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A Gu‑Net‑Based Architecture Predicting Ligand–Protein‑Binding Atoms Publisher



Nazem F1, 2 ; Ghasemi F2 ; Fassihi A3 ; Rasti R4 ; Dehnavi AM1, 5
Authors
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Authors Affiliations
  1. 1. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran
  2. 2. Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran
  3. 3. Department of Medicinal Chemistry, School of Pharmacology and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Iran
  4. 4. Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Iran
  5. 5. Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Medical Signals and Sensors Published:2023


Abstract

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. © 2023 Isfahan University of Medical Sciences(IUMS). All rights reserved.
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