Structural Prediction and Evolutionary Dynamics of Avian Influenza Hemagglutinin Using Deep Learning and Molecular Dynamics

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-05-10

Key Takeaways

• Deep learning models like AlphaFold2 and RosettaFold are now capable of predicting avian influenza hemagglutinin (HA) structures with high accuracy, even for strains lacking experimental templates, facilitating the characterization of emerging viral variants.
• Molecular Dynamics (MD) simulations, utilizing force fields such as CHARMM36m and Amber ff14SB, are crucial for elucidating HA's conformational dynamics, including low-pH-induced fusion peptide extrusion and receptor binding mechanisms.
• Integration of sequence surveillance data from GISAID with structural modeling allows for the rapid identification of mutations in HA that confer altered antigenicity or host tropism, critical for tracking immune evasion.
• Experimental deep mutational scanning (DMS) coupled with machine learning models, enhanced by structure-based features, can predict the functional impact of HA mutations on receptor binding and antibody escape.
• Computational pipelines combining deep learning structure prediction with MD simulations and sequence data analysis are instrumental in assessing the zoonotic potential of avian influenza viruses and guiding vaccine strain selection for pandemic preparedness.

Introduction

Avian influenza viruses (AIVs) of the Orthomyxoviridae family represent a persistent threat to poultry health and global food security [<a href="#ref-1">1</a>]. The viral hemagglutinin (HA) glycoprotein mediates host cell entry by binding to sialic acid receptors and facilitating membrane fusion [<a href="#ref-2">2</a>]. HA is also the primary target of neutralizing antibodies, making it the focal point of antigenic drift and vaccine design [<a href="#ref-3">3</a>]. Accurate prediction of HA three-dimensional (3D) structure and its conformational dynamics is essential for understanding receptor binding specificity, immune evasion, and zoonotic potential [<a href="#ref-4">4</a>]. Recent advances in deep learning and molecular dynamics (MD) simulations have transformed the ability to model HA structure and evolution at atomic resolution, complementing traditional X-ray crystallography and cryo-electron microscopy [<a href="#ref-5">5</a>, <a href="#ref-6">6</a>].

This article reviews computational methodologies for predicting HA structure using deep learning models such as AlphaFold2 and RosettaFold, and for simulating conformational transitions through MD simulations. The integration of sequence surveillance data from platforms such as GISAID with structural modeling is discussed in the context of tracking mutations that alter antigenicity or host tropism [<a href="#ref-7">7</a>, <a href="#ref-8">8</a>]. Emphasis is placed on the application of these tools for vaccine strain selection and pandemic preparedness in veterinary medicine.

Deep Learning for Hemagglutinin Structure Prediction

AlphaFold2 and RosettaFold Architectures

Deep learning-based protein structure prediction has achieved near-experimental accuracy for many globular proteins, including viral glycoproteins [<a href="#ref-9">9</a>]. AlphaFold2 employs an end-to-end neural network that uses multiple sequence alignments (MSAs) and pairwise residue features to predict backbone coordinates and side-chain rotamers [<a href="#ref-10">10</a>]. For HA, AlphaFold2 reliably models the globular head domain (HA1) and the stem region (HA2), although flexible loops at the receptor-binding site (RBS) may show elevated predicted local distance difference test (pLDDT) scores [<a href="#ref-11">11</a>]. RosettaFold alternatively uses a SE(3)-equivariant transformer architecture that iteratively refines residue positions through a protein-specific energy function [<a href="#ref-12">12</a>]. Both methods can produce high-confidence models for HA subtypes that lack experimental templates, facilitating structural characterization of emerging strains [<a href="#ref-13">13</a>].

The accuracy of deep learning models depends on the depth and diversity of the MSA [<a href="#ref-14">14</a>]. For avian influenza HA, MSA construction often draws from full-length sequences deposited in public databases including GISAID and GenBank [<a href="#ref-15">15</a>]. Low-complexity regions, such as the signal peptide and transmembrane domain, are typically omitted to improve modeling [<a href="#ref-16">16</a>]. Post-prediction refinement using energy minimization or short MD equilibration further improves stereochemical quality and removes steric clashes [<a href="#ref-17">17</a>].

