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

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2015-06-01 DOI:10.1504/IJBRA.2015.070139

F. Hadizadeh, Jamal Shamsara

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引用次数: 8

摘要

选择性基质金属蛋白酶(MMPs)抑制剂的设计仍然是一个具有挑战性的任务，因为MMPs家族之间的结合口袋相似性和灵活性。为了克服这个问题，我们试图生成一个(三维定量结构活性关系)3D-QSAR模型，该模型至少部分反映了MMP-12和MMP-13活性位点相互比较的差异性质。在CoMFA/CoMSIA模型开发中应用了不同的对齐规则。在一种方法中，最佳停靠姿势之后是基于锌结合组的对齐。通过比较CoMSIA对MMP-12和MMP-13的等高线图表明，基于配体的方法可以发现具有相似高度柔性活性位点的MMPs的特异性更详细的特征，而不是仅仅分析可用的晶体结构。建议研究MMP-13的残基Val(194)、Leu(214)和Thr(220)在不同配体结合时的柔韧性。

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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.

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

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.