首页|期刊导航|生物技术通报|人工智能驱动的酶改造与设计研究进展

人工智能驱动的酶改造与设计研究进展

GUO Fa-xu FENG Quan ZHANG Jian-hua ZHOU Huan-bin YANG Sen WANG Jian ZHOU Guo-min

生物技术通报2025，Vol.41Issue(12)：50-65,16.

生物技术通报2025，Vol.41Issue(12)：50-65,16.DOI:10.13560/j.cnki.biotech.bull.1985.2025-0627

人工智能驱动的酶改造与设计研究进展

Research Advances in AI-driven Enzyme Modifying and Design

GUO Fa-xu ¹FENG Quan ²ZHANG Jian-hua ³ZHOU Huan-bin ⁴YANG Sen ²WANG Jian ³ZHOU Guo-min⁵

作者信息

1. College of Mechanical and Electrical Engineering,Gansu Agricultural University,Lanzhou 730070||National Nanfan Research Institute,Chinese Academy of Agriculture Science,Sanya 572024
2. College of Mechanical and Electrical Engineering,Gansu Agricultural University,Lanzhou 730070
3. National Nanfan Research Institute,Chinese Academy of Agriculture Science,Sanya 572024||Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081
4. National Nanfan Research Institute,Chinese Academy of Agriculture Science,Sanya 572024||Institute of Plant Protection,Chinese Academy of Agricultural Sciences,Beijing 100193
5. Nanjing Institute of Agricultural Mechani-zation,Ministry of Agriculture and Rural Affairs,Nanjing 210014||National Agricultural Science Data Center,Beijing 100081||Institute of Western Agriculture,Chinese Academy of Agricultural Sciences,Changji 831100
折叠

摘要

Abstract

Enzymes play a crucial role in both biological systems and industrial applications.Due to their unique catalytic properties,they are among the key choices for catalytic processes.However,traditional enzyme engineering and design approaches face significant challenges,such as the vastness of sequence space and the complexities associated with multi-objective optimization.In recent years,artificial intelligence(AI)technologies,particularly deep learning and generative AI methods,have provided novel perspectives and solutions for enzyme modification and design,enabling breakthroughs in overcoming these limitations with large-scale data support.AI-driven strategies have facilitated efficient exploration of sequence space,accurate prediction of structure-function relationships,and the coordinated multi-objective optimization using reinforcement learning frameworks.These methods have not only significantly accelerated the enzyme engineering process but also led to groundbreaking advancements in the enhancement of catalytic efficiency,thermal stability,and substrate specificity.This review systematically summarizes the latest research on AI-driven enzyme modification and design,providing an in-depth analysis of foundational database construction,intelligent modification strategies,and design methodologies.Furthermore,it discusses current challenges related to data,models,and engineering applications,as well as future directions.These innovations open up vast possibilities for the design of high-performance,multifunctional enzymes and are poised to propel fields such as biomanufacturing,environmental remediation,and agricultural biotechnology toward more efficient,intelligent,and sustainable development.

关键词

人工智能/酶/从头设计/数据驱动/生成式AI

Key words

artificial intelligence/enzymes/de novo design/data-driven/generative AI

引用本文复制引用

GUO Fa-xu,FENG Quan,ZHANG Jian-hua,ZHOU Huan-bin,YANG Sen,WANG Jian,ZHOU Guo-min..人工智能驱动的酶改造与设计研究进展[J].生物技术通报,2025,41(12):50-65,16.

基金项目

国家重点研发计划(2022YFF0711800),海南省自然科学基金(325MS155),三亚崖州湾科技城科技专项资助(SCKJ-JYRC-2023-45),三亚中国农业科学院国家南繁研究院南繁专项(YBXM2409,YBXM2410,YBXM2430,YBXM2508,YBXM2509),中央级公益性科研院所基本科研业务费专项(JBYW-AII-2024-05,JBYW-AII-2025-05,Y2025YC90) （2022YFF0711800）

生物技术通报

OA北大核心

ISSN：1002-5464

访问量0

下载量0

段落导航