A general model to predict small molecule substrates of enzymes based on machine and deep learning
Open accessAbstract
For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science.
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The enzyme sequence contains information about the catalyzed reaction.
“the enzyme sequence contains information about the catalyzed reaction 32”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [32]
Prediction performance depends strongly on the sequence similarity between a target enzyme and enzymes in the training set.
“In our study on predicting the substrate scope of enzymes 32 , we found that prediction performance depends strongly on the sequence similarity between a target enzyme and enzymes in the training set”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [32]
The representations from ESM-1b contain rich information about the structure and function of proteins.
“It has been shown that the resulting representations contain rich information about the structure and the function of the proteins 17 , 32 , 33 .”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [32]
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- Authors
- Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J.
- Affiliations
- Heinrich Heine University Düsseldorf (DE)
- Indian Institute of Technology Bombay (IN)
- EnginZyme (Sweden) (SE)
- Corresponding author
- Lercher, Martin J.
- Venue
- Nature Communications
- Published
- 2023-05-15
- Volume
- 14
- Issue
- 1
- Pages
- 2787-2787
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10185530
- OpenAlex
- W4376601927
- Cited by
- 193
- References
- 73
- Citations by year
- 2026: 65 · 2025: 64 · 2024: 51 · 2023: 13
- FWCI
- 17.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Univ. Duesseldorf: Duesseldorfer Dokumenten- und Publikationsserver (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (CRC 1310 + 390686111)
- Volkswagen Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Keywords
- enzyme catalysis, transformer models, deep learning, metabolites
- MeSH terms
- Machine Learning, Deep Learning, Catalysis, Proteins, Support Vector Machine
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