ArticleJournal, genetic engineering & biotechnology2025
In-silico, in-vitro, and proteomics analyses on repurposed drugs in targeting the small GTPase, Rho subfamily protein (Rho GTPase), and putative Rho GTPase-activating protein (RhoGAP) of Giardia lamblia.
Article in Journal, genetic engineering & biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Giardia lamblia is a globally prevalent protozoan responsible for Giardiasis, an intestinal disease commonly treated with nitroimidazoles such as metronidazole, tinidazole, and albendazole. However, increasing drug resistance has reduced treatment efficacy. Rho GTPase and its regulator, putative Rho GTPase-activating protein (RhoGAP), play crucial roles in Giardia's encystation, membrane trafficking, and metabolism. This study explores these proteins as potential drug targets. We employed sequence-based target prediction, molecular docking, molecular dynamics (MD) simulations, minimal inhibitory concentration (MIC) assays, and label-free quantitative proteomics (LFQ) to identify and validate potential inhibitors. Target sequence analysis revealed Dextromethorphan and Azathioprine as candidate Rho GTPase inhibitors, and Imatinib, Dasatinib, and Ponatinib as RhoGAP inhibitors. Molecular docking using AutoDock Vina yielded binding energies ranging from -5.3 to -8.5 kcal/mol. MD simulations via GROMACS confirmed structural compactness and complex stability. In vitro MIC assays demonstrated that Dasatinib and Imatinib inhibited RhoGAP at concentrations of 12.5 µM and 100 µM, respectively, while azathioprine inhibited Rho GTPase at 500 µM. Dextromethorphan showed no inhibitory activity. LFQ proteomic analysis further confirmed Dasatinib's significant impact on protein expression profiles. These findings highlight RhoGAP as a promising therapeutic target in G. lamblia, with Dasatinib showing potential as a repurposed treatment for Giardiasis. Proteomic data are publicly available in the MASSIVE database under identifier MSV000097321.
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