Lead Generation of Neuraminidase Inhibitor using a Computational Fragment-Based Drug Designing Approach

Abstract

Influenza infections pose a persistent threat to human health worldwide. Various neuraminidase (NA) inhibitors have been used to surmount this situation. However, the emergence of new mutant strains has limited its therapeutic effect. In recent times fragment-based drug designing has gained the limelight as it is time- and labor-efficient. Here in the present study, we have used a total of 8 NA inhibitors (both FDA and investigational drugs) to design novel and more potent drug molecules to inhibit NA. Fragment script, and BREED tool of Schrodinger suite was utilized to generate molecules with new structural combinations. The produced hybrids were docked against native NA protein, and the hybrids with higher binding affinity were further analyzed. A total of 50 hits were then subjected to ADMET analysis using the Qikprop module, OSIRIS, and PROTOX server. The molecules were also assessed for their biological activity using the PASS prediction server. It was observed that 14 out of 50 hybrids comply with all the studied properties. Hence these screened out molecules were analyzed for its interaction pattern with native NA protein. The results from the analysis showed that all the 14 molecules exhibited interaction with vital key residues of NA such as Glu 119, Arg 152, Arg 292, and Arg 371. The results were further validated by carrying out mutation studies with the most prevalent mutations. It is noteworthy to mention that all the 14 hit molecules exhibited better binding efficiency than the reference molecule. We believe that these results could pave the way in the development of novel NA inhibitors in the near future.