Application to Receptor-Binding Site and Antigenic Epitopes

The receptor-binding site of HA comprises a set of conserved residues (e.g., Y98, W153, H183, Y195 in H3 numbering) that coordinate sialic acid [<a href="#ref-18">18</a>]. Deep learning models can capture the spatial arrangement of these residues and predict how substitutions at positions 226 and 228 (H3 numbering) alter binding preference for α2,3-linked (avian) versus α2,6-linked (mammalian) sialic acids [<a href="#ref-19">19</a>]. Structural models of HA from H5N1, H7N9, and H9N2 subtypes have been used to map antibody escape mutations at five major antigenic sites (A, B, C, D, E in H3; or homologous sites in H5) [<a href="#ref-20">20</a>]. Changes in surface electrostatic potential and solvent-accessible surface area can be computed from predicted models and correlated with reduced neutralization titers [<a href="#ref-21">21</a>].

Molecular Dynamics Simulations of HA Conformational Dynamics

Force Fields and Simulation Protocols

MD simulations provide atomistic trajectories of HA in explicit solvent over nanosecond to microsecond timescales [<a href="#ref-22">22</a>]. Commonly used force fields include CHARMM36m and Amber ff14SB, with TIP3P or TIP4P water models and physiological ionic strength (150 mM NaCl) [<a href="#ref-23">23</a>]. Simulations are typically initiated from crystal structures or deep learning models after protonation state assignment at pH 7.4 using pKa prediction tools [<a href="#ref-24">24</a>]. The HA trimer is embedded in a lipid bilayer (e.g., POPC or a viral membrane mimic) when studying membrane fusion mechanisms [<a href="#ref-25">25</a>].

Conformational Changes Related to Membrane Fusion

The low-pH-induced conformational rearrangement of HA2 (the fusion peptide) is a critical event during viral entry [<a href="#ref-26">26</a>]. MD simulations at pH 5.0 have revealed the transition of the B loop (residues 56-76) from a loop to a helix, driving the extrusion of the fusion peptide toward the target membrane [<a href="#ref-27">27</a>]. Steered MD and umbrella sampling calculations estimate the free energy barriers for this transition, which can be altered by mutations such as D112G in H5N1 HA that increase pH threshold and enhance fusogenicity [<a href="#ref-28">28</a>]. Replica exchange MD and Markov state models further characterize intermediate states along the fusion pathway [<a href="#ref-29">29</a>].

Receptor Binding Dynamics and Free Energy Landscapes

Binding of HA to sialyloligosaccharide receptors is governed by hydrogen bonding, van der Waals contacts, and water-mediated interactions [<a href="#ref-30">30</a>]. MD simulations with explicit glycan ligands (e.g., 3′-sialyllactose for avian receptors, 6′-sialyllactose for human receptors) can compute binding free energies using methods such as molecular mechanics generalized Born surface area (MM-GBSA) and free energy perturbation [<a href="#ref-31">31</a>]. Simulations of H5N1 HA mutants (e.g., Q226L, G228S) show a shift in binding preference from α2,3 to α2,6 receptors, consistent with mammalian adaptation [<a href="#ref-32">32</a>]. Principal component analysis (PCA) of HA trajectories reveals collective motions of the 190-helix and 130-loop that modulate receptor access [<a href="#ref-33">33</a>].

Integration of Sequence Surveillance with Structural Modeling

Tracking Mutations through GISAID

Continuous genomic surveillance of AIVs through GISAID provides real-time data on HA sequence diversity [<a href="#ref-34">34</a>]. Phylogenetic analysis coupled with structural annotation allows rapid identification of mutations at functionally important sites. For example, the H5N1 clade 2.3.4.4b HA acquired substitutions at antigenic sites (e.g., S133A, T156A in H5 numbering) that correlate with vaccine escape in poultry [<a href="#ref-35">35</a>]. Structural models built for each new clade enable prospective assessment of antibody neutralization breadth [<a href="#ref-36">36</a>].

