Structural Dynamics of Avian Influenza Hemagglutinin: Molecular Modeling and Receptor Binding Predictions for Pandemic Risk Assessment

Introduction

Avian influenza viruses remain a persistent threat to poultry production systems and represent a reservoir for zoonotic emergence. The hemagglutinin (HA) surface glycoprotein is the primary determinant of host range and transmissibility, mediating viral entry through binding to sialic acid (SA) receptors on host epithelial cells [1, 2]. Avian influenza viruses preferentially bind to SA linked via an alpha-2,3 glycosidic bond to galactose (SA-alpha-2,3-Gal), whereas human influenza viruses exhibit specificity for SA-alpha-2,6-Gal [3, 4]. The molecular basis of this receptor binding preference is governed by the topology and electrostatic properties of the HA receptor binding site (RBS), which comprises the 190-helix, the 130-loop, and the 220-loop [5, 6].

A comprehensive understanding of the structural dynamics governing HA receptor binding specificity is essential for pandemic risk assessment. Computational approaches, including molecular dynamics (MD) simulations, homology modeling, and molecular docking, have become indispensable tools for characterizing HA-receptor interactions at atomic resolution [7, 8]. These methods enable the prediction of how specific amino acid substitutions modulate binding affinity and selectivity, thereby informing surveillance efforts and risk evaluation frameworks [9, 10]. This article reviews the current state of computational virology applied to avian influenza HA, focusing on structural modeling, receptor binding predictions, and the implications for pandemic risk assessment in veterinary and zoonotic contexts.

Hemagglutinin Structure and Receptor Binding Site Architecture

The HA trimer is a class I viral fusion protein composed of three identical monomers, each comprising a globular head domain (HA1) and a stem domain (HA2) [5, 11]. The receptor binding site is located in the membrane-distal globular head of HA1 and is formed by three secondary structural elements: the 130-loop (residues 135-138), the 190-helix (residues 190-198), and the 220-loop (residues 221-228) [6, 12]. A conserved hydrophobic pocket at the base of the RBS accommodates the acetyl group of sialic acid, while hydrogen bonding networks with conserved residues (e.g., Tyr98, Trp153, His183, and Tyr195) stabilize the bound ligand [13].

The structural basis for avian versus human receptor specificity lies in the conformation of the 220-loop and the identity of residues at positions 226 and 228 (H3 numbering) [5, 9]. In avian-adapted HAs, residue 226 is typically Gln (Q) and residue 228 is Gly (G), resulting in a narrow RBS that accommodates the trans conformation of the alpha-2,3-linked sialyloligosaccharide [7, 10]. In human-adapted HAs, substitutions to Leu (L) at position 226 and Ser (S) at position 228 widen the RBS and stabilize the cis conformation preferred by alpha-2,6-linked receptors [8, 9]. These mutations are considered critical markers of mammalian adaptation and pandemic potential [2, 5].

Molecular dynamics simulations have revealed that the RBS is not a rigid scaffold but exhibits considerable conformational flexibility that influences ligand binding kinetics [2, 6]. Kephart et al. demonstrated that host switching mutations in H5N1 HA suppress site-specific activation dynamics, altering the conformational sampling of the RBS and modulating receptor binding breadth [2]. Similarly, Cueno et al. identified distinct structural patterns re-occurring across H5N1 strains from 1959 to 2023, suggesting conserved dynamic motifs that may facilitate host range expansion [6].

Molecular Dynamics Simulations of HA-Receptor Interactions

MD simulations provide atomistic insight into the time-dependent behavior of HA-receptor complexes, capturing conformational changes, hydrogen bond dynamics, and solvation effects that are inaccessible to static crystallographic structures [2, 12]. The application of MD to avian influenza HA typically involves the construction of a simulation system containing the HA trimer (or the RBS domain), a glycan receptor analog (e.g., LSTa for alpha-2,3-sialyllactose or LSTc for alpha-2,6-sialyllactose), explicit water molecules, and neutralizing counterions [7, 13].

Early computational studies by Li and Wang employed MD simulations to characterize the binding of H5N1 HA with SA-alpha-2,3-Gal and SA-alpha-2,6-Gal, revealing that the avian-adapted HA formed more stable hydrogen bonds with the alpha-2,3-linked receptor [13]. More recently, Macchi et al. combined nuclear magnetic resonance (NMR) spectroscopy with MD simulations to study the interaction between H7 HA and glycan cell surface receptors, demonstrating that the recognition protein H7 exhibits differential binding modes dependent on glycan presentation [12].

