In silico structure-based design of enhanced peptide inhibitors targeting RNA polymerase PAN-PB1C interaction

Abstract

Developing antivirals for influenza A virus (FluA) has become more challenging due to high range of antigenic mutation and increasing numbers of drug-resistant viruses. Finding a selective inhibitor to target highly conserved region of protein-protein interactions interface, thereby increasing its efficiency against drug resistant virus could be highly beneficial. In this study, we used in silico approach to derive FluAPep1 from highly conserved region, PAN-PB1C interface and generated 121 FluAPep1 analogues. Interestingly, we found that the FluAPep1 interaction region in the PAN domain are highly conserved in many FluA subtypes. Especially, FluAPep1 targets two pandemic FluA strains, H1N1/avian/2009 and H3N2/Victoria/1975. All of these FluA subtypes PAN domain (H1N1/H3N2CAN/H3N2VIC/H7N1/H7N2) were superimposed with PAN domain from H17N10 and the calculated root mean standards deviations were less than 3 Å. FlexPepDock analysis revealed that FluAPep1 exhibited higher binding affinity (score -246.155) with the PAN domain. In addition, around 86% of non-hot spot mutated peptides (FluAPep28-122) showed enhanced binding affinity with PAN domain. ToxinPred analysis confirmed that designed peptides were non-toxic. Thus, FluAPep1 and its analogues has potential to be further developed into an antiviral treatment against FluA infection.