Deep Mutational Scanning and Structural Modeling of Avian Influenza HA: Predicting Zoonotic Risk from Computational Binding Landscapes
By Dr. Zubair Khalid, DVM, MS, PhD ·
Key Takeaways
- Deep Mutational Scanning (DMS) systematically quantifies the functional impact of every single amino acid mutation in avian influenza Hemagglutinin (HA), revealing mutations that alter receptor-binding specificity from avian (SAα2,3Gal) to human (SAα2,6Gal) types.
- Computational structural modeling, utilizing tools like AlphaFold2 and Rosetta, predicts the biophysical consequences of these mutations on HA-receptor interactions, providing a 3D context for functional data.
- Integration of DMS data with structural models generates computational binding landscapes, mapping mutation effects onto HA structure to identify high-risk candidates for zoonotic spillover.
- Key structural determinants of host range include residues at positions 226 and 228 in the HA receptor-binding site, where specific substitutions (e.g., Q226L, G228S) are critical for switching receptor preference.
- Machine learning and AI are increasingly employed to predict HA receptor-binding specificity and pathogenicity directly from sequence and structural features, enhancing risk assessment capabilities.
- Experimental validation using glycan microarrays, surface plasmon resonance, and viral pseudotype entry assays is crucial to confirm computational predictions and build robust zoonotic risk assessment frameworks.
Introduction
Avian influenza viruses, particularly those of the H5N1 and H7N9 subtypes, represent a persistent threat to poultry health and a potential source of zoonotic spillover events [<a href="#ref-1">1</a>, <a href="#ref-2">2</a>]. The primary molecular barrier to cross-species transmission is the binding specificity of the viral hemagglutinin (HA) glycoprotein for host cell surface sialic acid receptors [<a href="#ref-3">3</a>]. Avian influenza viruses preferentially bind to alpha-2,3-linked sialic acids (SAα2,3Gal), which are abundant in the intestinal and respiratory tracts of birds, whereas human-adapted influenza viruses bind to alpha-2,6-linked sialic acids (SAα2,6Gal) prevalent in the human upper respiratory tract [<a href="#ref-3">3</a>, <a href="#ref-4">4</a>]. A switch in HA receptor-binding specificity from SAα2,3Gal to SAα2,6Gal is a critical step for zoonotic transmission and pandemic potential [<a href="#ref-4">4</a>, <a href="#ref-5">5</a>].
Deep mutational scanning (DMS) has emerged as a powerful experimental technique to systematically measure the functional impact of every possible single amino acid mutation in a protein [<a href="#ref-6">6</a>, <a href="#ref-7">7</a>]. When applied to avian influenza HA, DMS generates comprehensive fitness landscapes that reveal which mutations are tolerated and which alter receptor-binding specificity [<a href="#ref-5">5</a>, <a href="#ref-6">6</a>]. These experimental data are increasingly integrated with computational structural models, such as those generated by Rosetta and AlphaFold2, to predict the biophysical consequences of mutations on HA-receptor interactions [<a href="#ref-8">8</a>, <a href="#ref-9">9</a>]. This article reviews the methodological framework for combining DMS data with structural modeling to construct computational binding landscapes that forecast zoonotic risk in avian influenza viruses.
Deep Mutational Scanning of Avian Influenza Hemagglutinin
Experimental Principles of DMS
Deep mutational scanning involves the construction of a library of viral HA variants, each containing a single amino acid substitution [<a href="#ref-6">6</a>]. This library is expressed on the surface of cells or in viral particles and subjected to a functional selection, such as binding to a specific receptor analog or antibody [<a href="#ref-7">7</a>, <a href="#ref-10">10</a>]. The relative enrichment or depletion of each variant after selection is quantified using high-throughput sequencing, yielding a functional score for each mutation [<a href="#ref-6">6</a>]. These scores are typically normalized to the wild-type sequence and can be interpreted as a measure of fitness or binding affinity [<a href="#ref-5">5</a>, <a href="#ref-6">6</a>].
For avian influenza HA, DMS experiments have been performed to assess the impact of mutations on receptor-binding specificity, thermostability, and antibody escape [<a href="#ref-7">7</a>, <a href="#ref-10">10</a>, <a href="#ref-11">11</a>]. For example, DMS of H5N1 HA has identified mutations in the receptor-binding site (RBS) that enhance binding to human-type SAα2,6Gal receptors while maintaining or reducing binding to avian-type SAα2,3Gal receptors [<a href="#ref-5">5</a>]. Similarly, DMS of H9N2 HA has revealed mutations that potentiate virus transmission in warming environments, linking thermostability to host range [<a href="#ref-11">11</a>].
Key Mutations Identified by DMS
Several canonical mutations in the HA RBS are known to modulate receptor-binding specificity. The Q226L and G228S substitutions (H3 numbering) in the HA1 subunit are among the most well-characterized switches that shift binding preference from SAα2,3Gal to SAα2,6Gal in H2 and H3 subtypes [<a href="#ref-4">4</a>, <a href="#ref-5">5</a>]. In H5N1 and H7N9 viruses, analogous mutations at these positions have been shown to enhance human-type receptor binding [<a href="#ref-5">5</a>, <a href="#ref-12">12</a>]. DMS data have confirmed that these mutations are highly enriched under selection for human-type receptor binding [<a href="#ref-5">5</a>]. Additional mutations, such as N158K, T160A, and S137A, have also been implicated in modulating receptor specificity and antigenicity [<a href="#ref-13">13</a>, <a href="#ref-14">14</a>].
The DMS approach has further revealed that the fitness landscape of HA is highly epistatic, meaning that the effect of a mutation depends on the genetic background [<a href="#ref-5">5</a>, <a href="#ref-6">6</a>]. For instance, the Q226L mutation may only confer a strong switch to SAα2,6Gal binding in the presence of permissive secondary mutations that stabilize the RBS [<a href="#ref-5">5</a>]. This epistasis complicates simple sequence-based predictions and underscores the need for structural modeling to interpret mutational effects in a three-dimensional context [<a href="#ref-6">6</a>, <a href="#ref-9">9</a>].
Structural Modeling of Hemagglutinin-Receptor Interactions
Computational Tools for Structure Prediction
High-resolution crystal structures of HA in complex with sialic acid receptor analogs provide the foundation for computational modeling [<a href="#ref-15">15</a>, <a href="#ref-16">16</a>]. However, for emerging viral strains or engineered variants, experimental structures may not be available. In such cases, homology modeling and deep learning-based methods such as AlphaFold2 are employed to generate reliable structural models of HA [<a href="#ref-8">8</a>, <a href="#ref-9">9</a>, <a href="#ref-15">15</a>]. AlphaFold2 has demonstrated remarkable accuracy in predicting protein structures, including the globular head domain of HA that contains the RBS [<a href="#ref-8">8</a>, <a href="#ref-9">9</a>].
Rosetta is another widely used suite for protein modeling and design [<a href="#ref-7">7</a>, <a href="#ref-8">8</a>]. Rosetta can be used to predict the structure of HA mutants, calculate binding free energies between HA and receptor analogs, and design stabilizing mutations [<a href="#ref-7">7</a>, <a href="#ref-8">8</a>]. The Rosetta energy function evaluates van der Waals interactions, hydrogen bonding, solvation, and electrostatic contributions to estimate the binding affinity of a protein-ligand complex [<a href="#ref-8">8</a>, <a href="#ref-17">17</a>].
Free Energy Perturbation and Molecular Dynamics
To quantitatively predict changes in receptor-binding affinity upon mutation, free energy perturbation (FEP) calculations are often employed [<a href="#ref-17">17</a>, <a href="#ref-18">18</a>]. FEP is a rigorous thermodynamic method that computes the difference in binding free energy between a wild-type and mutant complex by simulating the alchemical transformation of one residue into another [<a href="#ref-18">18</a>]. When applied to HA-receptor complexes, FEP can predict the relative binding affinity for SAα2,3Gal versus SAα2,6Gal receptors [<a href="#ref-17">17</a>, <a href="#ref-18">18</a>].
Molecular dynamics (MD) simulations complement FEP by providing insights into the conformational dynamics of the HA-receptor interface [<a href="#ref-5">5</a>, <a href="#ref-18">18</a>]. MD simulations can reveal how mutations alter the flexibility of key loops in the RBS, affect hydrogen bond networks, and modulate the overall stability of the HA trimer [<a href="#ref-4">4</a>, <a href="#ref-5">5</a>]. For example, MD simulations of H5N1 HA have shown that host-switching mutations suppress site-specific activation dynamics, thereby stabilizing the pre-fusion conformation required for receptor binding [<a href="#ref-5">5</a>].
