PUBLICATION

Combating Drug Resistance in Mycobacterium Tuberculosis: A Combinatorial in Silico and Experimental Modeling Approach Toward Novel ATP Synthase Inhibitor Discovery

Authors
El-Zoheiry, H.H., Bhowmik, R., Manaithiya, A., Ray, R., Samantaray, M., Ramaswamy, A., Aspatwar, A.
ID
ZDB-PUB-260414-4
Date
2026
Source
Bioinformatics and biology insights   20: 11779322261438313 (Journal)
Registered Authors
Keywords
QSAR, Structure-based pharmacophore modeling, antimycobacterial inhibition assay, machine learning, molecular docking, molecular dynamics simulations, toxicity studies
MeSH Terms
none
PubMed
41971764 Full text @ Bioinform. Biol. Insights
Abstract
Adenosine triphosphate (ATP) synthase in Mycobacterium tuberculosis (Mtb) is essential for energy metabolism through oxidative phosphorylation, where ATP is synthesized from ADP. This enzyme supports bacterial survival during both active growth and dormancy, enabling Mtb to persist under stressful conditions. During dormancy, Mtb enters a non-replicating, drug-tolerant state that reduces the effectiveness of many antibiotics. Inhibition of ATP synthase therefore disrupts ATP-dependent survival mechanisms in Mtb. Although this target has been clinically validated by bedaquiline, the emergence of resistance and the limited chemical diversity of reported inhibitors indicate a clear need for new ATP synthase-targeting compounds. In this study, we employed an integrative pipeline combining structure-based pharmacophore modeling, artificial neural network-driven quantitative structure-activity relationship (ANN-QSAR) modeling, and absorption distribution metabolism excretion and toxicity (ADMET)-based pharmacokinetic filtering strategies to screen an antituberculosis-targeted library of approximately 4200 molecules from the Life Chemicals database. Initial screening identified 8 hit molecules characterized by key molecular features previously highlighted as positive contributors in both Shapley Additive Explanations (SHAP) and Pearson correlation analyses, including SubFP1 (primary carbon), SubFP88 (carboxylic acid derivative), SubFP143 (carbonic acid derivative), SubFP9 (alkyl fluoride), SubFP137 (vinylogous ester), SubFP184 (heteroaromatic), SubFP26 (tertiary aliphatic amine), and SubFP171 (aryl chloride). Molecular docking and molecular dynamics simulation studies (200 ns) further highlighted molecules F0526-1306 and F0526-1309 as the most promising candidates. Subsequent antimycobacterial inhibition assays demonstrated that both molecules significantly reduced mycobacterial biofilm formation. In addition, toxicity evaluations using a zebrafish model confirmed the safety and favorable tolerability of these molecules, supporting their potential as viable candidates for further preclinical and in vivo drug development studies.
Genes / Markers
Figures
Show all Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping