Molecular modeling studies of Escherichia coli MurA towards novel Mur enzyme inhibitors against resistant bacterial strains
Yao, Tianran (2016-12-21)
Molecular modeling studies of Escherichia coli MurA towards novel Mur enzyme inhibitors against resistant bacterial strains
Yao, Tianran
(21.12.2016)
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Turun yliopisto
Kuvaus
Siirretty Doriasta
Tiivistelmä
Currently more and more antibiotics are put into clinical use; meanwhile bacteria also develop diverse antibiotic resistance. MurA, a key enzyme on cell wall precursor biosynthesis pathway, was effectively inhibited by a broad-spectrum antibiotic fosfomycin. However a serial of antibiotic-resistance pathogenic bacteria such as Mycobacterium tuberculosis have developed fosfomycin-resistance by mutating the cysteine residue at the active site of MurA enzyme into an aspartate residue. This could be impairment to public health.
Nowadays ordinary experimental methods on drug design are very resource and time-consuming processes. The demand for faster, safer and more effective in silico methods is growing. Hereby we use computer-aided drug design such as sequence analysis and molecular dynamics simulation to study E.coli MurA molecule model to reveal the binding patterns for a known E.coli MurA inhibitor T6361. Applying such information into virtual docking and a pharmacophore model screening, we are able to retrieve 22 potential inhibitory compounds from National Cancer Institute (NCI) Diversity Set III compound database, which contains 1568 candidates. This will definitely narrow down the experimental searching range and facilitate the laboratory work. Those 22 potential inhibitory compounds will be further verified and tested by in vitro experiments.
Nowadays ordinary experimental methods on drug design are very resource and time-consuming processes. The demand for faster, safer and more effective in silico methods is growing. Hereby we use computer-aided drug design such as sequence analysis and molecular dynamics simulation to study E.coli MurA molecule model to reveal the binding patterns for a known E.coli MurA inhibitor T6361. Applying such information into virtual docking and a pharmacophore model screening, we are able to retrieve 22 potential inhibitory compounds from National Cancer Institute (NCI) Diversity Set III compound database, which contains 1568 candidates. This will definitely narrow down the experimental searching range and facilitate the laboratory work. Those 22 potential inhibitory compounds will be further verified and tested by in vitro experiments.