Enzyme function prediction using contrastive learning

• 成果类型: Article
• 署名作者: Yu, Tianhao; Cui, Haiyang; Li, Jianan Canal; Luo, Yunan; Jiang, Guangde; Zhao, Huimin
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; Cornell University; University System of Georgia; Georgia Institute of Technology; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-10077
• DOI: 10.1126/science.adf2465
• 发表日期: 2023-03-31
• 页码: 1358-+
• 关键词: crystal-structure; protein; bacterial; mechanism; EVOLUTION; families; cofactor; sequence; biology

摘要

Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized functions or multiple activities. We present a machine learning algorithm named CLEAN (contrastive learning-enabled enzyme annotation) to assign EC numbers to enzymes with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp. The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes, (ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers-functions that we demonstrate by systematic in silico and in vitro experiments. We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes, thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis.