Computational Prediction of Antigenic Evolution in Influenza A Virus Using Machine Learning

1. Introduction

Influenza A virus (IAV) circulates in a wide range of veterinary hosts including swine, poultry, and equids, imposing substantial economic burdens on livestock production and posing zoonotic risks through interspecies transmission [1]. The viral hemagglutinin (HA) glycoprotein is the primary target of neutralizing antibodies, and its continuous evolution under immune pressure leads to antigenic drift [2]. Antigenic drift necessitates frequent updates of vaccine strains, a process that traditionally relies on serological assays such as hemagglutination inhibition (HI) tests and antigenic cartography [1, 3]. These methods are labor-intensive, costly, and often retrospective. Machine learning (ML) approaches trained on HA sequence data and antigenic measurements offer a high-throughput, prospective alternative for predicting antigenic evolution and guiding vaccine strain selection [4, 5, 3].

This article reviews the current state of computational methods for predicting antigenic drift in IAV using ML, with emphasis on applications relevant to veterinary medicine, including swine and avian hosts. We discuss sequence representation strategies, neural network architectures, structural modeling integration, and experimental validation. For broader context, see also the site articles on Computational Modeling of Viral Glycoprotein Evolution: Predicting Antigenic Drift Using Machine Learning and Machine Learning-Driven Prediction of Antigenic Drift in Influenza A Hemagglutinin Using Structural Dynamics and Sequence Surveillance.

2. Biological Basis of Antigenic Drift and Data Sources

Antigenic drift arises from the accumulation of point mutations in HA epitopes, particularly in the globular head domain surrounding the receptor-binding site [6, 2]. In swine, H3N2 and H1N1 subtypes undergo rapid antigenic evolution, complicating vaccine efficacy [1]. In poultry, H5Nx and H9N2 subtypes show similar drift patterns [5]. Antigenic cartography, which translates HI titers into a two-dimensional map of antigenic distances, provides the continuous output variable for many ML models [4, 1]. The HA1 domain sequences (approximately 330 residues) serve as the primary input, optionally supplemented with structural features such as solvent accessibility and B-factor [7, 2].

3. Sequence Encoding Strategies

Raw amino acid sequences must be converted into numerical representations amenable to ML algorithms. Zhou et al. [7] introduced a context-free encoding scheme that converts each amino acid into a vector of physicochemical properties (hydrophobicity, polarity, volume, etc.) and uses a sliding window to capture local epitope context. This method outperforms one-hot encoding for small training sets typical of veterinary IAV subtypes [7]. Other approaches use position-specific scoring matrices (PSSMs) derived from multiple sequence alignments or evolutionary conservation scores [5, 2]. More recently, foundation protein language models (e.g., transformer-based embeddings) have been applied to capture long-range dependencies in HA sequences [6]. Bukhari and Ogudo [6] demonstrated that embeddings from pre-trained protein language models, when fine-tuned on IAV epitope data, improve T-cell epitope prediction and can be adapted for B-cell epitope mapping relevant to antigenic drift.

4. Machine Learning Architectures for Antigenic Prediction

4.1 Convolutional Neural Networks (CNNs)

Convolutional neural networks excel at detecting local sequence motifs. Yin et al. [5] developed IAV-CNN, a 2D CNN that converts HA1 sequences into image-like matrices using physicochemical encoding and applies convolutional filters to identify antigenic variant patterns. The model was trained on HI data for H3N2 and accurately distinguished antigenic clusters [5]. A related 2D CNN approach by Lee et al. [3] incorporated residue-level attention to highlight epitope positions, achieving high concordance with antigenic cartography for human H3N2. Extending these principles to swine H3N2 and avian H5 subtypes is straightforward given the shared HA structure [1].

4.2 Multi-Task Learning

Antigenic evolution is multivariate: each virus strain has a distinct antigenic fingerprint relative to reference strains. Cai et al. [4] introduced FluPMT, a multi-task learning framework that predicts antigenic distances between a query strain and multiple reference strains simultaneously. By sharing a common HA sequence encoder but using separate task-specific output layers, FluPMT captures correlated antigenic features and improves generalization for rare subtypes [4]. This model directly supports vaccine strain selection by ranking candidate strains according to predicted antigenic coverage.

4.3 Transformer and Protein Language Models

Transformer architectures with self-attention mechanisms model dependencies across the entire HA sequence. Bukhari and Ogudo [6] showed that a transformer fine-tuned on IAV hemagglutinin sequences can predict antigenic sites without explicit epitope annotation. The attention weights reveal positions contributing most to antigenic change, often mapping to known epitope clusters [6]. Such models can be extended to predict the impact of individual mutations on antigenic properties, a capability analogous to deep mutational scanning.

5. Integrating Structural Information

Structural modeling enhances prediction by mapping sequence changes to three-dimensional epitope positions. Ren et al. [2] used homology models of H1N1 HA to identify surface-exposed residues in the Sa, Sb, Ca1, and Ca2 antigenic sites and then applied random forest classifiers to predict antigenic relevance based on residue physicochemical changes at those sites. Similarly, integrating predicted structures from AlphaFold or other deep learning tools (see Structural Prediction and Evolutionary Dynamics of Avian Influenza Hemagglutinin Using Deep Learning and Molecular Dynamics) allows extraction of features such as residue depth, solvent-accessible surface area, and B-factor [5, 1]. These structural features can be incorporated as additional input channels in CNNs or as fixed embeddings for tree-based models. A summary of representative methods is provided in Table 1.

