Abstract
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.
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Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (No. NRF-2018R1A5A1025077 and No. NRF-2019M3-E5D4065682). This work was also supported by the National Research Foundation of Korea (NRF) and the Center for Women in Science, Engineering and Technology (WISET) Grant funded by the Ministry of Science and ICT (MSIT) under the Program for Returners into R&D.
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Shaker, B., Yu, MS., Lee, J. et al. User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation. J Microbiol. 58, 235–244 (2020). https://doi.org/10.1007/s12275-020-9563-z
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DOI: https://doi.org/10.1007/s12275-020-9563-z