Integrating DMS Data with Structural Models
Computational Binding Landscapes
The integration of DMS data with structural models yields computational binding landscapes that map the functional impact of mutations onto the three-dimensional structure of HA [<a href="#ref-6">6</a>, <a href="#ref-8">8</a>]. In this framework, each mutation is assigned a DMS-derived functional score, and its structural context is analyzed using Rosetta or AlphaFold2 models [<a href="#ref-6">6</a>, <a href="#ref-7">7</a>]. Mutations that cluster in the RBS and are predicted to alter binding free energy toward human-type receptors are flagged as high-risk candidates for zoonotic spillover [<a href="#ref-5">5</a>, <a href="#ref-8">8</a>].
A typical computational pipeline for constructing binding landscapes includes the following steps:
- DMS Data Generation: Perform a deep mutational scan of the HA gene, selecting for binding to SAα2,6Gal receptor analogs [<a href="#ref-5">5</a>, <a href="#ref-6">6</a>].
- Structure Prediction: Generate a structural model of the wild-type HA using AlphaFold2 or a crystal structure if available [<a href="#ref-8">8</a>, <a href="#ref-15">15</a>].
- Mutation Modeling: Introduce each single amino acid substitution into the structural model using Rosetta's fixed-backbone design or flexible backbone protocols [<a href="#ref-7">7</a>, <a href="#ref-8">8</a>].
- Binding Free Energy Calculation: Compute the binding free energy of each HA variant with SAα2,3Gal and SAα2,6Gal receptor analogs using Rosetta or FEP [<a href="#ref-8">8</a>, <a href="#ref-17">17</a>].
- Correlation Analysis: Correlate DMS functional scores with computed binding free energy changes to validate the computational model [<a href="#ref-6">6</a>, <a href="#ref-8">8</a>].
- Risk Scoring: Assign a zoonotic risk score to each mutation based on its DMS score, predicted binding affinity switch, and structural context [<a href="#ref-8">8</a>, <a href="#ref-9">9</a>].
Workflow Diagram
The following Mermaid diagram illustrates the integrated workflow for predicting zoonotic risk from DMS and structural modeling.
graph TD
A["HA Gene Library"] --> B["Deep Mutational Scanning"]
B --> C["Functional Scores for Each Mutation"]
D["Wild-Type HA Sequence"] --> E["AlphaFold2 / Rosetta Structure Prediction"]
E --> F["Structural Model of HA"]
C --> G["Correlation Analysis"]
F --> G
G --> H["Validated Computational Model"]
H --> I["Free Energy Perturbation / MD Simulations"]
I --> J["Predicted Binding Affinity for SAα2,3Gal vs SAα2,6Gal"]
J --> K["Zoonotic Risk Score Assignment"]
K --> L["High-Risk Mutation Identification"]
L --> M["Surveillance and Experimental Validation"]Predicting Zoonotic Spillover
Key Structural Determinants of Host Range
The receptor-binding site of HA is a shallow pocket formed by the 130-loop, 190-helix, and 220-loop [<a href="#ref-4">4</a>, <a href="#ref-5">5</a>]. The identity of residues at positions 226 and 228 is critical for determining receptor specificity [<a href="#ref-4">4</a>, <a href="#ref-5">5</a>]. In avian-adapted HAs, residue 226 is typically glutamine (Q), which forms a hydrogen bond with the SAα2,3Gal linkage [<a href="#ref-4">4</a>]. The substitution to leucine (L) at position 226 creates a hydrophobic environment that favors the SAα2,6Gal conformation [<a href="#ref-4">4</a>, <a href="#ref-5">5</a>]. Similarly, glycine at position 228 (G228) allows for a more open pocket that accommodates the SAα2,6Gal linkage, whereas serine (S228) can form additional hydrogen bonds with the receptor [<a href="#ref-4">4</a>].
DMS studies have shown that these canonical mutations are not sufficient on their own to confer full human-type receptor binding in all HA subtypes [<a href="#ref-5">5</a>, <a href="#ref-6">6</a>]. For example, in H5N1 clade 2.3.4.4b viruses, the Q226L and G228S mutations must be accompanied by additional changes in the 130-loop and 190-helix to achieve high-affinity binding to SAα2,6Gal [<a href="#ref-1">1</a>, <a href="#ref-5">5</a>]. Computational modeling has identified permissive mutations such as S137A, T160A, and N158K that stabilize the RBS and enhance the effect of the primary specificity-switching mutations [<a href="#ref-5">5</a>, <a href="#ref-13">13</a>].
