Computational evaluation of Niloticin as a potential multi-target binder against Lassa virus glycoprotein and nucleoprotein

Lassa fever is a potentially fatal disease with a high mortality rate and currently lacks an effective treatment, posing a significant threat, particularly in West Africa. In this study, Niloticin, a naturally occurring protolimonoid triterpenoid, was computationally evaluated as a potential multi-target binder against Lassa virus–associated host and viral targets using network pharmacology, molecular docking, molecular dynamics simulation, hydrogen-bond occupancy analysis, and MM/PBSA binding-energy estimation. Eight overlapping candidate host-associated targets, namely F10, ABL1, AKT1, AR, BCHE, EGFR, KIT, and NLRP3, were identified using the GeneCards, OMIM, and SwissTargetPrediction databases. Functional enrichment analysis suggested possible associations with apoptosis, immune regulation, inflammation, coagulation, and cell-signaling pathways. Molecular docking predicted favorable binding of Niloticin to LASV glycoprotein regions GP2 and SSP, with docking scores of − 6.85 and − 7.17 kcal/mol, respectively, and to the nucleoprotein dTTP-binding pocket with a docking score of − 7.36 kcal/mol. Niloticin formed predicted hydrogen-bonding and hydrophobic interactions with key residues in GP2, SSP, and NP, including ARG422 and PHE434 in GP2, ALA25 and VAL18 in SSP, and residues surrounding the NP binding pocket. During MD simulations, Niloticin-bound systems exhibited comparatively stable trajectory behavior, as supported by RMSD, RMSF, radius of gyration, SASA, and hydrogen-bond analyses. Hydrogen-bond occupancy analysis further indicated recurrent ligand–protein contacts involving ARG422 in GP2, VAL18 and ALA25 in SSP, and THR178, GLU299, ARG323, and LYS253 in NP. MM/PBSA analysis showed favorable relative binding-energy estimates for Niloticin, ranging from approximately − 84 to − 100 kJ/mol for glycoprotein-associated systems and approximately − 62 kJ/mol for NP, mainly supported by van der Waals and nonpolar solvation contributions. Overall, these computational findings suggest that Niloticin may engage multiple LASV-associated viral and host targets and may be prioritized for further experimental investigation. However, the results are based entirely on computational analyses and do not confirm antiviral inhibition, direct host-target modulation, or functional blockade of LASV entry or replication. Experimental validation through cytotoxicity assays, biochemical binding studies, pseudovirus entry assays, and infectious-virus antiviral assays is required to establish the anti-LASV potential of Niloticin.

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Journal
Scientific Reports
Published
2026-08-24
DOI
https://doi.org/10.1038/s41598-026-64782-4
Primary Topic
Computational Drug Discovery Methods
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article
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0.00

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article

Computational evaluation of Niloticin as a potential multi-target binder against Lassa virus glycoprotein and nucleoprotein

Quan Zou, Antony Stalin, Stalin Fathima Merlin, Xiaorui Kang et al.
Scientific Reports
Computational Drug Discovery Methods
article

Computational evaluation of Niloticin as a potential multi-target binder against Lassa virus glycoprotein and nucleoprotein

Quan Zou, Antony Stalin, Stalin Fathima Merlin, Xiaorui Kang, Ximei Luo, Mudassar Mehmood Baig
article en

Abstract

Lassa fever is a potentially fatal disease with a high mortality rate and currently lacks an effective treatment, posing a significant threat, particularly in West Africa. In this study, Niloticin, a naturally occurring protolimonoid triterpenoid, was computationally evaluated as a potential multi-target binder against Lassa virus–associated host and viral targets using network pharmacology, molecular docking, molecular dynamics simulation, hydrogen-bond occupancy analysis, and MM/PBSA binding-energy estimation. Eight overlapping candidate host-associated targets, namely F10, ABL1, AKT1, AR, BCHE, EGFR, KIT, and NLRP3, were identified using the GeneCards, OMIM, and SwissTargetPrediction databases. Functional enrichment analysis suggested possible associations with apoptosis, immune regulation, inflammation, coagulation, and cell-signaling pathways. Molecular docking predicted favorable binding of Niloticin to LASV glycoprotein regions GP2 and SSP, with docking scores of − 6.85 and − 7.17 kcal/mol, respectively, and to the nucleoprotein dTTP-binding pocket with a docking score of − 7.36 kcal/mol. Niloticin formed predicted hydrogen-bonding and hydrophobic interactions with key residues in GP2, SSP, and NP, including ARG422 and PHE434 in GP2, ALA25 and VAL18 in SSP, and residues surrounding the NP binding pocket. During MD simulations, Niloticin-bound systems exhibited comparatively stable trajectory behavior, as supported by RMSD, RMSF, radius of gyration, SASA, and hydrogen-bond analyses. Hydrogen-bond occupancy analysis further indicated recurrent ligand–protein contacts involving ARG422 in GP2, VAL18 and ALA25 in SSP, and THR178, GLU299, ARG323, and LYS253 in NP. MM/PBSA analysis showed favorable relative binding-energy estimates for Niloticin, ranging from approximately − 84 to − 100 kJ/mol for glycoprotein-associated systems and approximately − 62 kJ/mol for NP, mainly supported by van der Waals and nonpolar solvation contributions. Overall, these computational findings suggest that Niloticin may engage multiple LASV-associated viral and host targets and may be prioritized for further experimental investigation. However, the results are based entirely on computational analyses and do not confirm antiviral inhibition, direct host-target modulation, or functional blockade of LASV entry or replication. Experimental validation through cytotoxicity assays, biochemical binding studies, pseudovirus entry assays, and infectious-virus antiviral assays is required to establish the anti-LASV potential of Niloticin.

Scientific Reports
University of Electronic Science and Technology of China (CN), Quzhou University (CN), Macao Polytechnic University (MO)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 8%
Computational Drug Discovery Methods
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