bioRxivBioinformaticsPreprint
Structure-based virtual screening and Molecular Dynamic Simulations identified FDA-approved molecules as potential inhibitors against the surface proteins of H1N1
Sukanya Bordoloi, Roshny PrasadiD, VS Lakshmi, Rajesh ChandramohanadasiD, Kathiresan NatarajaniD, Shijulal Nelson SathiiD
Abstract
H1N1, a subtype of influenza A virus, remains a global concern due to its ability to cause highly contagious, and fatal infections of the respiratory tract that can lead to seasonal pandemics. Factors such as genetic reassortment and antigenic shift due to the segmented nature of its RNA genome, lead to its rapid evolution that impacts drug and vaccine interactions. Thus, there is growing interest in identifying small-molecules against H1N1 to address the challenges posed by its ever-changing nature and enhance ability to combat the virus. By targeting the surface proteins crucial in the viral life cycle and interactions with host cells, we conducted structure-based virtual screening of 2,471 FDA-approved small molecules against H1N1s hemagglutinin (HA) and neuraminidase (NA) using molecular docking, and molecular dynamic simulations. A binding pocket for HA was identified at the interface of the three protomers, close to the fusion peptide, while in the case of NA the co-crystal ligand binding site was targeted. We identified 5 molecules, namely Econazole, Butoconazole, Miconazole, Isoconazole, and Tioconazole with higher binding affinity to HA and 4 molecules, namely Acarbose, Rutin, Paromomycin, and Idarubicin showing superior binding affinity to NA. Further molecular dynamic simulation of these molecules bound with HA and NA reaffirm the stability of the complexes. These molecules are known to have antifungal and antiviral properties. Thus, this study elucidates the importance of targeting HA and NA and paves the way for repurposing existing antivirals, antibacterials, and antifungals as inhibitors of the H1N1 viral entry into host cells.
- organism: virus
- assay: protein structure
- research focus: drug response / drug discovery
- approach: machine learning (classical)
- Citations
- 2
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Breathing and Tilting: Mesoscale simulations illuminate influenza glycoprotein vulnerabilities
L. Casalino, C. Seitz, … R. E. Amaro·bioRxiv·Aug 2, 2022
- An influenza A hemagglutinin small-molecule fusion inhibitor identified by a new high-throughput fluorescence polarization screen.
Y. Yao, R. U. Kadam, … D. W. Wolan·bioRxiv·Apr 2, 2020
- Repurposing of Drugs against mutated strain of Eurasian Avian like H1N1 (EA H1N1) Swine flu, Genotype-4 (G4) virus
Sangita Ghimire, Sandhya Sahukhal, … Pramod Aryal·bioRxiv·Oct 20, 2022
- N1 neuraminidase monoclonal antibodies 2H08 and 3H03 have decreased binding and inhibition properties against H1N1 influenza viruses circulating after 2018
Disha Bhavsar, Enikő Hermann, … Florian Krammer·bioRxiv·Oct 28, 2026
- Structural Convergence and Water-Mediated Substrate Mimicry Enable Broad Neuraminidase Inhibition by Human Antibodies
Julia Lederhofer, Andrew J. Borst, … Masaru Kanekiyo·Nature CommunicationsPeer-reviewed·Aug 1, 2025
- Exploiting the Affimer platform against influenza A virus
O. Debski-Antoniak, A. Flynn, … J. Fontana·bioRxiv·Aug 22, 2023