重组
定向进化
定向分子进化
上位性
基石
合成生物学
突变
选择(遗传算法)
计算机科学
基因组工程
蛋白质工程
多样性(政治)
生化工程
代谢工程
同源重组
生物
遗传学
计算生物学
突变
人工智能
工程类
清脆的
基因组编辑
基因
生物化学
酶
艺术
社会学
突变体
人类学
视觉艺术
作者
Xinyue Wang,Anni Li,Xiujuan Li,Haiyang Cui
标识
DOI:10.1002/chem.202303889
摘要
Abstract Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high‐throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer‐assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning‐guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.
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