ArticleBriefings in bioinformatics2024
MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.
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 22 papers.
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Who cites it
22 citing papers in PubMed.
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- Chemical neighborhood exploration for substrate discovery in biocatalysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction.Molecular diversity · 2026Article
- CACLENS: A Multitask Deep Learning System for Enzyme Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework.Frontiers in artificial intelligence · 2026Article
- Deep Learning-Based Prediction of Enzyme Optimal pH and Design of Point Mutations to Improve Acid Resistance.ACS synthetic biology · 2025Article
- Structure-Based Pipeline for Plant Enzymes: Pilot Study Identifying Novel Ginsenoside Biosynthetic UGTs.Biotech (Basel (Switzerland)) · 2025Article
- Mapping multi-substrate specificity of Arabidopsis aminotransferases.Nature plants · 2025Article
- A structure-oriented kinetics dataset of enzyme-substrate interactions.Scientific data · 2025Article
- Fermi calculations enable quick downselection of target genes and process optimization in photosynthetic systems.Plant physiology · 2025Review
- NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.Briefings in bioinformatics · 2025Article
- IECata: interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes.Briefings in bioinformatics · 2025Article
- SELFprot: Effective and Efficient Multitask Finetuning Methods for Protein Parameter Prediction.Journal of chemical information and modeling · 2025Article
- Robust enzyme discovery and engineering with deep learning using CataPro.Nature communications · 2025Article
- Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature.Briefings in bioinformatics · 2025Article
- RBC-GEM: A genome-scale metabolic model for systems biology of the human red blood cell.PLoS computational biology · 2025Article
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9 authors.
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Abstract
Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameters for measuring enzyme catalytic efficiency, are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km and further improve the prediction performance, we propose a universal pretrained multitask deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. Through testing on the same kcat and Km test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting kcat, improving ca. 14.6% and 7.6% compared to the DLKcat and UniKP models, and it achieved the Pearson coefficient of 0.777 for predicting Km, improving ca. 34.9% and 53.3% compared to the Kroll_model and UniKP models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown that MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model, which can be accessed at http://mathtc.nscc-tj.cn/mpek.
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