Structural and Dynamic Insights into Hemagglutinin Evolution: Computational Modeling of Receptor Binding and Antigenic Drift in Influenza A

Introduction

Influenza A viruses (IAV) circulate widely in avian and swine populations, posing continuous threats to animal health and acting as reservoirs for zoonotic emergence [1, 2]. The hemagglutinin (HA) glycoprotein is the primary determinant of host range and immune recognition. HA mediates viral attachment to sialic acid (SA) receptors on host cells and is the principal target of neutralizing antibodies [3, 4]. Evolutionary pressure from host immunity and receptor availability drives HA sequence diversity, leading to antigenic drift and shifts in receptor binding preference [5, 6]. Computational structural biology has become indispensable for dissecting these processes at atomic resolution. This article reviews how homology modeling, molecular docking, and molecular dynamics (MD) simulations are applied to map HA receptor-binding specificity (α2,3 versus α2,6 SA linkages) and to track antigenic drift, with emphasis on veterinary applications in zoonotic risk assessment and vaccine strain selection.

Hemagglutinin Structure and Receptor Binding

The HA trimer comprises a globular head domain containing the receptor-binding site (RBS) and a stem domain responsible for membrane fusion [1, 5]. The RBS is formed by three structural elements: the 130-loop, the 190-helix, and the 220-loop. Key residues at positions 226 and 228 (H3 numbering) govern linkage preference [4]. Avian IAVs typically bind α2,3-linked SA, whereas mammalian-adapted viruses prefer α2,6-linked SA [4, 5]. Swine respiratory epithelium expresses both linkage types, making pigs a mixing vessel for reassortment [2]. Mutations in the RBS can alter binding affinity and specificity, enabling cross-species transmission [1, 4]. For example, the Q226L and G228S substitutions in H2 and H3 subtypes shift preference from α2,3 to α2,6 SA [4]. Computational modeling allows systematic evaluation of such mutations before experimental validation.

Computational Methods for Structural Modeling

Homology Modeling

When experimental structures are unavailable, homology modeling builds three-dimensional HA models using templates from the [Protein Data Bank](/knowledge/bioinformatics/protein-data-bank-formats-archival-validation 2) (PDB) [5]. Tools such as HHpred and MODELLER align target sequences to known HA structures, generating models that capture backbone and side-chain conformations [5]. The accuracy of these models depends on sequence identity to the template; HA subtypes with >70% identity yield reliable RBS geometries [5]. Homology models serve as starting points for docking and MD simulations.

Molecular Docking

Molecular docking predicts the orientation and binding affinity of SA analogs within the RBS [4, 5]. Programs like AutoDock Vina and RosettaLigand sample ligand poses and score them using energy functions. Docking studies have quantified the impact of RBS mutations on SA linkage preference [4]. For instance, docking of α2,3-sialyllactose and α2,6-sialyllactose into HA models from avian and human isolates recapitulates known specificity patterns [4]. Docking scores correlate with experimental binding data, enabling high-throughput screening of emerging variants [4, 5].

Molecular Dynamics Simulations

MD simulations provide dynamic information on HA-receptor interactions over nanosecond to microsecond timescales [5]. Using packages such as GROMACS and AMBER, solvated HA-receptor complexes are simulated under physiological conditions. Trajectories reveal conformational fluctuations in the RBS loops, hydrogen bond networks, and water-mediated contacts [5]. Free energy calculations (e.g., MM-PBSA) estimate binding affinities and identify stabilizing mutations [5]. MD simulations have been used to study the effect of glycosylation on HA antigenicity and receptor binding [5].

Table 1 summarizes the key computational methods and their applications in HA analysis.

Receptor Binding Specificity and Host Tropism

The balance between α2,3 and α2,6 SA binding is a critical barrier for zoonotic spillover [1, 4]. Avian IAVs (e.g., H5N1, H9N2) predominantly bind α2,3 SA, limiting replication in human upper airways [1, 3]. However, mutations in the RBS can broaden specificity. Ivan et al. [4] characterized receptor-binding mutations in influenza A and B viruses from Singapore, identifying substitutions at positions 226 and 228 that enhanced α2,6 binding. Similarly, Jones et al. [5] analyzed H1N1 strains from India and found that the D225G mutation altered receptor binding and was associated with increased virulence. Computational docking and MD simulations predicted that D225G introduces a new hydrogen bond with α2,6 SA, stabilizing the complex [5].

Swine IAVs, such as H1N1pdm09, exhibit dual receptor binding, facilitating reassortment [2]. Giovanetti et al. [2] studied the transmission dynamics of H1N1pdm09 in Italian swine herds, combining phylogenetic analysis with structural modeling to infer receptor binding properties. Their work demonstrated that HA sequences from swine retained avian-like features in the RBS, suggesting ongoing adaptation [2].

Antigenic Drift and Immune Escape

Antigenic drift results from the accumulation of amino acid substitutions in HA epitopes that reduce antibody neutralization [3, 6]. Computational methods can predict antigenic clusters by mapping mutations onto HA structures and assessing their impact on antibody binding [5]. Al-Eitan et al. [3] performed phylogenetic and structural analysis of HPAI H5N1 HA sequences from the Middle East, identifying positively selected sites in the globular head that overlap with known epitopes. These sites are candidates for antigenic drift.

