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Computational Approaches to Identify Molecules Binding to Mycobacterium tuberculosis KasA.

ABSTRACT: Tuberculosis is caused by Mycobacterium tuberculosis (Mtb) and is a deadly disease resulting in the deaths of approximately 1.5 million people with 10 million infections reported in 2018. Recently, a key condensation step in the synthesis of mycolic acids was shown to require ?-ketoacyl-ACP synthase (KasA). A crystal structure of KasA with the small molecule DG167 was recently described, which provided a starting point for using computational structure-based approaches to identify additional molecules binding to this protein. We now describe structure-based pharmacophores, docking and machine learning studies with Assay Central as a computational tool for the identification of small molecules targeting KasA. We then tested these compounds using nanoscale differential scanning fluorimetry and microscale thermophoresis. Of note, we identified several molecules including the Food and Drug Administration (FDA)-approved drugs sildenafil and flubendazole with K _d values between 30-40 ?M. This may provide additional starting points for further optimization.

SUBMITTER: Puhl AC

PROVIDER: S-EPMC7689923 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Computational Approaches to Identify Molecules Binding to <i>Mycobacterium tuberculosis</i> KasA.

Puhl Ana C AC Lane Thomas R TR Vignaux Patricia A PA Zorn Kimberley M KM Capodagli Glenn C GC Neiditch Matthew B MB Freundlich Joel S JS Ekins Sean S

ACS omega 20201115 46

Tuberculosis is caused by <i>Mycobacterium tuberculosis (Mtb)</i> and is a deadly disease resulting in the deaths of approximately 1.5 million people with 10 million infections reported in 2018. Recently, a key condensation step in the synthesis of mycolic acids was shown to require β-ketoacyl-ACP synthase (KasA). A crystal structure of KasA with the small molecule DG167 was recently described, which provided a starting point for using computational structure-based approaches to identify additio ...[more]

PMID: 33251429

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Project description:We report GSK3011724A (DG167) as a binary inhibitor of β-ketoacyl-ACP synthase (KasA) in Mycobacterium tuberculosis Genetic and biochemical studies established KasA as the primary target. The X-ray crystal structure of the KasA-DG167 complex refined to 2.0-Å resolution revealed two interacting DG167 molecules occupying nonidentical sites in the substrate-binding channel of KasA. The binding affinities of KasA to DG167 and its analog, 5g, which binds only once in the substrate-binding channel, were determined, along with the KasA-5g X-ray crystal structure. DG167 strongly augmented the in vitro activity of isoniazid (INH), leading to synergistic lethality, and also synergized in an acute mouse model of M. tuberculosis infection. Synergistic lethality correlated with a unique transcriptional signature, including upregulation of oxidoreductases and downregulation of molecular chaperones. The lead structure-activity relationships (SAR), pharmacokinetic profile, and detailed interactions with the KasA protein that we describe may be applied to evolve a next-generation therapeutic strategy for tuberculosis (TB).IMPORTANCE Cell wall biosynthesis inhibitors have proven highly effective for treating tuberculosis (TB). We discovered and validated members of the indazole sulfonamide class of small molecules as inhibitors of Mycobacterium tuberculosis KasA-a key component for biosynthesis of the mycolic acid layer of the bacterium's cell wall and the same pathway as that inhibited by the first-line antitubercular drug isoniazid (INH). One lead compound, DG167, demonstrated synergistic lethality in combination with INH and a transcriptional pattern consistent with bactericidality and loss of persisters. Our results also detail a novel dual-binding mechanism for this compound as well as substantial structure-activity relationships (SAR) that may help in lead optimization activities. Together, these results suggest that KasA inhibition, specifically, that shown by the DG167 series, may be developed into a potent therapy that can synergize with existing antituberculars.

Dataset Information

Computational Approaches to Identify Molecules Binding to Mycobacterium tuberculosis KasA.

Publications

Computational Approaches to Identify Molecules Binding to <i>Mycobacterium tuberculosis</i> KasA.

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