Hemagglutinin 3 and 8 can be the most efficient influenza subtypes for human host invasion; a comparative in silico approach

Abstract

The severity of the influenza virus infection is largely determined by its ability to invade the human host receptor. This critical step is conducted by utilizing hemagglutinin (HA) due to its binding with sialic acid 2,6 (SA). Though 18 subtypes (H1–H18) of HA have been identified, the most efficient one for conducting the host entry has not yet been resolved. This study aims to assess the severity of infections for HA variants by conducting a comparative docking of H1–H18 with the human SA receptor. Eighteen viral 3D structures were retrieved, minimized, and optimized for docking with human SA. In all retrieved structures, five conserved amino acid residues were selected for docking with human SA. Special protein grids were prepared by locating these five residues in the 18 selected subtypes. Results showed that H3 and H8 exerted the highest standard precision and extra precision docking scores, and the highest binding affinities with the human SA, respectively. Phylogenetic analyses confirmed the actual positioning of the selected 3D structures and showed these docked structures belonged to their usual classes due to the extremely close distances found in each docked subtype compared with its corresponding non-docked structures. H8-SA showed slightly better RMSD and SASA values than H3-SA, while H3-SIA showed more favourable radius of gyration scores than H8-SIA in the majority of the simulation period. Due to the highest affinity of binding of H3 and H8 with the human receptor, special caution should be exercised regarding any possible outbreak mediated by these subtypes in human populations. However, it is important to acknowledge a limitation inherent to the computational approach; it may hold relative rather than absolute significance. Further research is needed to deepen our understanding of the intricate interplay between HA variants and the host receptor, taking into account the broader context of viral infection dynamics. Communicated by Ramaswamy H. Sarma