关键词: 大数据, 机器学习, 酶催化, 酶设计, 生物合成

Abstract:

Enzymes are efficient catalysts with substrate specificity and stereo- and regioselectivity, which are widely used in producing chemicals, drugs and materials. Enzymes are cores for biocatalysis, and thus prediction on their functions and design of enzymatic reactions are driving forces for intelligent biomanufacturing through biocatalysis. So far limited understanding on enzymatic catalysis hinders the exploration of enzymatic reactions for industrial applications. For example, it is difficult to predict enzymatic activities on unreported substrates, to elucidate synthetic routes for newly found structures of enzymes, and to redesign enzymes for specific scenarios. In the era of big data, data-driven approaches have exhibited powerful capabilities for exploring enzymatic reactions, by filling gap between the large corpora of enzymatic data and limited understanding on functions of the enzymes. Recently, computational tools and platforms have greatly accelerated experimental research, and improved the design-build-test-learn cycle. Herein we review progress in computational tools for enzymatic reaction prediction and design, focusing on the application of deep learning methods in this field. Referring to key elements (substrate, product and enzyme) for enzymatic reactions, related databases are summarized. Then, the data-driven approaches for forward and backward prediction of enzymatic reaction routes and functions of enzymes, their design and theoretical calculation for enzymatic catalysis are addressed. Finally, the status and prospective of data-driven approaches for enzymatic catalysis prediction and design, including the data, model, algorithm and platform, are discussed.

Key words: big data, machine learning, enzymatic catalysis, enzyme design, biosynthesis