文献类型：期刊文献

中文题名：机器学习方法在酶定向进化中的应用进展

英文题名：Advances in the Application of Machine Learning Methods for Directed Evolution of Enzymes

作者：王慕镪[1];陈琦[1];马薇[1];李春秀[1];欧阳鹏飞[2];许建和[1]

机构：[1]华东理工大学生物工程学院生物反应器工程国家重点实验室,上海200237;[2]苏州百福安酶技术有限公司,苏州215512

年份：2023

卷号：39

期号：4

起止页码：38

中文期刊名：生物技术通报

外文期刊名：Biotechnology Bulletin

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD2023_2024】；

基金：国家重点研发计划(2019YFA0905000,2021YFC2102300);国家自然科学基金项目(21871085,31971380,31971381)。

语种：中文

中文关键词：定向进化;机器学习;蛋白质工程;生物催化

外文关键词：directed evolution;machine learning;protein engineering;biocatalysis

摘要：定向进化法通过模拟自然界的进化过程,可提高酶的进化速度,成为酶分子改造的关键技术。定向进化在生物催化以及药物设计等方面发挥着重要作用,但因突变的随机性所产生的数量庞大的突变体,使得实验筛选的能力面临巨大挑战。近年来,人工智能、大数据处理等新兴技术也发展成为生物催化领域的重要研究手段。其中,机器学习是一种统计学习的方法,通过数据驱动的方式获得序列/结构到酶功能的映射,为提高酶分子工程的效率提供帮助。本文综述了机器学习模型中所涉及的数据处理、描述符和算法等内容,重点叙述了机器学习方法在酶工程方面的研究与应用进展。随着机器学习算法和应用技术的进步,有望提出更加精准和有效的模型,助力新酶筛选与生物催化剂的精准设计改造。
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.