Deep Mutational Scanning and Computational Modeling of Avian Influenza Hemagglutinin for Zoonotic Risk Prediction

Introduction

The emergence of zoonotic influenza A viruses from avian reservoirs represents a persistent threat to animal and public health. The hemagglutinin (HA) glycoprotein is the primary determinant of host range and transmissibility, mediating viral entry by binding to sialic acid receptors on host epithelial cells [1, 2]. Avian influenza viruses preferentially bind α2,3-linked sialic acids, whereas human-adapted viruses bind α2,6-linked sialic acids [2]. A small number of amino acid substitutions in the HA receptor-binding site (RBS) can switch this preference, enabling cross-species transmission [1, 2]. Predicting which mutations confer zoonotic potential is a central challenge in computational virology.

Deep mutational scanning (DMS) has emerged as a powerful experimental technique to systematically measure the functional effects of all single amino acid mutations in a protein [2]. When applied to avian influenza HA, DMS generates comprehensive fitness landscapes that quantify how each mutation affects receptor binding, thermostability, and antibody escape [2, 3]. Integrating these empirical data with computational structural modeling and molecular dynamics (MD) simulations enables residue-level prediction of zoonotic risk [4, 5]. This article reviews the integration of DMS data with computational approaches to predict the zoonotic potential of avian influenza HA mutations, with a focus on receptor-binding specificity, immune evasion, and structural stability.

Deep Mutational Scanning of Hemagglutinin

DMS involves generating a library of HA variants, each containing a single amino acid substitution, and subjecting the library to a functional selection [2]. High-throughput sequencing quantifies the enrichment or depletion of each variant under the selection pressure, producing a fitness score for every mutation [2]. For avian influenza HA, DMS libraries have been constructed for multiple subtypes, including H1, H3, H5, H7, and H9 [2, 5]. These experiments reveal that different HA subtypes have distinct sequence constraints despite sharing highly similar three-dimensional structures [2].

Ahn et al. demonstrated that the fitness landscapes of different HA subtypes are shaped by subtype-specific epistatic interactions and structural constraints [2]. For example, residues in the RBS show high mutational tolerance in some subtypes but strong constraint in others, reflecting differences in receptor-binding geometry and glycan specificity [2]. These DMS data provide a quantitative baseline for identifying mutations that are both tolerated and functionally advantageous for human receptor binding.

Receptor-Binding Specificity and Key Mutations

The switch from avian α2,3-linked sialic acid preference to human α2,6-linked sialic acid preference is a critical step in zoonotic emergence [1, 2]. Two canonical mutations in the HA RBS, Q226L and G228S (H3 numbering), are well-established determinants of this switch [2]. Q226L replaces a glutamine with a leucine at position 226, altering the hydrophobic character of the binding pocket and favoring α2,6-linked receptors [2]. G228S introduces a serine that forms additional hydrogen bonds with human-type receptors [2].

DMS experiments have systematically quantified the effects of these and other RBS mutations across subtypes [2]. For H5 and H7 subtypes, the Q226L mutation alone is often insufficient to confer full human-type receptor binding; additional mutations in the 190-helix and 130-loop are required [2]. Pei et al. identified thermostability mutations S84F, G167N, and D168N in H9N2 HA that potentiate viral transmission in warming environments, highlighting the interplay between receptor binding and environmental stability [1]. These findings underscore the need to consider multiple biophysical properties when predicting zoonotic risk.

Computational Structural Modeling and Molecular Dynamics

Computational structural modeling complements DMS by providing mechanistic explanations for observed mutational effects [4, 5]. Homology modeling and AlphaFold-based structure prediction generate high-resolution models of HA from sequence data [5]. These models are used to dock sialic acid analogs and calculate binding free energies using molecular mechanics generalized Born surface area (MM-GBSA) or Poisson-Boltzmann surface area (MM-PBSA) methods [4].

MD simulations capture the dynamic behavior of HA in complex with receptor analogs [4]. Simulations of wild-type and mutant HA reveal how mutations alter the conformational ensemble of the RBS, affecting the accessibility and orientation of key contact residues [4]. Agarwal et al. developed multi-view transformers that integrate structural features from MD trajectories with sequence-based features to score HA-NA (neuraminidase) drift risk and map mutation hotspots [4]. This approach identifies residues that are structurally coupled to the RBS and whose mutation may allosterically modulate receptor specificity [4].

Immune Escape Prediction

Antibody-mediated immune pressure drives antigenic drift in HA, and mutations that confer immune escape can also affect receptor binding and zoonotic potential [3]. DMS experiments that include antibody neutralization as a selective pressure map the complete escape landscape for monoclonal antibodies and polyclonal sera [3]. Song et al. identified a key HA mutation mediating antibody escape in H1N1pdm09 viruses, demonstrating that a single substitution can abrogate neutralization by a major epitope [3].

