Predicting Antibody Escape Mutations in Influenza A Virus Using Deep Mutational Scanning and Machine Learning

Introduction

Influenza A virus (IAV) remains a persistent threat to animal health, causing respiratory disease in swine, poultry, equids, and numerous other mammalian and avian species. The viral surface glycoprotein hemagglutinin (HA) is the primary target of host neutralizing antibodies, yet it evolves rapidly under immune pressure. This process, termed antigenic drift, enables IAV to escape preexisting immunity and necessitates frequent updates to veterinary vaccines [1]. Predicting which HA mutations confer antibody escape is a central challenge in computational virology. Recent advances combine deep mutational scanning (DMS) with machine learning (ML) to systematically map the fitness landscape of HA mutations and forecast future escape variants [2, 3, 4]. This review covers the experimental and computational components of such pipelines, with an emphasis on applications in veterinary virology.

Deep Mutational Scanning: Experimental Foundation

Deep mutational scanning is a high-throughput approach that measures the functional impact of thousands of single amino acid substitutions in a protein of interest. For IAV HA, DMS libraries are generated by introducing mutations across the HA1 domain (the globular head containing the receptor-binding site and major antigenic epitopes) and selecting the resultant variant viruses or pseudotyped particles under defined antibody pressure. Deep sequencing of input and output populations allows calculation of an escape fraction for each mutation: the proportion of viral particles carrying that mutation that survive neutralization [3, 4]. DMS experiments have identified key escape residues at antigenic sites Sa, Sb, Ca1, Ca2, and Cb for H1 subtypes, as well as analogous positions in H3 and H5 subtypes relevant to avian and swine hosts [1, 4]. For example, Song et al. [1] demonstrated that a single substitution at residue K163 in H1 HA (H1 numbering) conferred substantial escape from polyclonal sera in a ferret model of swine-origin IAV. Such data provide ground-truth labels for ML models.

Machine Learning Architectures for Escape Prediction

Several ML paradigms have been adapted to predict antibody escape from sequence and structural features. The models range from classical supervised classifiers to deep learning language models that embed protein sequences without explicit feature engineering.

Sequence-Only Models and Language Models

Classical approaches encode HA1 sequences using one-hot vectors, physicochemical properties (e.g., hydrophobicity, charge), or reduced amino acid alphabets. Forghani et al. [5] demonstrated that encoding using reduced alphabets (e.g., grouping amino acids by biochemical class) can achieve accuracy comparable to full 20-letter alphabets while providing interpretability regarding which physicochemical properties most influence antigenicity. Their analysis revealed that structural and charge characteristics are the most predictive features, and that non-antigenic sites neighboring known epitopes also contribute to antigenic variation [5].

More recently, deep language models such as BiLSTM and ProtBERT have been applied to HA sequences to learn distributed representations of mutations. Durazzi et al. [3] compared these language models with classical distance-based methods for reconstructing antigenic maps of influenza A(H3N2) viruses. Both BiLSTM and ProtBERT outperformed simpler models in ranking single substitutions according to their antigenic impact and in simulating DMS escape experiments, despite being trained solely on sequence data [3]. The protein embeddings generated by ProtBERT appear to capture higher-order interactions between residues that are not accessible from primary sequence alone.

Structure-Aware Models

Incorporating three-dimensional (3D) structural information improves prediction accuracy by considering residue contacts, surface exposure, and stability. The TRIAD-Influenza framework (Token–Residue–Integrated Architecture for Drift) exemplifies this approach [4]. TRIAD combines codon-level sequence representations, residue-level features, and structure-derived interaction graphs from predicted HA models. It outputs a continuous risk score for each HA–neuraminidase (NA) pair and identifies mutation hotspots using a contact-weighted mutation risk index (CMRI). On temporal cross-validation with over 300,000 HA/NA coding sequences from public databases, TRIAD achieved an AUROC of approximately 0.89 for classifying high-risk variants [4]. Notably, CMRI hotspots enriched known DMS escape residues with odds ratios of 2.7 to 3.6, validating the structure-aware approach [4].

Clade-Informed and Multi-View Transformers

Hu et al. [2] developed a clade-informed sequence transformer (CIST) that incorporates phylogenetic context into the attention mechanism. By representing the antigenic distance between circulating IAV strains and vaccine strains as a continuous target, CIST learns to weight mutations according to their clade-specific frequency. This framework is particularly valuable for veterinary surveillance where multiple subtypes (e.g., H1N1, H3N2, H5N1) circulate in different host species and require separate vaccine formulations.

Feature Engineering for Escape Prediction

Effective ML models depend on informative features. The following table summarizes commonly used feature categories derived from DMS data, structural modeling, and evolutionary analysis.

One key insight from Forghani et al. [5] is that mutations at positions not traditionally considered antigenic sites can nonetheless affect antigenicity by altering local backbone conformation or by being recognized as T-cell or B-cell epitopes. Therefore, a feature set limited to canonical epitope residues may miss important escape mechanisms.

Model Training and Validation

Training datasets for escape prediction are typically assembled from publicly available IAV sequences (e.g., from GISAID, NCBI Virus) paired with corresponding DMS data for a reference strain. The target variable can be either binary (escape vs. non-escape) or continuous (antigenic distance). For binary classification, random forest and gradient-boosted trees are common baselines, while neural networks are used for continuous regression or ranking [3, 4, 5].