The use of MD simulations to evaluate the impact of specific point mutations on receptor binding has become a standard approach in pandemic risk assessment [2, 5]. Good et al. showed that a single mutation in dairy cow-associated H5N1 viruses increased receptor binding breadth, expanding the range of glycans recognized by HA [5]. This finding underscores the importance of continuous structural surveillance, as single amino acid changes can dramatically alter receptor specificity and host tropism [2, 5].

Table 1 summarizes selected MD simulation studies of avian influenza HA-receptor interactions, highlighting the subtypes analyzed, the computational methods employed, and the key findings.

Table 1. Representative Molecular Dynamics Studies of Avian Influenza HA-Receptor Interactions

Homology Modeling and Template-Based Structure Prediction

Homology modeling remains a cornerstone of structural bioinformatics for avian influenza HA, particularly when experimentally determined structures are unavailable for emerging variants [3, 10]. The construction of a reliable homology model requires a suitable template structure with high sequence identity (typically >70% for HA), followed by target-template alignment, model building, loop refinement, and energy minimization [6, 8].

Singh et al. conducted a comprehensive evolutionary and structural analysis of H5N1 clade 2.3.4.4b HA across multiple hosts, employing homology modeling to predict the three-dimensional structures of HA variants and mapping sequence variations onto the RBS [3]. This approach enabled the identification of host-specific mutations and the assessment of their potential impact on receptor binding [3]. Similarly, Zhou et al. used computational analysis to predict the receptor binding specificity of novel influenza A/H7N9 viruses, combining homology modeling with docking simulations to evaluate the binding affinities of HA variants for avian and human receptors [10].

The accuracy of homology models depends critically on the quality of the sequence alignment and the selection of appropriate templates [8, 10]. For HA subtypes that exhibit high structural conservation (e.g., H5, H7, and H9), public repositories such as the Protein Data Bank (PDB) provide ample templates for model construction [3, 6]. The integration of homology modeling with MD simulation allows for the refinement of initial models and the assessment of conformational stability under physiological conditions [2, 12].

Molecular Docking and Receptor Binding Prediction

Molecular docking simulations predict the preferred orientation and binding affinity of a ligand (e.g., a sialyloligosaccharide) within the HA RBS [4, 7, 13]. Docking studies are widely employed to evaluate the receptor binding specificity of avian influenza HA variants and to predict the likelihood of mammalian adaptation [5, 10].

Xu et al. performed a comparative docking simulation study to assess the adaptability of H9N2 avian influenza A virus to humans, demonstrating that H9N2 HA variants carrying the Q226L substitution exhibited enhanced binding to alpha-2,6-linked receptors [7]. Obadan et al. investigated the flexibility of amino acid 226 in the RBS of an H9 subtype influenza A virus and its effect on virus replication, tropism, and transmission, providing experimental validation of computational predictions [9]. These studies illustrate the value of docking simulations as a screening tool for identifying high-risk HA variants [7, 9].

Khan et al. applied an in silico docking approach to repurpose nucleoside analogs as inhibitors targeting key proteins of the avian H5N1 clade 2.3.4.4b, demonstrating that computational screening can identify compounds with differential binding affinities for HA variants [4]. Although this study focused on antiviral repurposing, the docking methodology is directly transferable to receptor binding prediction [4].

Figure 1 presents a schematic workflow for computational pandemic risk assessment based on HA structural dynamics and receptor binding predictions.

flowchart TD
    A[Sequence Surveillance], > B[Phylogenetic Analysis]
    B, > C[Subtype Classification]
    C, > D{Homology Modeling}
    D, > E[Template Selection]
    E, > F[Model Building & Refinement]
    F, > G[MD Simulation of HA-Receptor Complex]
    G, > H[Docking of Sialyloligosaccharides]
    H, > I[Binding Free Energy Calculation]
    I, > J{Receptor Specificity?}
    J, >|Alpha-2,3-Gal predominant| K[Avian-adapted phenotype]
    J, >|Alpha-2,6-Gal binding detected| L[Increased pandemic risk]
    L, > M[Key Mutation Analysis]
    M, > N[Q226L, G228S, other substitutions]
    N, > O[Risk Categorization]
    O, > P[Surveillance & Preparedness Actions]

