A Python-Automated Computational Pipeline for Screening Nigella sativa Phytochemicals against the Hantavirus Gn-Gc Tetramer

This repository contains a Python-automated computational structural bioinformatics pipeline developed for the preliminary virtual screening of selected Nigella sativa phytochemicals against the Hantavirus Gn-Gc tetrameric glycoprotein complex (PDB ID: 6Z6G), a key viral structure involved in host-cell entry and membrane fusion. The workflow integrates multiple open-source computational biology and cheminformatics tools, including Biopython, PubChemPy, RDKit, OpenBabel, AutoDock Vina, py3Dmol, and PLIP, to automate receptor preparation, ligand retrieval, molecular docking, pose analysis, interaction profiling, and drug-likeness evaluation. Four major Nigella sativa phytochemicals were computationally screened: Thymoquinone Dithymoquinone Carvacrol Thymohydroquinone Among the tested compounds, Dithymoquinone demonstrated the strongest predicted binding affinity against the Hantavirus Gn-Gc target, with favorable docking stability, interaction density, and theoretical drug-likeness properties based on Lipinski’s Rule of Five. The repository includes: Manuscript and supplementary material Python scripts and computational workflow Docking outputs and log files Structural visualization files Interaction analysis results Reproducibility information and dependency requirements This work is intended as an exploratory computational study and hypothesis-generating resource for future antiviral drug discovery investigations. The findings presented are based solely on in silico modeling and require further experimental validation, including molecular dynamics simulations, in vitro assays, and in vivo studies, before any therapeutic conclusions can be established. Keywords: Hantavirus, Nigella sativa, Dithymoquinone, Molecular Docking, Computational Biology, Structural Bioinformatics, AutoDock Vina, Python, Antiviral Drug Discovery, Bioinformatics.