Deep Mutational Scanning and Machine Learning

Experimental deep mutational scanning (DMS) of HA libraries quantifies the effects of single amino acid substitutions on receptor binding, antibody escape, and viral fitness [<a href="#ref-37">37</a>]. Machine learning models trained on DMS data, such as EVE and deep sequence models, predict mutational effects for novel sequences [<a href="#ref-38">38</a>]. Structure-based features (e.g., residue depth, local packing density, distance to epitope) improve prediction of antigenic escape compared to sequence-only models [<a href="#ref-39">39</a>]. Combining AlphaFold2-predicted structures with graph neural networks further enhances variant effect prediction on HA stability and binding [<a href="#ref-40">40</a>].

Implications for Vaccine Strain Selection and Pandemic Preparedness

In Silico Antigenic Cartography

Antigenic cartography translates hemagglutination inhibition (HI) assay data into 2D maps of antigenic distance [<a href="#ref-41">41</a>]. Structure-based mapping using predicted HA epitope conformations can supplement experimental HI titers, especially for emerging subtypes where antisera are limited [<a href="#ref-42">42</a>]. Clustering of structurally similar HA strains allows selection of vaccine candidates that cover circulating antigenic variants [<a href="#ref-43">43</a>]. MD-derived epitope flexibility scores inform which residues are immunodominant and likely to mutate under vaccine pressure [<a href="#ref-44">44</a>].

Risk Assessment of Zoonotic Spillover

Computational models that integrate receptor binding dynamics (from MD) with host range determinants (e.g., presence of avian-like receptors in the upper respiratory tract) are used to rank AIV subtypes by pandemic risk [<a href="#ref-45">45</a>]. For instance, H7N9 and H5N1 viruses with HA mutations enabling α2,6 binding are flagged for enhanced surveillance in poultry and live bird markets [<a href="#ref-46">46</a>]. Structural prediction pipelines fed with weekly GISAID data can automatically flag high-risk mutations and generate reports for veterinary authorities.

Workflow Overview

The following Mermaid diagram illustrates a typical computational pipeline for HA structure prediction, MD simulation, and evolutionary analysis.

flowchart TD
 A["GISAID Sequence Database"] --> B["Multiple Sequence Alignment and Phylogenetic Analysis"]
 B --> C["Deep Learning Structure Prediction: AlphaFold2 / RosettaFold"]
 C --> D["Structure Quality Assessment: pLDDT, Ramachandran"]
 D --> E["Molecular Dynamics Simulations: Force Field Selection, Solvation, Equilibration"]
 E --> F["Receptor Binding Free Energy Calculation: MM-GBSA / FEP"]
 E --> G["Conformational Analysis: PCA, Markov State Models"]
 F --> H["Antigenic Epitope Mapping and Escape Prediction"]
 G --> H
 H --> I["Vaccine Strain Candidate Selection"]
 H --> J["Pandemic Risk Scoring: Receptor Preference, Immune Escape"]
 I --> K["Antigenic Cartography Update"]
 J --> K
 K --> L["Integration with Surveillance Reports for Veterinary Authorities"]

Key Computational Tools and Resources

The table below summarizes major tools used in HA structural prediction and dynamics.

Discussion and Future Directions

Deep learning models have democratized access to high-quality HA structures, enabling computational virology labs without access to synchrotron facilities to perform meaningful analyses [<a href="#ref-47">47</a>]. However, limitations remain in modeling glycan shield heterogeneity and the conformational plasticity of hypervariable loops [<a href="#ref-48">48</a>]. MD simulations are computationally intensive; coarse-grained models and machine learning potentials offer faster alternatives for capturing large-scale rearrangements [<a href="#ref-49">49</a>]. Integration of AlphaFold2-predicted structures with enhanced sampling MD techniques (e.g., Hamiltonian replica exchange) may improve the accuracy of free energy landscapes for mutant HA [<a href="#ref-50">50</a>].

The ultimate goal is a real-time surveillance system that automatically submits new HA sequences from GISAID, predicts structure, runs short MD simulations to evaluate receptor binding and antibody accessibility, and outputs an antigenic risk report. Such systems are under development in several veterinary research institutes and promise to accelerate vaccine strain updates during outbreaks.

References

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