Machine learning modeling of family wide enzyme-substrate specificity screens
Open accessAbstract
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.
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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 [33]
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- Authors
- Goldman, S., Das, R., Yang, K. K., & Coley, C. W.
- Affiliations
- Massachusetts Institute of Technology (US)
- Microsoft Research New England (United States) (US)
- Corresponding author
- Coley, Connor W.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-02-10
- Volume
- 18
- Issue
- 2
- Pages
- e1009853
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC8865696
- OpenAlex
- W3198646379
- Cited by
- 120
- References
- 62
- Citations by year
- 2026: 27 · 2025: 25 · 2024: 29 · 2023: 29 · 2022: 10
- FWCI
- 12.72
- Type
- journal article
- Language
- en
- Update notices
- new_version (2022-02-23)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- arXiv (Cornell University)
- Funders
- Machine Learning for Pharmaceutical Discovery and Synthesis Consortium
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Keywords
- enzyme substrate specificity, biocatalysis, compound-protein interaction prediction, kinase inhibitors, protein sequence representation, enzyme discovery, drug-target interactions, machine learning prediction models
- MeSH terms
- Machine Learning, Amino Acid Sequence, Proteins, Substrate Specificity, Drug Discovery
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