ArticleInternational journal of molecular sciences2019
Alignment-Free Method to Predict Enzyme Classes and Subclasses.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 32 citations in OpenAlex.
- Exploring the enzymatic repertoires of Bacteria and Archaea and their associations with metabolic maps.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2024Article
- Purification of polyphenol oxidase from tea (Food chemistry: X · 2024Article
- GloEC: a hierarchical-aware global model for predicting enzyme function.Briefings in bioinformatics · 2024Article
- MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.Molecules (Basel, Switzerland) · 2023Article
- Enzyme Promiscuity Prediction Using Hierarchy-Informed Multi-Label Classification.Bioinformatics (Oxford, England) · 2021Article
- Enzyme Models-From Catalysis to Prodrugs.Molecules (Basel, Switzerland) · 2021Review
- The Role of Gene Duplication in the Divergence of Enzyme Function: A Comparative Approach.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.
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Registered trials
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