Applied biocatalysis is driven by environmental and economic incentives for using enzymes in the synthesis of various pharmaceutical and industrially important chemicals. Protein engineering is used to tailor the properties of enzymes to catalyze desired chemical transformations, and some engineered enzymes now outperform the best chemocatalytic alternatives by orders of magnitude. Unfortunately, custom engineering of a robust biocatalyst is still a time-consuming process, but an understanding of how enzyme function depends on amino acid sequence will speed up the process. This review demonstrates how recent advances in ultrahigh-throughput screening, mutational scanning, DNA synthesis, metagenomics, and machine learning will soon make it possible to model, predict, and manipulate the relationship between protein sequence and function, accelerating the tailor design of novel biocatalysts.

在环境和经济上的动机下，应用酶催化合成各种药物和工业上重要的化学药品都需要使用生物催化。蛋白质工程用于调整酶的性质，以催化所需的化学转化，现在，某些工程化的酶比最佳的化学催化替代方案好几个数量级。不幸的是，强大的生物催化剂的定制工程仍然是一个耗时的过程，但是对酶功能如何取决于氨基酸序列的理解将加快该过程。这篇评论证明了超高通量筛选，突变扫描，DNA合成，宏基因组学和机器学习方面的最新进展将如何很快使建模，预测和操纵蛋白质序列与功能之间的关系成为可能，