Unveiling the antifungal potential of Xantolis assamica methanolic leaf extract: A phytochemical investigation and in silico analysis

Abstract

Fungal infections are an expanding global health burden, particularly in immunocompromised patients, and clinical management is increasingly limited by antifungal resistance and the narrow repertoire of current drug classes. To explore antifungal-relevant chemotypes from an underinvestigated botanical source, Xantolis assamica leaves were profiled via gas chromatography–mass spectrometry (GC–MS), and the annotated metabolites were prioritized in silico against Aspergillus niger β-1,4-endoglucanase (AnCel5A; PDB ID: 5I77), an underexplored enzyme implicated in fungal cellulose utilization. GC–MS revealed a chemically diverse metabolite set comprising fatty acids and their esters, terpenoids, phenolics, alkaloids, providing a rational basis for structure-based screening. Molecular docking identified CID 20845580 and CID 3083930 as the highest-ranked candidates (−9.9 and −8.9 kcal/mol), which were subsequently evaluated via ADMET profiling to assess developability; CID 20845580 satisfied the Lipinski criteria, whereas CID 3083930 showed a single lipophilicity-associated deviation that highlights a clear optimization target. Both candidates were then examined via 100 ns MD simulation and post-simulation MM-GBSA analysis alongside a reference compound. The CID 3083930 complex exhibited greater dynamic stability and an apo-like flexibility profile, whereas CID 20845580 showed higher structural deviation and residue-level fluctuation. Consistent with these findings, post-simulation MM-GBSA analysis indicated that CID 3083930 retained a more stable energetic profile over time, whereas CID 20845580 showed stronger initial energetic preference but reduced favorability during simulation. Overall, CID 20845580 was prioritized for docking-derived affinity, whereas CID 3083930, identified as onocerin, was prioritized for post-simulation stability, supporting their prioritization for lead optimization and experimental validation.