ArticleMetabolic engineering2017
Predicting novel substrates for enzymes with minimal experimental effort with active learning.
Article in Metabolic engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Information leakage in enzyme-substrate prediction.Bioinformatics (Oxford, England) · 2026Article
- Robots and Minimal, Physics-Informed Features: A Hybrid Framework for Enzyme Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computationally Assisted Noncanonical Amino Acid Incorporation.ACS central science · 2025Article
- A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.PLoS computational biology · 2024Article
- Protein structure-function continuum model: Emerging nexuses between specificity, evolution, and structure.Protein science : a publication of the Protein Society · 2024Review
- Advances in ligand-specific biosensing for structurally similar molecules.Cell systems · 2023Review
- A general model to predict small molecule substrates of enzymes based on machine and deep learning.Nature communications · 2023Article
- Data-driven enzyme engineering to identify function-enhancing enzymes.Protein engineering, design & selection : PEDS · 2023Review
- Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Review
- EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases.Biomolecules · 2022Article
- Data-driven discovery of cardiolipin-selective small molecules by computational active learning.Chemical science · 2022Article
- Key Enzymes in Fatty Acid Synthesis Pathway for Bioactive Lipids Biosynthesis.Frontiers in nutrition · 2022Review
- Enzyme Promiscuity Prediction Using Hierarchy-Informed Multi-Label Classification.Bioinformatics (Oxford, England) · 2021Article
- New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review.Frontiers in bioengineering and biotechnology · 2021Review
- A semi-supervised machine learning framework for microRNA classification.Human genomics · 2019Article
- Biochemical control systems for small molecule damage in plants.Plant signaling & behavior · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized with a limited number of human-selected compounds that may not be representative of the enzyme's versatility. While testing large numbers of compounds may be impractical, computational approaches can exploit existing data to determine the most informative substrates to test next, thereby more thoroughly exploring an enzyme's versatility. To demonstrate this, we used existing studies and tested compounds for four different enzymes, developed support vector machine (SVM) models using these datasets, and selected additional compounds for experiments using an active learning approach. SVMs trained on a chemically diverse set of compounds were discovered to achieve maximum accuracies of ~80% using ~33% fewer compounds than datasets based on all compounds tested in existing studies. Active learning-selected compounds for testing resolved apparent conflicts in the existing training data, while adding diversity to the dataset. The application of these algorithms to wide arrays of metabolic enzymes would result in a library of SVMs that can predict high-probability promiscuous enzymatic reactions and could prove a valuable resource for the design of novel metabolic pathways.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.