ArticleBriefings in bioinformatics2024
High-throughput prediction of enzyme promiscuity based on substrate-product pairs.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
12 citing papers in PubMed.
- Iterative and data-driven ortholog mining enables reliable discovery of stereoselective ketoreductases.Nature communications · 2026Article
- A Novel Unsupervised Learning Model with Bayesian Hyperparameter Optimization for Predicting Substrates of Promiscuous Enzymes.ACS omega · 2026Article
- Leveraging artificial intelligence for mycotoxin management in food systems.NPJ science of food · 2026Review
- CACLENS: A Multitask Deep Learning System for Enzyme Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Diversity of FAAL enzymes and prediction of their substrate specificity using FAALPred.Protein science : a publication of the Protein Society · 2026Article
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- From reactants to products: computational methods for biosynthetic pathway design.Synthetic and systems biotechnology · 2025Review
- Enzyme promiscuity in the field of synthetic biology applied to white biotechnology: Opportunities and weaknesses.Biodesign research · 2025Review
- Finding the dark matter: Large language model-based enzyme kinetic data extractor and its validation.Protein science : a publication of the Protein Society · 2025Article
- Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model.Bioresources and bioprocessing · 2025Article
- FuncFetch: an LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The screening of enzymes for catalyzing specific substrate-product pairs is often constrained in the realms of metabolic engineering and synthetic biology. Existing tools based on substrate and reaction similarity predominantly rely on prior knowledge, demonstrating limited extrapolative capabilities and an inability to incorporate custom candidate-enzyme libraries. Addressing these limitations, we have developed the Substrate-product Pair-based Enzyme Promiscuity Prediction (SPEPP) model. This innovative approach utilizes transfer learning and transformer architecture to predict enzyme promiscuity, thereby elucidating the intricate interplay between enzymes and substrate-product pairs. SPEPP exhibited robust predictive ability, eliminating the need for prior knowledge of reactions and allowing users to define their own candidate-enzyme libraries. It can be seamlessly integrated into various applications, including metabolic engineering, de novo pathway design, and hazardous material degradation. To better assist metabolic engineers in designing and refining biochemical pathways, particularly those without programming skills, we also designed EnzyPick, an easy-to-use web server for enzyme screening based on SPEPP. EnzyPick is accessible at http://www.biosynther.com/enzypick/.
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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.