Machine Learning and AI Integration
Recent advances in machine learning have enabled the prediction of HA receptor-binding specificity directly from sequence and structure [<a href="#ref-8">8</a>, <a href="#ref-9">9</a>]. AI-powered methods can identify human cell surface protein interactors of HA, providing a broader view of potential host range determinants beyond sialic acid binding [<a href="#ref-8">8</a>]. For instance, machine learning models trained on DMS data and structural features can predict the pathogenicity of avian influenza viruses with high accuracy [<a href="#ref-9">9</a>]. These models incorporate features such as electrostatic potential, hydrogen bond donor/acceptor density, and pocket volume at the RBS [<a href="#ref-8">8</a>, <a href="#ref-9">9</a>].
Template-based structure prediction combined with machine learning has been used to classify avian influenza viruses as high or low pathogenicity based on HA structural features [<a href="#ref-9">9</a>]. This approach leverages the fact that pathogenic mutations often cluster in specific structural regions, such as the RBS and the fusion peptide [<a href="#ref-4">4</a>, <a href="#ref-9">9</a>]. By integrating DMS data with these predictive models, researchers can prioritize viral strains for enhanced surveillance and experimental characterization [<a href="#ref-8">8</a>, <a href="#ref-9">9</a>].
Cross-Linking to Related Articles
For a broader discussion of how DMS and computational modeling are applied to other viral glycoproteins, readers are directed to the article on Deep Mutational Scanning and Computational Modeling of Avian Influenza Hemagglutinin for Zoonotic Risk Prediction. The principles of receptor-binding dynamics are further explored in Structural Dynamics of Avian Influenza Hemagglutinin: Molecular Modeling and Receptor Binding Predictions for Pandemic Risk Assessment. For a comparative perspective on coronavirus spike protein modeling, see Deep Mutational Scanning and Computational Modeling of SARS-CoV-2 Spike-ACE2 Binding Dynamics for Predicting Zoonotic Risk.
Limitations and Future Directions
Challenges in Computational Prediction
Despite significant progress, several limitations remain in the computational prediction of zoonotic risk from HA binding landscapes. First, DMS data are typically generated in vitro using simplified receptor analogs, which may not fully recapitulate the complex glycan environment of the human respiratory tract [<a href="#ref-6">6</a>, <a href="#ref-19">19</a>]. Second, the computational prediction of binding free energies using Rosetta or FEP is computationally expensive and can be sensitive to the force field parameters used [<a href="#ref-17">17</a>, <a href="#ref-18">18</a>]. Third, epistatic interactions between mutations are difficult to model accurately, and current methods may underestimate the impact of non-additive effects [<a href="#ref-5">5</a>, <a href="#ref-6">6</a>].
Need for Experimental Validation
Computational predictions must be validated experimentally using techniques such as glycan microarray binding assays, surface plasmon resonance, and viral pseudotype entry assays [<a href="#ref-3">3</a>, <a href="#ref-5">5</a>]. The integration of computational and experimental approaches is essential for building robust risk assessment frameworks [<a href="#ref-5">5</a>, <a href="#ref-8">8</a>]. Surveillance of circulating avian influenza viruses through platforms such as GISAID provides the sequence data necessary to apply these predictive models in real time [<a href="#ref-1">1</a>, <a href="#ref-20">20</a>].
Emerging Technologies
Advances in cryo-electron microscopy are providing high-resolution structures of HA in complex with native receptors, which can improve the accuracy of computational models [<a href="#ref-21">21</a>]. Additionally, the development of protein language models trained on large sequence databases is enabling the prediction of mutational effects without the need for explicit structural modeling [<a href="#ref-6">6</a>, <a href="#ref-8">8</a>]. These tools are likely to become increasingly important for rapid zoonotic risk assessment during outbreaks.
Conclusion
Deep mutational scanning and structural modeling of avian influenza HA provide a powerful framework for predicting zoonotic risk from computational binding landscapes. By integrating experimental functional scores with biophysical simulations, researchers can identify mutations that alter receptor-binding specificity and assess the pandemic potential of emerging viral strains. The continued refinement of these computational methods, combined with robust experimental validation and genomic surveillance, will enhance our ability to anticipate and mitigate the threat of avian influenza spillover into mammalian hosts.