Table 1. Representative machine learning methods for IAV antigenic drift prediction.

6. Workflow for Computational Antigenic Drift Prediction

The typical computational pipeline integrates sequence acquisition, feature engineering, model training, and output interpretation. Figure 1 illustrates a generalized workflow.

flowchart TD
    A[HA sequence data from surveillance], > B[Multiple sequence alignment]
    B, > C[Feature extraction: physicochemical, evolutionary, structural]
    C, > D[Training set: paired sequences + antigenic distances\nfrom HI cartography]
    D, > E[Train ML model (CNN, transformer, multi-task)]
    E, > F[Model validation on held-out HI data]
    F, > G{Performance satisfactory?}
    G, >|Yes| H[Predict antigenic distances for novel sequences]
    G, >|No| C
    H, > I[Strain ranking for vaccine candidate selection]
    I, > J[Experimental validation: HI assay in ferret\nor swine sera]
    J, > K[Update antigenic map and retrain]

The workflow begins with HA sequence data collected from active surveillance in swine or avian populations [1, 3]. Multiple sequence alignment ensures positional homology. Feature extraction encodes each residue position using physicochemical properties, evolutionary conservation, or structural descriptors from three-dimensional models [7, 2]. The training targets are antigenic distances derived from HI cartography, which are continuous values for regression tasks or discrete cluster labels for classification [4, 5]. After model training and validation, predictions for new sequences are used to rank emerging drift variants. The top candidates can be tested experimentally in animal models (e.g., swine or ferrets) to confirm antigenic phenotype [1].

7. Experimental Validation in Veterinary Species

Validation of computational predictions is essential for clinical utility. Zeller et al. [1] conducted an exemplary study in which ML predictions of antigenic drift in swine H3N2 were experimentally validated using HI assays with post-infection ferret antisera (a surrogate for swine immune sera). The model, trained on historical HI data, correctly identified emerging drift variants before they dominated the population [1]. This prospective validation demonstrates that such pipelines can outperform purely phylogenetic approaches. In poultry, similar validation using chicken or duck antisera would be required, though such systematic studies remain scarce.

8. Challenges and Future Directions

Several challenges persist. First, antigenic cartography requires extensive pairwise HI titers, which are costly to generate for each new subtype [4, 1]. Second, host-specific differences in immune pressure and glycosylation patterns affect drift dynamics; models trained on human data may not directly transfer to swine or avian hosts without retraining [6, 5]. Third, the emergence of novel reassortant viruses can produce antigenic shifts that sequence-based models cannot predict from drift alone [7].

Future developments include integrating deep mutational scanning data to calibrate mutation-level fitness effects (see Predicting Antibody Escape Mutations in Influenza A Virus Using Deep Mutational Scanning and Machine Learning) and incorporating glycoprotein dynamics from molecular dynamics simulations [1]. Furthermore, linking to a 3D Protein Viewer (e.g., a built-in NGL Viewer) could allow users to visualize predicted mutation impacts on HA structure, enhancing interpretability for veterinary virologists.

9. Conclusion

Machine learning models, trained on hemagglutinin sequences and antigenic cartography data, offer powerful tools for predicting antigenic drift in influenza A virus. Convolutional neural networks, multi-task learning frameworks, and transformer-based protein language models have each demonstrated success in classifying antigenic variants and ranking vaccine candidates [6, 4, 5, 3]. Integration of structural features from homology models and deep learning structure prediction further improves accuracy [2]. Prospective validation in swine and poultry settings is critical for translating these computational approaches into routine vaccine strain selection pipelines in veterinary medicine.

References

[1] Zeller MA, Gauger PC, Arendsee ZW, et al. Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine. mSphere. 2021. URL: https://pubmed.ncbi.nlm.nih.gov/33731472/

[2] Ren X, Li Y, Liu X, et al. Computational Identification of Antigenicity-Associated Sites in the Hemagglutinin Protein of A/H1N1 Seasonal Influenza Virus. PLoS One. 2015. URL: https://pubmed.ncbi.nlm.nih.gov/25978416/ *** 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.

[3] Lee EK, Tian H, Nakaya HI. Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks. Hum Vaccin Immunother. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/32750260/

[4] Cai C, Li J, Xia Y, et al. FluPMT: Prediction of Predominant Strains of Influenza A Viruses via Multi-Task Learning. IEEE/ACM Trans Comput Biol Bioinform. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/38498763/

[5] Yin R, Thwin NN, Zhuang P, et al. IAV-CNN: A 2D Convolutional Neural Network Model to Predict Antigenic Variants of Influenza A Virus. IEEE/ACM Trans Comput Biol Bioinform. 2022. URL: https://pubmed.ncbi.nlm.nih.gov/34469306/

[6] Bukhari SNH, Ogudo KA. Foundation Protein Language Models for Influenza A Virus T-Cell Epitope Prediction: A Transformer-Based Viroinformatics Framework. Viruses. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41902287/

[7] Zhou X, Yin R, Kwoh CK, et al. A context-free encoding scheme of protein sequences for predicting antigenicity of diverse influenza A viruses. BMC Genomics. 2018. URL: https://pubmed.ncbi.nlm.nih.gov/30598102/