Structural insights into the binding potential of natural therapeutic leads as antiviral agents against human Metapneumovirus nucleoprotein
Human metapneumovirus (hMPV), a member of the family Pneumoviridae and the genus Metapneumovirus, is a prevalent cause of respiratory tract infections, leading to severe complications and even death. As of now, there are no approved specific antiviral drugs or vaccines for hMPV, underscoring the need for potential therapeutic candidates. Hence, the present study employed a structure-based virtual screening (SBVS) of prepared ligands from CMNPD and SPECS databases against the hMPV nucleoprotein. Multi-level precision screening identified CMNPD3066, CMNPD12667, CMNPD25748, SPECS-AE-765, and SPECS-AP-123 as the top computationally prioritized compounds. These compounds demonstrated favourable predicted interactions with the docking scores ranging from −9.8 to −7.5 kcal/mol, and post-docking MMGBSA binding free energies ranging from −74.02 to −54.81 kcal/mol. Further, ADME/T evaluation predicted that these candidates were not involved in the violation of Lipinski’s rule of five (Ro5) and all demonstrated favourable pharmacokinetic profiles. Subsequent 500 ns molecular dynamics simulations (MDS) and essential dynamics (ED) analyses, including principal component analysis (PCA) and free energy landscape (FEL) mapping, revealed the conformational stability of the nucleoprotein-ligand complexes throughout the simulation period. Collectively, the study concludes that the identified hit molecules are promising binding candidates for therapeutic development against hMPV and warrant further experimental and clinical validation.
Authors
- J. Jeyaraman
- D. Prabhu
- S. Nathar
- M. Muthuvairam Subbulakshmi
- M. Jayaraman
- R. Vijayarangan
Institutions
- Alagappa University (IN)
- Indian Institute of Technology Guwahati (IN)
- Karpagam Academy of Higher Education (IN)
Publication Details
- Journal
- SAR and QSAR in environmental research
- Published
- 2026-10-07
- DOI
- https://doi.org/10.1080/1062936x.2026.2734388
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00