intermediates. Enzymatic synthesis has advantages, mainly in terms of strict chirality and regional selectivity compared with chemical methods. However, the enzymatic properties of wild-type enzymes may or may not meet the requirements for biopharmaceutical applications. Therefore, protein engineering is required to improve their catalytic activities. Thanks to advances in algorithmic models and the accumulation of immense biological data, artificial intelligence can provide novel approaches for the functional evolution of enzymes. Deep learning has the advantage of learning functions that can predict the properties of previously unknown protein sequences. Deep learning-based computational algorithms can intelligently navigate the sequence space and reduce the screening burden during evolution. Thus, intelligent computational design combined with laboratory evolution is a powerful and potentially versatile strategy for developing enzymes with novel functions. Herein, we introduce and summarize deep-learning-assisted enzyme functional adaptive evolution strategies based on recent studies on the application of deep learning in enzyme design and evolution. Altogether, with the developments of technology and the accumulation of data for the characterization of enzyme functions, artificial intelligence may become a powerful tool for the design and evolution of intelligent enzymes in the future.

Keywords

artificial intelligence - deep learning - protein engineering - directed evolution - biopharmaceuticals


Publikationsverlauf

Eingereicht: 19. September 2023

Angenommen: 25. Juni 2024

Artikel online veröffentlicht:
03. September 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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