Mucin-mimetic glycan arrays integrating machine learning for analyzing receptor pattern recognition by influenza A viruses

Influenza A viruses (IAVs) exploit host glycans in airway mucosa for entry and infection. Detection of changes in IAV glycan-binding phenotype can provide early indication of transmissibility and infection potential. While zoonotic viruses are monitored for mutations, the influence of host glycan presentation on viral specificity remains obscured. Here, we describe an array platform that uses synthetic mimetics of mucin glycoproteins to model how receptor presentation and density in the mutinous glycocalyx may impact IAV recognition H1N1 and H3N2 binding in arrays of alpha 2,3- and alpha 2,6-sialyllactose receptors confirmed their known sialic-acid-binding specificities and revealed their different sensitivities to receptor presentation. Further, the transition of H1N1 from avian to mammalian cell culture improved the ability of the virus to recognize mucin-like displays of alpha 2,6-sialic acid receptors. Support vector machine (SVM) learning efficiently characterized this shift in binding preference and may prove useful to study viral evolution to a new host.