In silico drug repurposing and in vitro validation of cestode fatty acid binding proteins
Rodriguez, S.; Alberca, L. N.; Gavernet, L.; Franchini, G. R.; Talevi, A.
Show abstract
Echinococcosis is a Neglected Tropical Disease (NTD) caused by Echinococcus granulosus and Echinococcus multilocularis, the etiological agents of cystic and alveolar echinococcosis, respectively. These infections pose a significant public health burden, particularly in endemic regions. Cestodes lack key enzymes involved in lipid metabolism and must acquire lipids from their hosts. Fatty Acid Binding Proteins (FABPs), which mediate lipid trafficking and intracellular transport, have therefore emerged as essential and potentially druggable targets. In this study, we implemented an integrated virtual screening strategy combining ligand-based and structure-based approaches to identify novel FABP binders as potential therapeutic agents against Echinococcus spp. High-specificity screening of approximately 435,000 compounds yielded a limited number of prioritized in silico hits. Four compounds--hydrochlorothiazide, naratriptan, fenticonazole, and montelukast--were selected for experimental validation, prioritizing repurposing candidates. Fluorescence displacement assays confirmed that hydrochlorothiazide binds to three cestode FABPs (EgFABP1, EmFABP1, and EmFABP3), validating the predictive performance of the computational workflow. These findings support the value of parallel in silico screening strategies and drug repurposing approaches for the discovery of new therapeutic candidates against neglected tropical diseases.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges 96%
- Structure-based identification of naphthoquinones and derivatives as novel inhibitors of main protease Mpro and papain-like protease PLpro of SARS-CoV-2 96%
- Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction 96%
Similar papers in this journal
Similar papers in this journal
- PharmaNet: Pharmaceutical discovery with deep recurrent neural networks. 96%
- Structure-based drug repositioning explains ibrutinib as VEGFR2 inhibitor 95%
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 95%
Similar papers in this journal
- Structure-Based Design of Small-Molecule Inhibitors of Human Interleukin-6 95%
- Multiscale virtual screening optimization for shotgun drug repurposing using the CANDO platform 95%
- Identifying Protein Features and Pathways Responsible for Toxicity using Machine learning, CANDO, and Tox21 datasets: Implications for Predictive Toxicology 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.