加尔各答NDM-1菌株的结构、演变和虚拟筛选。

Q4 Health Professions International Journal of Bioinformatics Research and Applications Pub Date : 2014-01-01 DOI:10.1504/IJBRA.2014.060761

Ganesh Chandra Sahoo, Mukta Rani, Yousuf Ansari, Chanda Jha, Sindhuprava Rana, Manas Ranjan Dikhit, Kanhu Charan Moharana, Rakesh Kumar, Pradeep Das

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引用次数: 6

摘要

β-内酰胺类抗生素用于治疗细菌感染。β-内酰胺酶(EC 3.5.2.6)由几种细菌产生，并负责它们对青霉素、头孢菌素和碳青霉烯类β-内酰胺类抗生素的耐药性。新德里金属β-内酰胺酶(NDM-1)是一种使细菌对β-内酰胺类抗生素产生耐药性的基因。制备抗NDM-1的化合物需要药品制造商额外的投资和开发，因为目前的抗生素不能治疗耐NDM-1的患者。加尔各答菌株NDM-1与其他产NDM-1菌株呈趋同型进化。模型结构为α-β-α桶型结构域和Zn金属-β-内酰胺酶n端结构域。头孢菌素类化合物(对β-内酰胺酶相对耐药)和其他抗生素头孢他林、头孢双prole、哌拉西林、青霉素、氮多西林、头孢菌素、替加环素和粘菌素与模型NDM-1表现出较好的结合亲和力。

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Structure, evolution and virtual screening of NDM-1 strain from Kolkata.

β-lactam antibiotics are utilised to treat bacterial infection. β-lactamase enzymes (EC 3.5.2.6) are produced by several bacteria and are responsible for their resistance to β-lactam antibiotics like penicillin, cephamycins and carbapenems. New Delhi Metallo-β-lactamase (NDM-1) is a gene that makes bacteria resistant to β-lactam antibiotics. Preparing a compound against NDM-1 will require additional investment and development by drug manufacturers as the current antibiotics will not treat patients with NDM-1 resistance. NDM-1 of Kolkata showed convergent-type evolution with other NDM-1 producing strains. The modelled structure exhibited α-β-α barrel-type domain along with Zn metallo-β-lactamase N-terminal domain. Compounds belonging to cephalosporins (relatively resistant to β-lactamase) and other antibiotics ceftaroline, ceftobiprole, piperacillin, penamecillin, azidocillin, cefonicid, tigecycline and colistin have exhibited better binding affinity with the modelled NDM-1.

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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.