Figure 1. Computational workflow for pandemic risk assessment based on HA structural dynamics and receptor binding predictions. Sequence data from public repositories (e.g., GISAID, NCBI) are subjected to phylogenetic analysis and subtype classification. Homology modeling is performed when experimental structures are unavailable, followed by MD simulations and docking studies to evaluate receptor binding specificity. The presence of human-adaptive mutations (e.g., Q226L, G228S) and detectable binding to alpha-2,6-linked receptors indicate increased pandemic risk.

Sequence Surveillance and Phylogenetic Analysis

Continuous sequence surveillance of avian influenza HA is fundamental to pandemic risk assessment [3, 8]. Global initiatives such as the Global Initiative on Sharing All Influenza Data (GISAID) and the NCBI Influenza Virus Resource provide comprehensive repositories of HA sequences from avian, human, and other mammalian hosts [1, 3]. Phylogenetic analysis of HA sequences enables the tracking of clade evolution, the identification of reassortment events, and the early detection of mutations associated with mammalian adaptation [1, 8].

Zhang et al. characterized a reassortant H9N2 avian influenza virus isolated from a human case, combining genomic sequencing with structural analysis to identify genetic elements that may facilitate interspecies transmission [1]. Lee et al. conducted a systematic analysis of HA passage bias sites and host specificity mutations, revealing that certain amino acid positions are consistently selected during viral adaptation to mammalian hosts [8]. These studies highlight the importance of integrating genomic surveillance with structural and computational analyses [1, 8].

Glycosylation patterns on the HA globular head also influence receptor binding specificity and immune evasion [11]. Kim et al. demonstrated that glycosylation of HA and neuraminidase serves as a signature for ecological spillover and adaptation among influenza reservoirs, with specific glycan attachments modulating the accessibility of the RBS [11].

Table 2 lists key amino acid positions in HA that are associated with host range and receptor binding specificity, based on computational and experimental studies.

Table 2. Key HA Residues Associated with Host Range and Receptor Binding Specificity

Implications for Pandemic Risk Assessment

The integration of computational structural biology with genomic surveillance provides a powerful framework for pandemic risk assessment [2, 3, 5]. Avian influenza subtypes H5, H7, and H9 are considered to have the greatest pandemic potential due to their documented ability to infect humans and their prevalence in poultry populations [1, 3, 7]. The detection of human-adaptive mutations in the HA RBS, particularly at positions 226 and 228, warrants elevated surveillance and risk categorization [5, 8, 9].

Kephart et al. emphasized that host switching mutations in H5N1 HA suppress site-specific activation dynamics, suggesting that structural rigidity in the RBS may be a prerequisite for efficient binding to human receptors [2]. Good et al. demonstrated that a single mutation in dairy cow-associated H5N1 viruses increased receptor binding breadth, underscoring the rapid pace at which avian viruses can acquire mammalian-adaptive traits [5]. The detection of H5N1 clade 2.3.4.4b in multiple mammalian hosts, including dairy cattle, represents a significant development in the ecology of highly pathogenic avian influenza [3, 5].

Computational models also enable the prospective evaluation of emerging variants before experimental characterization is complete [10, 12]. Zhou et al. applied computational docking to predict the receptor binding specificity of novel H7N9 viruses, demonstrating that in silico predictions correlate with experimentally determined binding profiles [10]. Such predictive capacity is invaluable for triaging high-risk variants for further experimental investigation [10, 12].

Limitations and Future Directions

Despite the power of computational approaches, several limitations must be acknowledged. MD simulations are computationally intensive and are typically limited to microsecond timescales, which may be insufficient to capture slow conformational rearrangements [2, 12]. Force field accuracy and solvent model selection can influence simulation outcomes, and the absence of glycosylation or other post-translational modifications in many models may affect binding predictions [11, 12]. Docking algorithms, while efficient, may not fully account for receptor flexibility or water-mediated hydrogen bonding [4, 13].

