School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
J Microbiol. 2020 Mar;58(3):235-244. doi: 10.1007/s12275-020-9563-z. Epub 2020 Feb 27.
Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.
由于蛋白质结构信息的积累和计算方法的进步,现在已经可以预测蛋白质-化合物的相互作用。在生物学中,药物发现的经典策略一直是手动筛选多种化合物(小规模)来识别潜在的药物化合物。最近的策略利用了计算药物发现方法,包括预测靶标蛋白结构、识别活性位点和在大规模上寻找潜在的抑制剂化合物。在本方案文章中,我们介绍了一种计算机药物发现方案。由于致病细菌的多药耐药性仍然是一个难以解决的问题,因此选择鲍曼不动杆菌的 UDP-N-乙酰胞壁酸-L-丙氨酸连接酶(murC)作为一个例子,它在医院环境中引起医院感染,并在全球范围内导致高死亡率。本方案应有助于微生物学家扩展他们的知识和研究范围。