ArticlePeerJ2017
Automatic single- and multi-label enzymatic function prediction by machine learning.
Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Novel Unsupervised Learning Model with Bayesian Hyperparameter Optimization for Predicting Substrates of Promiscuous Enzymes.ACS omega · 2026Article
- Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis.JMIR medical informatics · 2024Article
- Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023Review
- Epitranscriptomics of cardiovascular diseases (Review).International journal of molecular medicine · 2022Review
- A hierarchical deep learning based approach for multi-functional enzyme classification.Protein science : a publication of the Protein Society · 2021Article
- Machine learning differentiates enzymatic and non-enzymatic metals in proteins.Nature communications · 2021Article
- Machine learning for enzyme engineering, selection and design.Protein engineering, design & selection : PEDS · 2021Review
- Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways.Frontiers in molecular biosciences · 2021Review
- New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review.Frontiers in bioengineering and biotechnology · 2021Review
- PSIONplusBiomolecules · 2020Article
- Structural Study of the DNA: Clock/Bmal1 Complex Provides Insights for the Role of Cortisol, hGR, and HPA Axis in Stress Management and Sleep Disorders.Advances in experimental medicine and biology · 2020Article
- Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation.International journal of molecular sciences · 2019Article
- mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning.Frontiers in genetics · 2018Article
- EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.PeerJ · 2018Article
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Authors and funding
5 authors.
Funding
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Abstract
The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing different reactions and can hence be directly associated with multiple enzymatic functions. In the present work, we propose a multi-label enzymatic function classification scheme that combines structural and amino acid sequence information. We investigate two fusion approaches (in the feature level and decision level) and assess the methodology for general enzymatic function prediction indicated by the first digit of the enzyme commission (EC) code (six main classes) on 40,034 enzymes from the PDB database. The proposed single-label and multi-label models predict correctly the actual functional activities in 97.8% and 95.5% (based on Hamming-loss) of the cases, respectively. Also the multi-label model predicts all possible enzymatic reactions in 85.4% of the multi-labeled enzymes when the number of reactions is unknown. Code and datasets are available at https://figshare.com/s/a63e0bafa9b71fc7cbd7.
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