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1.
J Chem Inf Model ; 62(24): 6508-6518, 2022 12 26.
Article in English | MEDLINE | ID: mdl-35994014

ABSTRACT

Mycobacterium tuberculosis protein kinase B (PknB) is essential to mycobacterial growth and has received considerable attention as an attractive target for novel anti-tuberculosis drug development. Here, virtual screening, validated by biological assays, was applied to select candidate inhibitors of M. tuberculosis PknB from the Specs compound library (www.specs.net). Fifteen compounds were identified as hits and selected for in vitro biological assays, of which three indoles (2, AE-848/42799159; 4, AH-262/34335013; 10, AP-124/40904362) inhibited growth of M. tuberculosis H37Rv with minimal inhibitory concentrations of 6.2, 12.5, and 6.2 µg/mL, respectively. Two compounds, 2 and 10, inhibited M. tuberculosis PknB activity in vitro, with IC50 values of 14.4 and 12.1 µM, respectively, suggesting this to be the likely basis of their anti-tubercular activity. In contrast, compound 4 displayed anti-tuberculosis activity against M. tuberculosis H37Rv but showed no inhibition of PknB activity (IC50 > 128 µM). We hypothesize that hydrolysis of its ethyl ester to a carboxylate moiety generates an active species that inhibits other M. tuberculosis enzymes. Molecular dynamics simulations of modeled complexes of compounds 2, 4, and 10 bound to M. tuberculosis PknB indicated that compound 4 has a lower affinity for M. tuberculosis PknB than compounds 2 and 10, as evidenced by higher calculated binding free energies, consistent with experiment. Compounds 2 and 10 therefore represent candidate inhibitors of M. tuberculosis PknB that provide attractive starting templates for optimization as anti-tubercular agents.


Subject(s)
Mycobacterium tuberculosis , Tuberculosis , Humans , Proto-Oncogene Proteins c-akt/metabolism , Antitubercular Agents/pharmacology , Antitubercular Agents/chemistry , Tuberculosis/drug therapy , Phosphorylation
2.
J Chem Inf Model ; 60(1): 226-234, 2020 01 27.
Article in English | MEDLINE | ID: mdl-31820972

ABSTRACT

The enoyl-acyl carrier protein reductase InhA of Mycobacterium tuberculosis is an attractive, validated target for antituberculosis drug development. Moreover, direct inhibitors of InhA remain effective against InhA variants with mutations associated with isoniazid resistance, offering the potential for activity against MDR isolates. Here, structure-based virtual screening supported by biological assays was applied to identify novel InhA inhibitors as potential antituberculosis agents. High-speed Glide SP docking was initially performed against two conformations of InhA differing in the orientation of the active site Tyr158. The resulting hits were filtered for drug-likeness based on Lipinski's rule and avoidance of PAINS-like properties and finally subjected to Glide XP docking to improve accuracy. Sixteen compounds were identified and selected for in vitro biological assays, of which two (compounds 1 and 7) showed MIC of 12.5 and 25 µg/mL against M. tuberculosis H37Rv, respectively. Inhibition assays against purified recombinant InhA determined IC50 values for these compounds of 0.38 and 0.22 µM, respectively. A crystal structure of the most potent compound, compound 7, bound to InhA revealed the inhibitor to occupy a hydrophobic pocket implicated in binding the aliphatic portions of InhA substrates but distant from the NADH cofactor, i.e., in a site distinct from those occupied by the great majority of known InhA inhibitors. This compound provides an attractive starting template for ligand optimization aimed at discovery of new and effective compounds against M. tuberculosis that act by targeting InhA.


Subject(s)
Antitubercular Agents/chemistry , Bacterial Proteins/antagonists & inhibitors , Mycobacterium tuberculosis/drug effects , Oxidoreductases/antagonists & inhibitors , Antitubercular Agents/pharmacology , Binding Sites , Crystallography, X-Ray , Drug Discovery , Microbial Sensitivity Tests , Molecular Docking Simulation , Molecular Structure , Reproducibility of Results , Structure-Activity Relationship
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