Deep Mutational Scanning and Structural Modeling of Avian Influenza Hemagglutinin for Pandemic Risk Assessment

Introduction

Avian influenza A viruses (IAV) circulate extensively in wild waterfowl and poultry, occasionally crossing species barriers to infect mammals and posing pandemic threats [1, 2]. In veterinary medicine, H5, H7, and H9 subtypes are of particular concern due to their high pathogenicity in gallinaceous poultry or their capacity for zoonotic spillover [3]. The hemagglutinin (HA) glycoprotein, a homotrimeric class I fusion protein, is the primary determinant of host range and antigenicity [3]. HA mediates viral attachment to sialic acid receptors on host cells and is the principal target of neutralizing antibodies [3]. The receptor binding site (RBS) of HA exhibits considerable plasticity, enabling adaptation from avian-type alpha-2,3-linked sialic acid receptors to mammalian-type alpha-2,6-linked receptors, a hallmark of zoonotic potential [4, 5]. deep mutational scanning (DMS) combined with structural modeling has emerged as a powerful approach to systematically map the functional consequences of every possible amino acid mutation in HA and to predict pandemic risk [6, 5]. This article reviews the integration of DMS data with computational structural biology to assess avian influenza HA function, receptor binding specificity, antibody escape, and thermostability in the context of pandemic risk assessment.

Deep Mutational Scanning: Principles and Applications to Avian HA

Deep mutational scanning involves the construction of comprehensive libraries of HA variants, typically through saturation mutagenesis of the entire ectodomain or specific functional regions such as the RBS or stem domain [3, 6]. Libraries are generated using site-directed mutagenesis, error-prone PCR, or synthetic DNA oligonucleotides covering all single nucleotide substitutions [3]. These libraries are expressed on the surface of mammalian cells or displayed on viral particles and subjected to selective pressures including receptor binding affinity, antibody neutralization, and thermal stability [1, 6]. High-throughput sequencing quantifies the enrichment or depletion of each variant, producing a functional fitness landscape that ranks amino acid substitutions by their effect on function [3].

Application of DMS to avian HA subtypes has revealed subtype-specific sequence constraints despite highly conserved three-dimensional folds. Ahn et al. demonstrated that H1, H3, H5, H7, and H9 HA subtypes share extremely similar structures yet exhibit distinct patterns of mutational tolerance, particularly within the RBS [3]. For example, H5 HA shows greater constraint at key receptor contact residues than does H9 HA, reflecting different degrees of receptor binding plasticity [3]. Such constraint maps are critical for predicting which mutations are likely to emerge under selective pressure in poultry populations.

Thermostability is another key property assessed by DMS. Pei et al. identified specific mutations (S84F, G167N, D168N) in the HA of H9N2 viruses that increase thermostability and potentiate viral transmission in a warming environment [1]. These findings highlight the need to consider environmental factors when assessing pandemic risk, as climate change may alter the selective landscape for avian influenza viruses circulating in poultry flocks [1]. The thermostability mutations were validated by measuring HA protein melting temperature and viral replication kinetics at elevated temperatures [1].

Structural Modeling of HA-Receptor Interactions

Structural modeling using tools such as Rosetta, AlphaFold2, and molecular dynamics (MD) simulations provides atomic-resolution insight into how mutations affect HA function. Yang et al. characterized the crystal structure of H7N9 HA from the fifth epidemic wave at 2.3 angstrom resolution, revealing alterations in the RBS that enhanced binding to human-type receptors [5]. The structural analysis identified a critical residue change at position 226 (Q226L in H3 numbering) that shifted receptor preference from alpha-2,3 to alpha-2,6 sialic acids [5]. Such structural data can be combined with DMS-derived mutational landscapes to predict the functional impact of novel mutations observed in surveillance databases.

Computational docking of receptor analogs (e.g., LSTc, LSTa) into HA RBS models allows estimation of binding free energy changes (delta-delta G) upon mutation [2, 3]. Rosetta's ddg_monomer protocol and molecular mechanics generalized Born surface area (MM-GBSA) methods are commonly used to compute these changes [2]. Multi-view transformer architectures have been applied to integrate sequence, structure, and functional data for HA-NA drift risk scoring and mutation hotspot mapping [2]. This approach identifies residues that are likely to co-evolve to maintain functional balance between HA receptor binding and NA receptor cleaving activity, a critical factor for viral fitness in avian hosts [2].

Molecular dynamics simulations further reveal the dynamic behavior of the RBS, including loop motions and glycan interactions [4]. Song et al. identified a key HA mutation mediating antibody escape in H1N1pdm09 viruses, and structural modeling showed that the mutation altered the conformation of antigenic site Sa, reducing antibody binding by steric hindrance and electrostatic changes [4]. Although this study focused on a human seasonal subtype, the methodological approach is directly transferable to avian HA subtypes for predicting escape from poultry-derived antibodies or monoclonal antibodies developed for veterinary use.

Integrating DMS and Structural Data for Risk Assessment

The integration of DMS fitness landscapes with structural models enables systematic pandemic risk scoring. A typical computational workflow is illustrated in Figure 1.

flowchart TD
    A[HA sequence data from surveillance], > B[Deep mutational scanning library construction]
    B, > C[Functional selection: receptor binding, antibody pressure, thermostability]
    C, > D[High-throughput sequencing to quantify variant enrichment]
    D, > E[Mutational fitness landscape for each condition]
    E, > F[Structural modeling: Rosetta, AlphaFold2, MD simulations]
    F, > G[Compute binding free energy changes (ΔΔG) for RBS]
    F, > H[Map mutations onto 3D structure of HA]
    G, > I[Assess receptor binding specificity shift]
    H, > I
    I, > J[Risk scoring: HA-NA drift, antibody escape, thermostability index]
    J, > K[Pandemic risk assessment score]
    K, > L[Recommend vaccine strain updates]
    L, > M[In silico validation with new sequence data from GISAID/NCBI]

Table 1 summarizes key studies that have applied DMS and structural modeling to avian HA.

The risk scoring framework developed by Agarwal et al. [2] integrates DMS-based mutational fitness, structural energy calculations, and phylogenetic information using attention mechanisms that weight relevant structural features. The model outputs a drift risk score for each residue position, identifying mutational hotspots that warrant intensified surveillance [2]. When applied to HA sequences from GISAID, the framework can flag emerging variants with elevated pandemic potential.

Implications for Vaccine Strain Selection and Surveillance

Deep mutational scanning data directly inform vaccine strain selection by identifying mutations that affect antigenicity, receptor binding, and protein stability [6]. Dosey et al. demonstrated that introduction of stabilizing mutations (e.g., disulfide bonds and cavity-filling substitutions) into the HA of H5 clade 2.3.4.4b improved the magnitude and breadth of neutralizing antibody responses in a mouse model, with significant increases in hemagglutination inhibition titers compared to wild-type HA immunogen [6]. Such engineered HA antigens can be produced as recombinant proteins or virus-like particles for use in poultry vaccines.

The computational risk scoring framework can be deployed as a surveillance tool to monitor emerging HA variants in GISAID and NCBI databases. Mutations identified as high-risk by the multi-view transformer model can be flagged for experimental validation in biosafety level 3 facilities [2]. Similarly, the thermostability mutations identified by Pei et al. [1] can be incorporated into surveillance criteria for H9N2 viruses in regions experiencing warming trends, allowing targeted monitoring of these variants in poultry flocks.

Data Resources and Visualization

All major avian influenza HA sequences are available through public repositories such as GISAID and NCBI GenBank. Three-dimensional protein structures can be visualized using interactive viewers (e.g., Mol*, NGL Viewer) that allow users to examine HA-receptor complexes and highlight key residue positions identified by DMS and structural modeling. Linking to a 3D Protein Viewer enables real-time exploration of mutational hotspots, antibody epitopes, and receptor contact residues. For example, the RBS with residues 226 and 228 (H3 numbering) can be highlighted to show their role in receptor specificity as described by Yang et al. [5].

Future Directions and Computational Workflows

Future integration of DMS with cryo-electron microscopy (cryo-EM) structures of HA in complex with antibodies or receptors will provide more accurate templates for modeling conformational dynamics. AlphaFold2-predicted structures, when validated against known crystal structures, can extend coverage to HA subtypes with limited experimental data. Machine learning models that predict the effect of mutations on HA function, trained on DMS data, can be applied to whole-genome surveillance data to identify emerging high-risk variants in real time [2]. Coupling DMS with environmental data, such as ambient temperature and humidity, will refine thermostability risk predictions as demonstrated by Pei et al. [1]. Computational workflows should incorporate automated pipelines for structural modeling, free energy calculation, and risk scoring. Open-source tools such as Rosetta, PyRosetta, and deep learning models like AlphaFold2 and ESM-1b enable reproducible analyses across laboratories.

Conclusion

Deep mutational scanning and structural modeling provide complementary approaches for assessing the pandemic risk posed by avian influenza hemagglutinin. By mapping the functional constraints of HA under diverse selective pressures (receptor binding, antibody escape, and thermostability), these methods predict which mutations are likely to emerge and facilitate zoonotic spillover from poultry to mammals. The integration of these data into computational risk scoring frameworks, as exemplified by multi-view transformer models [2], enhances surveillance and informs vaccine strain selection for poultry and potentially for pandemic preparedness. Ongoing efforts to expand DMS libraries to all avian HA subtypes and to incorporate environmental factors will further refine risk assessments, supporting One Health surveillance strategies.

References

[1] Pei X, Chen W, Xiao H, et al. HA thermostability mutations S84F, G167N, and D168N potentiate H9N2 virus transmission in a warming environment. Emerg Microbes Infect. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42343620/

[2] Agarwal P, Yogarayan S, Sayeed MS, et al. Multi-View Transformers for Structure-Aware HA-NA Drift Risk Scoring and Mutation Hotspot Mapping. Viruses. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42043210/

[3] Ahn JJ, Yu TC, Dadonaite B, et al. Influenza hemagglutinin subtypes have different sequence constraints despite sharing extremely similar structures. Virus Evol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41971714/

[4] Song W, Wang C, Xie W, et al. Identification of a Key Hemagglutinin Mutation Mediating Antibody Escape in Influenza A(H1N1)pdm09 Viruses. Viruses. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41902257/

[5] Yang H, Carney PJ, Chang JC, et al. Structural and Molecular Characterization of the Hemagglutinin from the Fifth-Epidemic-Wave A(H7N9) Influenza Viruses. J Virol. 2018. URL: https://pubmed.ncbi.nlm.nih.gov/29848588/ *** 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.

[6] Dosey A, Dadonaite B, Gillespie RA, et al. Stabilization of the H5 clade 2.3.4.4b hemagglutinin improves vaccine-elicited neutralizing antibody responses in mice. Sci Transl Med. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41779867/