Computational models predict escape mutations by analyzing the structural interface between HA and antibody paratopes [4, 3]. Docking simulations and free energy perturbation calculations estimate the impact of HA mutations on antibody binding affinity [4]. Multi-view transformer models incorporate both structural and evolutionary information to predict which residues are most likely to undergo antigenic drift [4]. These predictions inform vaccine strain selection and surveillance priorities.

Thermostability and Environmental Fitness

HA thermostability is a critical determinant of viral fitness in different host environments [1]. Avian influenza viruses replicate at the higher body temperature of birds (41-42 degrees Celsius) compared to mammals (37 degrees Celsius) [1]. Mutations that increase HA thermostability can enhance transmission in mammalian hosts, particularly in warming climates [1].

Pei et al. demonstrated that H9N2 HA mutations S84F, G167N, and D168N increase thermostability and promote aerosol transmission in a ferret model at elevated ambient temperatures [1]. These mutations are located in the HA1 domain near the RBS and the fusion peptide region [1]. Computational prediction of thermostability changes using tools such as FoldX or Rosetta can prioritize mutations for experimental validation [1, 5]. Dosey et al. showed that stabilizing mutations in H5 clade 2.3.4.4b HA improve vaccine-elicited neutralizing antibody responses in mice, illustrating the dual role of stability in both transmission and immunogenicity [5].

Integration with Genomic Surveillance

Global genomic surveillance platforms such as GISAID provide real-time data on circulating avian influenza strains [1, 4]. Integrating DMS-derived fitness landscapes with surveillance sequences enables rapid risk assessment of emerging variants [4]. When a novel HA sequence is deposited, its mutations can be mapped onto the DMS fitness landscape to identify substitutions that are tolerated and potentially advantageous for human adaptation [2].

Computational pipelines that combine sequence alignment, structural modeling, and DMS scoring can generate automated risk scores for each new variant [4]. Agarwal et al. demonstrated that multi-view transformer models trained on DMS data and structural features outperform sequence-only models in predicting HA-NA drift risk [4]. These pipelines can be deployed in veterinary diagnostic laboratories to flag high-risk variants for further experimental characterization.

Workflow for Zoonotic Risk Prediction

The following Mermaid diagram illustrates an integrated workflow combining DMS, computational modeling, and genomic surveillance for zoonotic risk prediction.

flowchart TD
    A[Genomic Surveillance GISAID], > B[HA Sequence Retrieval]
    B, > C[Deep Mutational Scanning Data]
    C, > D[Fitness Landscape Mapping]
    D, > E[Identify Tolerated Mutations]
    
    B, > F[Homology Modeling / AlphaFold]
    F, > G[Receptor Docking Simulations]
    G, > H[Binding Free Energy Calculation]
    
    B, > I[Molecular Dynamics Simulations]
    I, > J[Conformational Dynamics Analysis]
    J, > K[Receptor Specificity Prediction]
    
    E, > L[Multi-View Transformer Risk Scoring]
    H, > L
    K, > L
    
    L, > M[Zoonotic Risk Classification]
    M, > N[High Risk: Experimental Validation]
    M, > O[Low Risk: Continued Surveillance]

Key Mutations and Their Structural Context

The table below summarizes key HA mutations implicated in zoonotic risk, their structural location, and their functional effects as determined by DMS and computational modeling.

Limitations and Future Directions

Despite significant progress, several limitations remain. DMS experiments are typically performed in vitro using pseudotyped viruses or recombinant proteins, which may not fully recapitulate the in vivo fitness landscape [2]. Epistatic interactions between mutations are not captured by single-mutant DMS, although combinatorial DMS libraries are beginning to address this gap [2]. Computational models rely on accurate structural templates, which may not be available for all HA subtypes or for HA in complex with diverse receptor analogs [4].

Future directions include the development of deep learning models that directly predict fitness landscapes from sequence without requiring experimental DMS data for every new variant [4]. Integration with neuraminidase (NA) dynamics is also critical, as HA-NA balance is a key determinant of viral fitness [4]. Agarwal et al. have pioneered multi-view transformers that jointly model HA and NA drift risk [4]. Expanding these approaches to include other viral proteins and host factors will further improve zoonotic risk prediction.

Conclusion

Deep mutational scanning provides comprehensive empirical fitness landscapes for avian influenza hemagglutinin, quantifying the effects of all single amino acid mutations on receptor binding, thermostability, and immune escape. Computational structural modeling and molecular dynamics simulations offer mechanistic explanations for these effects and enable prediction of receptor-binding specificity and antigenic drift. Integration of DMS data with global genomic surveillance through machine learning models such as multi-view transformers allows real-time risk assessment of emerging variants. This combined experimental and computational framework is essential for predicting zoonotic spillover and guiding veterinary surveillance and vaccine development.

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] 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/

[3] 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/

[4] 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/

[5] 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/ *** 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.