Cheng Feng, Zhu Leilei, Schwaneberg Ulrich
Lehrstuhl für Biotechnologie, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
Chem Commun (Camb). 2015 Jun 18;51(48):9760-72. doi: 10.1039/c5cc01594d.
Directed evolution has matured to a routinely applied algorithm to tailor enzyme properties to meet the demands in various applications. In order to free directed enzyme evolution from methodological restraints and to efficiently explore its potential, many different strategies have been used in directed evolution campaigns. Analysis of directed evolution campaigns reveals that traditional approaches, in which several iterative rounds of diversity generation and screening are performed, are gradually replaced by strategies which require less time, less screening efforts, and generate a molecular understanding of the targeted properties. In this review, conceptual advances in knowledge generating directed evolution strategies are summarized, compared to each other and to traditional directed evolution strategies. Finally, a 'KnowVolution' (knowledge gaining directed evolution) termed strategy is proposed.
定向进化已发展成为一种常规应用的算法，可定制酶的特性以满足各种应用的需求。为了使定向酶进化摆脱方法上的限制并有效挖掘其潜力，在定向进化研究中采用了许多不同的策略。对定向进化研究的分析表明，传统方法（即进行几轮迭代的多样性生成和筛选）正逐渐被耗时更少、筛选工作量更小且能从分子层面理解目标特性的策略所取代。在本综述中，对知识生成型定向进化策略的概念进展进行了总结，并相互比较以及与传统定向进化策略进行了比较。最后，提出了一种名为“KnowVolution”（知识获取型定向进化）的策略。
