美国芝加哥大学Rama Ranganathan、法国索邦大学Martin Weigt等研究人员合作利用进化模型实现了分支酸变位酶的设计。2020年7月24日，《科学》发表了这一成果。

研究人员报道了一个过程，其可以完全从进化序列数据中了解蛋白质的限制条件，设计和构建合成基因的文库，并使用定量互补测定法对其进行体内活性测试。

对于分支酸变位酶（芳香族氨基酸生物合成中的关键酶），研究人员证明了具有大量序列多样性的天然样催化功能设计。进一步优化将生成模型集中于特定基因组环境中的功能。

数据表明，基于序列的统计模型足以指定蛋白质并提供具有大量功能的序列空间。该结果为基于进化的人工蛋白质设计提供了基础。

据了解，出于基础和实际的原因，酶的合理设计是一个重要目标。 

附：英文原文

Title: An evolution-based model for designing chorismate mutase enzymes

Author: William P. Russ, Matteo Figliuzzi, Christian Stocker, Pierre Barrat-Charlaix, Michael Socolich, Peter Kast, Donald Hilvert, Remi Monasson, Simona Cocco, Martin Weigt, Rama Ranganathan

Issue&Volume: 2020/07/24

Abstract: The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a process to learn the constraints for specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity. Further optimization focuses the generative model toward function in a specific genomic context. The data show that sequence-based statistical models suffice to specify proteins and provide access to an enormous space of functional sequences. This result provides a foundation for a general process for evolution-based design of artificial proteins.

DOI: 10.1126/science.aba3304

Source: https://science.sciencemag.org/content/369/6502/440