Biotechnology Bulletin ›› 2023, Vol. 39 ›› Issue (4): 38-48.doi: 10.13560/j.cnki.biotech.bull.1985.2022-0724

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Advances in the Application of Machine Learning Methods for Directed Evolution of Enzymes

WANG Mu-qiang1(), CHEN Qi1(), MA Wei1, LI Chun-xiu1, OUYANG Peng-fei2, XU Jian-he1()   

  1. 1. School of Biotechnology, East China University of Science and Technology, The State Key Laboratory of Bioreactor Engineering, Shanghai 200237
    2. Suzhou Bioforany EnzyTech Co. Ltd.,Suzhou 215512
  • Received:2022-06-16 Online:2023-04-26 Published:2023-05-16

Abstract:

Directed evolution can increase the rate of enzyme evolution by mimicking the natural evolutionary process and has become a key technology for enzyme engineering. Directed evolution has played an important role in biocatalysis and drug design, however the experimental screening is in great challenge due to the large number of mutant libraries caused by the randomness of mutations. In recent years, emerging technologies such as artificial intelligence and big data processing have also become crucial in biocatalysis researches. Machine learning methods are statistical learning approaches to obtain sequence/structure mappings to enzyme function in a data-driven manner, which will improve the efficiency of enzyme engineering. This paper reviews the state-of-the-art technologies involved in machine learning models, especially focusing on the research and application progresses of machine learning methods in enzyme engineering. With the advancement of machine learning algorithms and technologies, it is expected that more accurate and effective models will be proposed in the future to promote screening of new enzymes and accurate design of biocatalysts.

Key words: directed evolution, machine learning, protein engineering, biocatalysis