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ORIGINAL ARTICLE
Year : 2021  |  Volume : 10  |  Issue : 3  |  Page : 307-311

Computer-assisted screening of mycobacterial growth inhibitors: Exclusion of frequent hitters with the assistance of the multiple target screening method


Department of Bioscience and Bioinformatics, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan

Correspondence Address:
Shunsuke Aoki
Department of Bioscience and Bioinformatics, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka 820-8502
Japan
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmy.ijmy_137_21

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Background: The emergence of frequent hitters (FHs) remains a challenge in drug discovery. We have previously used in silico structure-based drug screening (SBDS) to identify antimycobacterial candidates. However, excluding FHs has not been integrated into the SBDS system. Methods: A dataset comprising 15,000 docking score (protein–compound affinity matrix) was constructed by multiple target screening (MTS): DOCK–GOLD two-step docking simulations with 154,118 compounds versus the 30 target proteins essential for mycobacterial survival. After extraction of 141 compounds from the protein–compound affinity matrix, compounds determined to be FHs or false positives were excluded. Antimycobacterial properties of the top nine compounds selected through SBDS were experimentally evaluated. Results: Nine compounds designated KS1–KS9 were selected for experimental evaluation. Among the selected compounds, KS3, identified as adenosylhomocysteinase inhibitor, showed a potent inhibitory effect on antimycobacterial growth (inhibitory concentration [IC]50 = 1.2 M). However, the compound also showed potent cytotoxicity. Conclusion: The MTS method is applicable in SBDS for the identification of enzyme-specific inhibitors.


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