Structure-based identification of non-covalent prolyl oligopeptidase 80 inhibitors targeting Trypanosoma cruzi cell entry
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Chagas disease remains a persistent public health challenge due to the limited efficacy and significant toxicity of
current pharmacological treatments. This highlights the urgent need for novel drugs with innovative mechanisms of action,
specifically targeting cell infection pathways. The prolyl oligopeptidase of Trypanosoma cruzi (POPTc80) has emerged as a
promising target for developing inhibitors to block the parasite’s infection process. In this study, we developed a robust structurebased virtual screening pipeline to discover potent POPTc80 inhibitors. The customized protocol integrated structural analysis of
the 3D structure of POPTc80 and enrichment analysis of molecular docking and shape-based models to optimize the selection of
potential inhibitors. After optimization, a large-scale virtual screening of 1.3 million compounds prioritized 19 putative hits for
experimental validation. Nine of these compounds demonstrated inhibitory activity at nanomolar concentrations. The most potent
inhibitors�LC-44 (Ki = 0.175 μM), LC-45 (Ki = 0.054 μM), LC-46 (Ki = 0.513 μM), LC-50 (Ki = 0.44 μM), LC-53 (Ki = 0.158
μM), and LC-55 (Ki = 0.83 μM)�demonstrated superior inhibitory activity, consistent with the competitive inhibition mechanism
predicted by our computational protocol. Subsequently, a phenotypic assay confirmed their ability to effectively inhibit T. cruzi entry
into host cells in a dose-dependent manner, further validating their mechanism of action. These findings establish these compounds
as promising chemical scaffolds for prospective hit-to-lead optimization, offering a unique opportunity to develop novel, mechanismdriven therapeutics targeting a critical step in the parasite’s infection process.
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COSTA, Vinícius Alexandre Fiaia et al. Structure-based identification of non-covalent prolyl oligopeptidase 80 inhibitors targeting Trypanosoma cruzi cell entry. Journal of Chemical Information and Modeling, Washington, v. 65, n. 5, p. 2636-2649, 2025. DOI: 10.1021/acs.jcim.4c02152. Disponível em: https://pubs.acs.org/doi/10.1021/acs.jcim.4c02152. Acesso em: 18 set. 2025.