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Open AccessArticle

Alignment-Free Method to Predict Enzyme Classes and Subclasses

[email protected]/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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Int. J. Mol. Sci. 2019, 20(21), 5389; https://doi.org/10.3390/ijms20215389
Received: 9 September 2019 / Revised: 21 October 2019 / Accepted: 23 October 2019 / Published: 29 October 2019
(This article belongs to the Special Issue QSAR and Chemoinformatics Tools for Modeling)
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 However, due to this new change, all such methods are now outdated and need updating.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. View Full-Text
Keywords: QSAR; machine learning; artificial neural network; enzyme; enzyme classification; alignment-free QSAR; machine learning; artificial neural network; enzyme; enzyme classification; alignment-free
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Concu, R.; Cordeiro, M.N.D.S. Alignment-Free Method to Predict Enzyme Classes and Subclasses. Int. J. Mol. Sci. 2019, 20, 5389.

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