English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Increasing numbers of H5N1 influenza viruses (IVs) are responsible for human deaths, especially in North Africa and Southeast Asian. The binding of hemagglutinin (HA) on the viral surface to host sialic acid (SA) receptors is a requisite step in the infection process. Phylogenetic analysis reveals that H5N1 viruses can be divided into 10 clades based on their HA sequences, with most human IVs centered from clade 1 and clade 2.1 to clade 2.3. Protein sequence alignment in various clades indicates the high conservation in the receptor-binding domains (RBDs) is essential for binding with the SA receptor. Two glycosylation sites, 158N and 169N, also participate in receptor recognition. In the present work, we attempted to construct a serial H5N1 HA models including diverse glycosylated HAs to simulate the binding process with various SA receptors  in silico . As the SA-α-2,3-Gal and SA-α-2,6-Gal receptor adopted two distinctive topologies, straight and fishhook-like, respectively, the presence of N-glycans at 158N would decrease the affinity of HA for all of the receptors, particularly SA-α-2,6-Gal analogs. The steric clashes of the huge glycans shown at another glycosylation site, 169N, located on an adjacent HA monomer, would be more effective in preventing the binding of SA-α-2,3-Gal analogs.

Two Glycosylation Sites in H5N1 Influenza Virus Hemagglutinin That Affect Binding Preference by Computer-Based Analysis