AbstractEnolase is a glycolytic enzyme that catalyzes the interconversion between 2-phosphoglycerate and phosphoenolpyruvate. In trypanosomatids enolase was proposed as a key enzyme afterin silicoandin vivoanalysis and it was validated as a protein essential for the survival of the parasite. Therefore, enolase constitutes an interesting enzyme target for the identification of drugs against Chagas disease. In this work, a combined virtual screening strategy was implemented, employing similarity virtual screening, molecular docking and molecular dynamics. First, two known enolase inhibitors and the enzyme substrates were used as queries for the similarity screening on the Sweetlead database using five different algorithms. Compounds retrieved in the top 10 of at least three search algorithms were selected for further analysis, resulting in six compounds of medical use (etidronate, pamidronate, fosfomycin, acetohydroximate, triclofos, and aminohydroxybutyrate). Molecular docking simulations predicted acetohydroxamate and triclofos would not bind to the active site of the enzyme, and a re-scoring of the obtained poses signaled fosfomycin and aminohydroxybutyrate as bad enzyme binders. Docking poses obtained for etidronate, pamidronate, and PEP, were used for molecular dynamics calculations to describe their mode of binding. From the obtained results, we propose etidronate as a possibleTcENO inhibitor, and describe desirable and undesirable molecular motifs to be taken into account in the repurposing or design of drugs aiming this enzyme active site.
Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis
