A GU-Net-based architecture predicting ligand–Protein-binding atoms

Fatemeh Nazem; Fahimeh Ghasemi; Afshin Fassihi; Reza Rasti; Alireza Mehri Dehnavi

doi:10.4103/jmss.jmss_142_21

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ORIGINAL ARTICLE

Year : 2023 \| Volume : 13 \| Issue : 1 \| Page : 1-10

A GU-Net-based architecture predicting ligand–Protein-binding atoms Fatemeh Nazem¹, Fahimeh Ghasemi², Afshin Fassihi³, Reza Rasti⁴, Alireza Mehri Dehnavi⁵ ¹ Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences; Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran ² Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran ³ Department of Medicinal Chemistry, School of Pharmacology and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran ⁴ Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran ⁵ Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran Correspondence Address: Alireza Mehri Dehnavi Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan Iran Source of Support: None, Conflict of Interest: None DOI: 10.4103/jmss.jmss_142_21

Background: The first step in developing new drugs is to find binding sites for a protein structure that can be used as a starting point to design new antagonists and inhibitors. The methods relying on convolutional neural network for the prediction of binding sites have attracted much attention. This study focuses on the use of optimized neural network for three-dimensional (3D) non-Euclidean data. Methods: A graph, which is made from 3D protein structure, is fed to the proposed GU-Net model based on graph convolutional operation. The features of each atom are considered as attributes of each node. The results of the proposed GU-Net are compared with a classifier based on random forest (RF). A new data exhibition is used as the input of RF classifier. Results: The performance of our model is also examined through extensive experiments on various datasets from other sources. GU-Net could predict the more number of pockets with accurate shape than RF. Conclusions: This study will enable future works on a better modeling of protein structures that will enhance knowledge of proteomics and offer deeper insight into drug design process.



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