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Identification of novel inhibitors for mycobacterial polyketide synthase 13 via in silico drug screening assisted by the parallel compound screening with genetic algorithm-based programs

The Journal of Antibiotics volume 75, pages 552–558 (2022)Cite this article

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Abstract

Identifying small compounds capable of inhibiting Mycobacterium tuberculosis polyketide synthase 13 (Pks13), in charge of final step of mycolic acid biosynthesis, could lead to the development of a novel antituberculosis drug. This study screened for lead compounds capable of targeting M. tuberculosis Pks13 from a chemical library comprising 154,118 compounds through multiple in silico docking simulations. The parallel compound screening (PCS), conducted via two genetic algorithm-based programs was applied in the screening strategy. Out of seven experimentally validated compounds, four compounds showed inhibitory effects on the growth of the model mycobacteria (Mycobacterium smegmatis). Subsequent docking simulation of analogs of the promising leads with the assistance of PCS resulted in the identification of three additional compounds with potent antimycobacterial effects (compounds A1, A2, and A5). Further, molecular dynamics simulation predicted stable interaction between M. tuberculosis Pks13 active site and compound A2, which showed potent antimycobacterial activity comparable to that of isoniazid. The present study demonstrated the efficacy of in silico structure-based drug screening through PCS in antituberculosis drug discovery.

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Funding

This work was supported in part by a grant from Takeda Science Foundation to JT and a Grant-in-Aid for Scientific Research (C) (26460145) to SA and a Grant-in-Aid for Transformative Research Areas (A) (21H05887) to JT from Japan Society for the Promotion of Science.

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Authors and Affiliations

  1. Department of Bioscience and Bioinformatics, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, 820-8502, Japan

    Junichi Taira, Kahori Murakami, Kohei Monobe, Kohei Kuriki, Miu Fujita, Yosuke Ochi, Hiroshi Sakamoto & Shunsuke Aoki

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  1. Junichi Taira
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  2. Kahori Murakami
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  3. Kohei Monobe
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  4. Kohei Kuriki
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  8. Shunsuke Aoki
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Correspondence to Shunsuke Aoki.

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Taira, J., Murakami, K., Monobe, K. et al. Identification of novel inhibitors for mycobacterial polyketide synthase 13 via in silico drug screening assisted by the parallel compound screening with genetic algorithm-based programs. J Antibiot 75, 552–558 (2022). https://doi.org/10.1038/s41429-022-00549-z

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