Marshall Liam R, Bhattacharya Sagar, Korendovych Ivan V
Department of Chemistry, Syracuse University, 111 College Place, Syracuse, New York 13224, United States.
JACS Au. 2023 Aug 18;3(9):2402-2412. doi: 10.1021/jacsau.3c00315. eCollection 2023 Sep 25.
Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.
定向进化改变了蛋白质工程，为快速改善蛋白质特性提供了一条途径。然而，在实践中，它受到超大蛋白质序列搜索空间的限制，因此需要识别诱变热点的方法，即最有可能产生有效影响的突变位置。从这个角度来看，我们对识别热点的实验方法（广义上定义为结构、生物信息学和动力学方法）的最新进展进行分类和讨论。利用蛋白质动力学和机器学习方法取得的最新成功为该领域提供了新的机会，无疑将有助于定向进化发挥其全部潜力。
