Abstract
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.


ML을 활용해 Protein sequence를 Input으로, EC number를 Output으로 제공하는 "CLEAN" Tool에 대해 논의했습니다.

Presenter : Seongmin Kim
Date : 2023.07.14