MD simulations can quantify the effect of drift mutations on epitope flexibility and antibody accessibility [5]. For example, mutations near the receptor-binding site can alter the local conformation, indirectly affecting antibody recognition [5]. Machine learning models trained on structural features (e.g., solvent accessibility, residue coevolution) can predict antigenic distances between strains [5]. Such predictions inform vaccine strain selection for poultry and swine.

Applications in Zoonotic Risk Assessment and Vaccine Strain Selection

Computational modeling of HA evolution directly supports veterinary surveillance. By integrating sequence data from databases such as GISAID and NCBI Influenza Virus Resource, researchers can build structural models of emerging strains and evaluate their receptor binding and antigenic properties [1, 3]. For instance, Zhang et al. [1] characterized a reassortant H9N2 virus from a human case using genomic and structural approaches. Their modeling revealed that the HA retained avian receptor specificity but carried mutations that enhanced replication in mammalian cells, highlighting zoonotic risk.

Vaccine strain selection for poultry relies on matching the HA of circulating strains to vaccine antigens. Computational antigenic cartography, combined with structural mapping of epitope mutations, can identify strains that are antigenically representative [6]. Langat et al. [6] applied genome-wide evolutionary dynamics to influenza B, but similar approaches are used for IAV in veterinary contexts. MD simulations can also predict the stability of HA under different pH and temperature conditions, relevant for vaccine formulation [5].

The following Mermaid diagram illustrates a typical computational workflow for HA analysis.

flowchart TD
    A[Sequence Data from GISAID/NCBI], > B[Phylogenetic Analysis]
    B, > C[Selection of Representative Strains]
    C, > D[Homology Modeling of HA]
    D, > E[Molecular Docking with SA Analogs]
    E, > F[Binding Specificity Prediction]
    F, > G[Zoonotic Risk Assessment]
    D, > H[Molecular Dynamics Simulations]
    H, > I[Antigenic Drift Prediction]
    I, > J[Vaccine Strain Recommendation]
    G, > K[Surveillance Prioritization]
    J, > K

Conclusion

Computational structural biology provides a powerful framework for understanding hemagglutinin evolution in influenza A viruses. Homology modeling, molecular docking, and MD simulations enable detailed characterization of receptor-binding specificity and antigenic drift, directly informing veterinary diagnostics and vaccine design. The integration of these methods with genomic surveillance (e.g., GISAID) allows rapid assessment of emerging strains for zoonotic potential. Future advances in machine learning and enhanced force fields will further improve predictive accuracy, supporting proactive control of influenza in animal populations.

For interactive exploration of HA trimers and key residues (e.g., positions 226 and 228 in H3 numbering), readers are encouraged to use the 3D Protein Viewer available on this portal. Related articles on Structural Dynamics of Avian Influenza Hemagglutinin: Molecular Modeling and Receptor Binding Predictions for Pandemic Risk Assessment and Machine Learning-Driven Prediction of Antigenic Drift in Influenza A Hemagglutinin Using Structural Dynamics and Sequence Surveillance provide complementary perspectives. The Global Initiative on Sharing All Influenza Data (GISAID) article details the primary sequence repository used in these analyses.

References

[1] Zhang J, Wu Y, Wang W, et al. Genomic and structural characterization of a reassortant H9N2 avian influenza virus from a human case. Int J Infect Dis. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41360219/

[2] Giovanetti M, Cella E, Soliani L, et al. From North to South: transmission dynamics of H1N1pdm09 swine influenza A viruses in Italy. J Gen Virol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41231533/

[3] Al-Eitan LN, Almahdawi DL, Khair IY. Phylogenetic Analysis and Spread of HPAI H5N1 in Middle Eastern Countries Based on Hemagglutinin and Neuraminidase Gene Sequences. Viruses. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40431745/

[4] Ivan FX, Zhou X, Lau SH, et al. Molecular insights into evolution, mutations and receptor-binding specificity of influenza A and B viruses from outpatients and hospitalized patients in Singapore. Int J Infect Dis. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/31669593/

[5] Jones S, Nelson-Sathi S, Wang Y, et al. Evolutionary, genetic, structural characterization and its functional implications for the influenza A (H1N1) infection outbreak in India from 2009 to 2017. Sci Rep. 2019. URL: https://pubmed.ncbi.nlm.nih.gov/31604969/

[6] Langat P, Raghwani J, Dudas G, et al. Genome-wide evolutionary dynamics of influenza B viruses on a global scale. PLoS Pathog. 2017. URL: https://pubmed.ncbi.nlm.nih.gov/29284042/ *** Disclaimer: This article is for educational and informational purposes only. It is not intended to substitute for professional veterinary advice, diagnosis, treatment, or regulatory guidance. Always consult a licensed veterinarian or qualified specialist regarding animal health, disease diagnosis, and therapeutic decisions.