Predicting target-ligand interactions using protein ligand-binding site and ligand substructures

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• 作者：Caihua Wang (3)
  Juan Liu (3)
  Fei Luo (3)
  Zixing Deng (4)
  Qian-Nan Hu (4)
  
  3. School of Computer ; Wuhan University ; 430072 ; Wuhan ; PR China
  4. Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education) and Department of Pharmaceutical Sciences ; 430071 ; Wuhan ; PR China
  
• 刊名：BMC Systems Biology
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：9
• 期：1-supp
• 全文大小：3,529 KB
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• 刊物主题：Bioinformatics; Systems Biology; Simulation and Modeling; Computational Biology/Bioinformatics; Physiological, Cellular and Medical Topics; Algorithms;
• 出版者：BioMed Central
• ISSN：1752-0509

文摘

Background Cell proliferation, differentiation, Gene expression, metabolism, immunization and signal transduction require the participation of ligands and targets. It is a great challenge to identify rules governing molecular recognition between chemical topological substructures of ligands and the binding sites of the targets. Methods We suppose that the ligand-target interactions are determined by ligand substructures as well as the physical-chemical properties of the binding sites. Therefore, we propose a fragment interaction model (FIM) to describe the interactions between ligands and targets, with the purpose of facilitating the chemical interpretation of ligand-target binding. First we extract target-ligand complexes from sc-PDB database, based on which, we get the target binding sites and the ligands. Then we represent each binding site as a fragment vector based on a target fragment dictionary that is composed of 199 clusters (denoted as fragements in this work) obtained by clustering 4200 trimers according to their physical-chemical properties. And then, we represent each ligand as a substructure vector based on a dictionary containing 747 substructures. Finally, we build the FIM by generating the interaction matrix M (representing the fragment interaction network), and the FIM can later be used for predicting unknown ligand-target interactions as well as providing the binding details of the interactions. Results The five-fold cross validation results show that the proposed model can get higher AUC score (92%) than three prevalence algorithms CS-PD (80%), BLM-NII (85%) and RF (85%), demonstrating the remarkable predictive ability of FIM. We also show that the ligand binding sites (local information) overweight the sequence similarities (global information) in ligand-target binding, and introducing too much global information would be harmful to the predictive ability. Moreover, The derived fragment interaction network can provide the chemical insights on the interactions. Conclusions The target and ligand bindings are local events, and the local information dominate the binding ability. Though integrating of the global information can promote the predictive ability, the role is very limited. The fragment interaction network is helpful for understanding the mechanism of the ligand-target interaction.