COMPUTATIONAL PROSPECTING FOR ANTIVIRAL PREDICTION OF THE OROPOUCHE VIRUS

Oropouche virus (OROV) is an emerging arbovirus circulating in the Americas and causing Oropouche fever. In Brazil, an alarming surge in cases rising from 831 in 2023 to over 13,000 in 2024 has been marked by expansion into non-endemic regions and severe clinical manifestations, including fatalities in young adults and suspected vertical transmission. Despite being recognized as a public health threat by PAHO, the disease remains largely neglected, lacking rapid diagnostics, vaccines, and specific treatments. Given this critical public health demand, computational methods offer a vital strategy for the rapid repositioning and discovery of antiviral compounds. Therefore, this study aims to predict and identify potential antiviral inhibitors targeting essential OROV proteins using a comprehensive in silico workflow.The methodology begins with the selection of the RNA-dependent RNA polymerase as the primary molecular target, followed by its three-dimensional comparative modeling using Modeller. Ligand library preparation from the DrugCentral database entails solvent and ion removal, followed by the generation of tautomeric variants at physiological pH using Gypsum-DL and ORCA. Following target and ligand protonation and the establishment of docking parameters, large-scale molecular docking is performed between the target protein and the ligands. The pipeline incorporates a consensus-based ligand screening strategy leveraging RMSD and score metrics, evaluation of tautomeric interactions, and rigorous pharmacological and toxicological profiling. Furthermore, Phase 1 and Phase 2 metabolic derivatives are generated and subjected to ADMET screening. Finally, the top-performing ligands undergo molecular dynamics simulations to evaluate complex stability and interaction dynamics over time. Through this multi-step computational pipeline, candidate molecules demonstrating high binding affinity and stable interactions are prioritized, minimizing false positives and providing a robust, accelerated framework for discovering novel therapeutics against OROV outbreaks.