Hierarchical Computational Prioritization of Uvaria chamae Phytochemicals Targeting Ebola Virus Glycoprotein and SARS-CoV-2 PLpro

Abstract

Medicinal plants are valuable sources of structurally diverse bioactive compounds with potential antiviral properties, although their chemical complexity complicates candidate prioritization. This study developed a hierarchical computational framework to prioritize metabolites from Uvaria chamae against Ebola virus glycoprotein GP1–GP2 (PDB ID: 5F1B) and SARS-CoV-2 papain-like protease PLpro (PDB ID: 7CMD). A library of 105 phytochemicals was evaluated using statistical analyses, molecular docking, Prime MM-GBSA, ADMET and medicinal chemistry assessments, induced-fit docking, 100 ns molecular dynamics simulations, and conceptual DFT. Although overall docking-score distributions did not differ significantly among plant organs, top-ranked candidates were predominantly associated with roots and stems, while flavonoids, acetogenins, and phenolics showed the most favorable profiles. Proanthocyanidin emerged as the leading Ebola GP candidate (Prime MM-GBSA ΔGbind = −45.66 kcal/mol), whereas uvangoletin showed the most balanced PLpro profile (−41.69 kcal/mol). MD simulations with toremifene and GRL0617 as references supported structural persistence of the prioritized complexes. DFT analysis yielded HOMO–LUMO gaps of 5.37 eV for proanthocyanidin and 4.81 eV for uvangoletin. This framework enables reproducible prioritization of plant organs, phytochemical classes, and metabolites before experimental validation.