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.

出于基本和实际原因，酶的合理设计是一个重要目标。在这里，我们描述了一个过程来学习纯粹从进化序列数据中指定蛋白质的限制，设计和构建合成基因库，并使用定量互补分析测试它们的体内活性。对于芳香族氨基酸生物合成中的关键酶分支酸变位酶，我们展示了具有大量序列多样性的类天然催化功能的设计。进一步的优化将生成模型集中在特定基因组环境中的功能上。数据表明，基于序列的统计模型足以指定蛋白质并提供对功能序列巨大空间的访问。