Validation must account for temporal structure because IAV evolves chronologically. Standard k-fold cross-validation can overestimate performance due to leakage between closely related sequences. Instead, rolling-origin temporal cross-validation is recommended, where models are trained on sequences from earlier years and tested on later years [4]. The TRIAD study used this approach and reported an AUPRC of about 0.44 on a held-out test set with strong class imbalance (only 3.4% high-risk HA/NA pairs), indicating that the model effectively prioritized rare escape variants [4]. External validation on independent GISAID/Nextstrain cohorts (2023-2024) preserved discrimination with AUROCs of 0.85-0.86 [4].

Interpretability methods such as SHAP values or gradient saliency are essential for identifying which features drive predictions. In the context of veterinary vaccine updates, knowing the specific residues responsible for predicted escape allows experimental verification via hemagglutination inhibition (HI) assays [1, 4]. The TRIAD framework directly produces mutation hotspot maps that can be overlaid on 3D HA structures for visual inspection [4].

Implications for Veterinary Vaccine Strain Selection

Seasonal influenza vaccines for swine and poultry, as well as emergency vaccines for emerging strains (e.g., H5N1 in poultry), must be updated periodically to match circulating antigenic variants. Current surveillance relies largely on HI assay data, which are labor-intensive and require well-characterized reference antisera [5]. ML models trained on DMS data can prioritize novel HA sequences for experimental testing, accelerating the vaccine selection timeline.

For example, when a novel HA variant is detected in a swine herd, a trained model can immediately predict its antigenic distance from the current vaccine strain and highlight specific escape mutations. If the predicted escape residues map to the same positions previously identified by DMS (such as residue 163 in H1 [1]), the risk of vaccine mismatch is high, prompting early consideration of a strain update. Similarly, for avian influenza, structure-aware models can assess whether mutations in the HA globular head (e.g., at residue Q226 or G228 that also affect receptor binding) will simultaneously alter antibody recognition [4].

The integration of DMS with ML also informs therapeutic antibody design for veterinary use. Broadly neutralizing antibodies targeting the HA stem are being developed for influenza therapy in swine and poultry. By mutating all positions in the stem epitope and scoring escape using DMS, one can generate a comprehensive escape map. ML models can then predict whether a given antibody is robust to naturally occurring polymorphisms or whether a single mutation would render it ineffective.

Workflow Diagram

The following Mermaid diagram outlines the integrated computational pipeline from DMS data to predicted escape mutations.

flowchart TD
    A[DMS Experiment: HA variant library + antibody selection], > B[Deep sequencing: input vs. output]
    B, > C[DMS escape scores for each mutation]
    C, > D{Feature Engineering}
    D, > E[Sequence features: conservation, AAindex, reduced alphabets]
    D, > F[Structural features: solvent accessibility, contact maps]
    D, > G[Phylogenetic features: clade context, drift distance]
    E & F & G, > H[Machine Learning Model: e.g., random forest, Transformer, BiLSTM]
    H, > I[Cross-validation: temporal split, AUROC/AUPRC]
    I, > J[Model interpretation: saliency maps, CMRI, SHAP]
    J, > K[Predicted escape hotspot residues]
    K, > L[Experimental validation: HI assay with ferret/species-specific antisera]
    L, > M[Vaccine strain update recommendation]

Conclusion

The combination of deep mutational scanning and machine learning provides a powerful framework for predicting antibody escape mutations in influenza A virus hemagglutinin. Experimental DMS data yield high-resolution maps of mutational tolerance under immune pressure, while ML models generalize these patterns to novel sequences using features derived from sequence, structure, and evolution. Methods ranging from reduced amino acid encodings [5] to multi-view transformers [4] and language models [3] have demonstrated strong predictive performance, particularly when validated under realistic temporal constraints [4]. The identification of key escape residues, such as K163 in H1 HA [1], directly supports vaccine strain selection for swine and poultry influenza. As ongoing surveillance generates ever larger genomic datasets, these computational tools will become essential for maintaining effective veterinary vaccines against antigenically drifting IAV.

References

[1] 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/

[2] Hu K, Zhu Y, Zhang Q, et al. CIST: A clade-informed sequence transformer framework for predicting influenza virus antigenicity. Sci Rep. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42365024/

[3] Durazzi F, Koopmans M, Fouchier R, et al. Language models learn to represent antigenic properties of human influenza A(H3) virus. bioRxiv. 2025. URL: https://www.semanticscholar.org/paper/97714e6dea81ef61a52d228c271f3fbdc7ae264a

[4] Agarwal P, Yogarayan S, Sayeed M, et al. Multi-View Transformers for Structure-Aware HA–NA Drift Risk Scoring and Mutation Hotspot Mapping. Viruses. 2026. URL: https://www.semanticscholar.org/paper/aa38cecad9c9e8b8f01726ac177a24ddcda5f717

[5] Forghani M, Firstkov A, AlyanNezhadi MM, et al. Reduced amino acid alphabet-based encoding and its impact on modeling influenza antigenic evolution. Russian Journal of Infection and Immunity. 2022. URL: https://www.semanticscholar.org/paper/8e430441380429317ac5c949f19f29d66bb74f9d *** 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.