人工智能辅助的蛋白质工程

卞佳豪, 杨广宇

Artificial intelligence-assisted protein engineering

Jiahao BIAN, Guangyu YANG

图1 理性设计，定向进化和人工智能辅助的蛋白质工程策略示意图 （理性设计依赖序列和结构信息，精准设计突变体文库，但难以应用于缺少结构功能信息的蛋白质。定向进化中对目标基因进行多轮突变和筛选实验，不受结构功能信息限制，但是需要进行高通量的筛选方法。人工智能辅助的蛋白质工程则需要大量的序列-功能数据，可以来源于实验、计算和数据库等多方面，通过构建的预测模型，能够更有效地探索蛋白质突变体序列空间）

Fig.2 Schematic diagram of rational design, directed evolution and artificial intelligence-assisted protein engineering （Rational design relies on sequence and structural information to design mutant libraries accurately. However, it is difficult to apply to proteins lacking structural and functional information. In the directed evolution strategy, multiple rounds of mutation and screening experiments are performed on target genes, which are not limited by structural and functional information, but high-throughput screening methods are required. Artificial intelligence-assisted protein engineering requires a large amount of sequence-function data, which can be derived from experiments, calculations, and databases. Through the predictive model, the sequence space of protein mutants can be explored more effectively）