Deepenz: prediction of enzyme classification by deep learning

<span>Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests show the effectiveness of this approach. The combination of these two tools give a model with a great capacity to extract knowledge from enzyme data to predict and classify them. The proposed model learns through intensive training by exploiting enzyme sequences. This work highlights the contribution of this approach to improve the precision of enzyme classification.</span>

Alignment-Free Method to Predict Enzyme Classes and Subclasses

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 However, due to this new change, all such methods are now outdated and need updating.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.