{"title":"基于受体的3D-QSAR方法寻找柔性相似结合位点的选择性特征:以MMP-12/MMP-13为例","authors":"F. Hadizadeh, Jamal Shamsara","doi":"10.1504/IJBRA.2015.070139","DOIUrl":null,"url":null,"abstract":"Design of selective matrix metalloproteinases (MMPs) inhibitors is still a challenging task because of binding pocket similarities and flexibility among MMPs family. To overcome this issue we try to generate a (three-dimensional quantitative structure activity relationship) 3D-QSAR model that might reflect, at least in part, the differential properties of MMP-12 and MMP-13 active sites compared to each other. The different alignment rules were applied for CoMFA/CoMSIA model development. In an approach the best docked poses were followed by alignment based on their zinc binding group. As it was suggested by comparison of CoMSIA contour maps of MMP-12 with MMP-13, the ligand based approach can find more detailed features of specificity for MMPs that have similar highly flexible active sites, than solely analysis of available crystal structures. The residues Val(194), Leu(214) and Thr(220) of MMP-13 were suggested to be investigated for flexibility upon binding of different ligands.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.070139","citationCount":"8","resultStr":"{\"title\":\"Receptor-based 3D-QSAR approach to find selectivity features of flexible similar binding sites: case study on MMP-12/MMP-13\",\"authors\":\"F. Hadizadeh, Jamal Shamsara\",\"doi\":\"10.1504/IJBRA.2015.070139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Design of selective matrix metalloproteinases (MMPs) inhibitors is still a challenging task because of binding pocket similarities and flexibility among MMPs family. To overcome this issue we try to generate a (three-dimensional quantitative structure activity relationship) 3D-QSAR model that might reflect, at least in part, the differential properties of MMP-12 and MMP-13 active sites compared to each other. The different alignment rules were applied for CoMFA/CoMSIA model development. In an approach the best docked poses were followed by alignment based on their zinc binding group. As it was suggested by comparison of CoMSIA contour maps of MMP-12 with MMP-13, the ligand based approach can find more detailed features of specificity for MMPs that have similar highly flexible active sites, than solely analysis of available crystal structures. The residues Val(194), Leu(214) and Thr(220) of MMP-13 were suggested to be investigated for flexibility upon binding of different ligands.\",\"PeriodicalId\":35444,\"journal\":{\"name\":\"International Journal of Bioinformatics Research and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1504/IJBRA.2015.070139\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Bioinformatics Research and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJBRA.2015.070139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Health Professions\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Bioinformatics Research and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJBRA.2015.070139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Health Professions","Score":null,"Total":0}
Receptor-based 3D-QSAR approach to find selectivity features of flexible similar binding sites: case study on MMP-12/MMP-13
Design of selective matrix metalloproteinases (MMPs) inhibitors is still a challenging task because of binding pocket similarities and flexibility among MMPs family. To overcome this issue we try to generate a (three-dimensional quantitative structure activity relationship) 3D-QSAR model that might reflect, at least in part, the differential properties of MMP-12 and MMP-13 active sites compared to each other. The different alignment rules were applied for CoMFA/CoMSIA model development. In an approach the best docked poses were followed by alignment based on their zinc binding group. As it was suggested by comparison of CoMSIA contour maps of MMP-12 with MMP-13, the ligand based approach can find more detailed features of specificity for MMPs that have similar highly flexible active sites, than solely analysis of available crystal structures. The residues Val(194), Leu(214) and Thr(220) of MMP-13 were suggested to be investigated for flexibility upon binding of different ligands.
期刊介绍:
Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.