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.

中文翻译：

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通过蛋白质结构预测和配体对接模拟发现潜在药物的用户指南

由于积累了蛋白质结构信息和计算方法的进步，现在预测蛋白质-化合物相互作用成为可能。在生物学中，药物发现的经典策略是手动筛选多种化合物（小规模）以鉴定潜在的药物化合物。最近的策略已经利用了计算药物发现方法，这些方法涉及预测靶蛋白结构，鉴定活性位点以及大规模发现潜在的抑制剂化合物。在此协议文章中，我们介绍了计算机药物发现协议。由于病原菌的多药耐药性仍然是一个具有挑战性的问题，因此，UDP-N-乙酰村氨酸-L-丙氨酸连接酶（murC）以鲍曼不动杆菌为例，它在医院中引起医院内感染，并导致全世界的高死亡率。该协议应有助于微生物学家扩大其知识和研究范围。