The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Enzyme function prediction using contrastive learning
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.
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Cites
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Database resources of the national center for biotechnology information
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The InterPro protein families and domains database: 20 years on
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Disease variant prediction with deep generative models of evolutionary data
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Genomic language model predicts protein co-regulation and function
Hwang, Y., Cornman, A. L., Kellogg, E. H., Ovchinnikov, S., & Girguis, P. R. (2024) · Nature Communications · doi:10.1038/s41467-024-46947-9 · Cited by 99
Deep contrastive learning enables genome-wide virtual screening
Jia, Y., Gao, B., Tan, J., Zheng, J., Hong, X., Zhu, W., Tan, H., Xiao, Y., Tan, L., Cai, H., et al. (2026) · Science · doi:10.1126/science.ads9530 · Cited by 66
Wang, D., Pourmirzaei, M., Abbas, U. L., Zeng, S., Manshour, N., Esmaili, F., Poudel, B., Jiang, Y., Shao, Q., Chen, J., et al. (2025) · Advanced Science · doi:10.1002/advs.202404212 · Cited by 46
Protein representations: Encoding biological information for machine learning in biocatalysis
Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38
Metadata
- Authors
- Yu, T., Cui, H., Li, J. C., Luo, Y., Jiang, G., & Zhao, H.
- Venue
- Science
- Published
- 2023-03-31
- Volume
- 379
- Issue
- 6639
- Pages
- 1358-1363
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4362471278
- Cited by
- 440
- References
- 9
- FWCI
- 32.88
- Type
- journal article
- Language
- en
- Open access
- closed
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