Future directions include the integration of machine learning with MD simulations to predict binding affinities more accurately, the development of coarse-grained models for longer timescale simulations, and the incorporation of glycosylation and other modifications into structural models [5, 11]. Enhanced sampling techniques, such as metadynamics and replica exchange MD, offer the potential to explore the conformational landscape of HA more thoroughly [2, 12]. The continued deposition of high-resolution HA structures from emerging variants will further improve the reliability of homology models and docking predictions [3, 6].

Conclusions

Computational structural biology provides a critical framework for understanding the molecular determinants of avian influenza HA receptor binding specificity and pandemic risk. MD simulations, homology modeling, and molecular docking enable the atomic-level characterization of HA-receptor interactions, the prediction of host-adaptive mutations, and the prospective evaluation of emerging viral variants [2, 4, 7, 13]. Sequence surveillance and phylogenetic analysis, supported by public repositories such as GISAID and NCBI, remain essential for the early detection of high-risk HA variants [1, 3, 8]. The integration of these computational and surveillance approaches is vital for informing veterinary diagnostics, biosecurity measures, and pandemic preparedness strategies. Continued advances in computational methods and structural data availability will further enhance the predictive capacity of these tools for pandemic risk assessment.

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] Kephart SM, Awatramani KF, Saunders MI, et al. Host Switching Mutations in H5N1 Influenza Hemagglutinin Suppress Site-specific Activation Dynamics. bioRxiv. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41279505/

[3] Singh K, Malik YS, Hemida MG. Comprehensive Evolutionary and Structural Analysis of the H5N1 Clade 2.4.3.4b Influenza a Virus Based on the Sequences and Data Mining of the Hemagglutinin, Nucleoprotein and Neuraminidase Genes Across Multiple Hosts. Pathogens. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41011764/

[4] Khan MY, Shah AU, Duraisamy N, et al. Repurposing of Some Nucleoside Analogs Targeting Some Key Proteins of the Avian H5N1 Clade 2.3.4.4b to Combat the Circulating HPAI in Birds: An In Silico Approach. Viruses. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40733589/

[5] Good MR, Fernández-Quintero ML, Ji W, et al. A single mutation in dairy cow-associated H5N1 viruses increases receptor binding breadth. Nat Commun. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39737954/

[6] Cueno ME, Kamio N, Imai K. Avian influenza A H5N1 hemagglutinin protein models have distinct structural patterns re-occurring across the 1959-2023 strains. Biosystems. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39349133/

[7] Xu H, Qian J, Song Y, et al. The adaptability of H9N2 avian influenza A virus to humans: A comparative docking simulation study. Biochem Biophys Res Commun. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/32819606/

[8] Lee RTC, Chang HH, Russell CA, et al. Influenza A Hemagglutinin Passage Bias Sites and Host Specificity Mutations. Cells. 2019. URL: https://pubmed.ncbi.nlm.nih.gov/31443542/

[9] Obadan AO, Santos J, Ferreri L, et al. Flexibility In Vitro of Amino Acid 226 in the Receptor-Binding Site of an H9 Subtype Influenza A Virus and Its Effect In Vivo on Virus Replication, Tropism, and Transmission. J Virol. 2019. URL: https://pubmed.ncbi.nlm.nih.gov/30567980/

[10] Zhou X, Zheng J, Ivan FX, et al. Computational analysis of the receptor binding specificity of novel influenza A/H7N9 viruses. BMC Genomics. 2018. URL: https://pubmed.ncbi.nlm.nih.gov/29764421/

[11] Kim P, Jang YH, Kwon SB, et al. Glycosylation of Hemagglutinin and Neuraminidase of Influenza A Virus as Signature for Ecological Spillover and Adaptation among Influenza Reservoirs. Viruses. 2018. URL: https://pubmed.ncbi.nlm.nih.gov/29642453/

[12] Macchi E, Rudd TR, Raman R, et al. Nuclear Magnetic Resonance and Molecular Dynamics Simulation of the Interaction between Recognition Protein H7 of the Novel Influenza Virus H7N9 and Glycan Cell Surface Receptors. Biochemistry. 2016. URL: https://pubmed.ncbi.nlm.nih.gov/27933797/

[13] Li M, Wang B. Computational studies of H5N1 hemagglutinin binding with SA-alpha-2, 3-Gal and SA-alpha-2, 6-Gal. Biochem Biophys Res Commun. 2006. URL: https://pubmed.ncbi.nlm.nih.gov/16844080/ *** 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.