没食子酸衍生物作为HIV-1蛋白酶抑制剂的对接分析

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2015-11-01 DOI:10.1504/IJBRA.2015.073239

Anjali Singh, T. Pal

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引用次数: 4

摘要

HIV-1蛋白酶(HIV-1 PR)酶对于感染性HIV逆转录病毒的准确组装和成熟至关重要。HIV-1蛋白酶在病毒复制中的重要作用使其成为潜在的药物靶点。近年来，植物化学没食子酸(GA)衍生物已被筛选为蛋白酶抑制剂活性。本工作旨在通过对接方法设计和评估潜在的基于ga的HIV-1 PR植物抑制剂。通过ChemDraw制备配体，并在HEX软件中进行对接。在本研究中，一种GA类似物(GA4)成为抑制HIV-1 PR的有效候选药物，对接结果显示其与抗hiv药物darunavir和amprenavir相当。GA4衍生物为设计更有效的HIV-1 PR抑制剂提供了先导。

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Docking analysis of gallic acid derivatives as HIV-1 protease inhibitors

HIV-1 Protease (HIV-1 PR) enzymes are essential for accurate assembly and maturation of infectious HIV retroviruses. The significant role of HIV-1 protease in viral replication has made it a potential drug target. In the recent past, phytochemical Gallic Acid (GA) derivatives have been screened for protease inhibitor activity. The present work aims to design and evaluate potential GA-based HIV-1 PR phytoinhibitors by docking approach. The ligands were prepared by ChemDraw and docking was performed in HEX software. In this present study, one of the GA analogues (GA4) emerged as a potent drug candidate for HIV-1 PR inhibition, and docking results showed it to be comparable with anti-HIV drugs, darunavir and amprenavir. The GA4 derivative provided a lead for designing more effective HIV-1 PR inhibitors.

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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.