Deep Learning for Predicting Viral Antigenic Drift from Sequence Surveillance

Introduction

Antigenic drift is the incremental accumulation of amino acid substitutions in viral surface glycoproteins that reduces or eliminates recognition by neutralizing antibodies elicited by prior infection or vaccination [1, 2]. This process is a central challenge in veterinary virology, particularly for pathogens such as influenza A viruses circulating in swine, poultry, and equine populations, where continuous immune evasion complicates vaccine strain selection and disease control strategies [3, 4]. The biological mechanism of antigenic drift involves point mutations primarily concentrated in antigenic epitopes of proteins such as hemagglutinin (HA) and neuraminidase (NA) for influenza A viruses [1, 5]. These mutations alter the electrostatic surface, hydrogen bonding networks, and side-chain packing at antibody-paratope interfaces, thereby reducing binding affinity and compromising neutralization [2, 4].

Traditional antigenic characterization relies on hemagglutination inhibition (HI) assays, which require paired virus-antisera titration experiments [6, 2]. These laboratory-based methods are labor-intensive, low-throughput, and often suffer from inter-laboratory variability [2]. Deep learning provides a computational framework that leverages the growing volume of genomic sequence data from surveillance programs to directly predict antigenic relationships, antibody escape sites, and fitness trajectories from sequence alone [1, 3, 6, 5]. This article reviews the architectures, training strategies, and interpretability methods that underpin deep learning approaches for predicting viral antigenic drift from sequence surveillance.

Deep Learning Architectures for Antigenic Drift Prediction

Convolutional Neural Networks and Bidirectional Long Short-Term Memory

Early deep learning models for antigenic variation prediction combined convolutional neural networks (CNNs) with bidirectional long short-term memory (BLSTM) networks to capture both local sequence motifs and long-range dependencies in HA amino acid sequences [1]. CNNs apply sliding convolutional filters that detect short, spatially contiguous patterns such as epitope-adjacent substitution motifs [1]. BLSTM networks propagate information bidirectionally through the sequence, enabling the model to learn dependencies between distant residues that may be proximal in the folded protein structure [1]. In an application to influenza A H3N2, a CNN-BLSTM hybrid achieved prediction agreements of 99.20% for strains from the forthcoming year and 96.46% for strains in the next two years on a chronological sequence dataset [1]. This architecture demonstrated that deep learning could successfully map sequence variation to antigenic phenotype with temporal generalizability [1].

Transformer-Based Architectures and Self-Attention

Transformer architectures have largely superseded recurrent networks for sequence-based modeling due to their ability to capture arbitrary-length dependencies via self-attention mechanisms [3, 2, 5]. The self-attention layer computes pairwise attention scores between all residues in a sequence, generating context-aware embeddings that weight the relative importance of each position for predicting antigenic outcome [5]. PREDAC-Transformer, an end-to-end framework integrating sequence, physicochemical, and evolutionary features, uses self-attention to identify long-range dependencies relevant to antigenicity [5]. This model introduced the integrated antigenicity score, which combines attention-based attribution with information-theoretic metrics to continuously quantify and rank the antigenic contributions of individual amino acid sites [5]. PREDAC-Transformer successfully recapitulated major historical antigenic cluster transitions and identified two classes of key sites: global key sites with sustained impact on antigenic evolution and cluster-transition determining sites that drive cluster transitions [5]. These sites included most canonical epitopes and revealed additional functional residues potentially influencing immune escape via cooperative effects or glycosylation [5].

End-to-end transformer models have also been developed to predict hemagglutination inhibition (HI) titers directly from viral genetic sequences [2]. One such model achieved prediction error under two-fold, comparable to experimental variability in HI assays, enabling high-throughput augmentation of HI titrations across viral isolates [2]. This approach allowed construction of large-scale antigenic maps for both A(H3N2) and A(H1N1)pdm viruses, revealing previously obscured evolutionary patterns including prolonged temporal clustering and genetically diverse co-circulating subclades that formed single antigenic clusters [2].

Protein Language Models and Transformer Embeddings

Large Language Model Embeddings for Sequence Representation

The application of large language models (LLMs) to biological sequences has transformed the representation of viral genetic data for downstream prediction tasks [3, 6]. DNABERT-2, a nucleotide-level transformer model, can be fine-tuned on large curated sequence datasets to generate high-dimensional embeddings (e.g., 768-dimensional vectors) that capture fine-grained genetic variation relevant to antigenic drift [3]. These embeddings encode positional information, nucleotide context, and evolutionary conservation in a continuous latent space that can be used as input to predictive models [3]. A multi-modal framework integrating DNABERT-2 embeddings with time-series epidemiological data achieved perfect sequence classification (macro-F1 = 1.00, Matthews correlation coefficient = 1.00) for viral clade identification and strong forecasting performance for influenza-like illness incidence [3]. The mutation intensity index derived from these embeddings provided a continuous monitoring signal for antigenic drift dynamics [3].

Pre-Trained Gene Sequence Models for Cross-Immunity Prediction

Pre-trained gene sequence models such as the DNA Pretrained Cross-Immunity Protection Inference model (DPCIPI) address the challenge of limited labeled laboratory data for model training [6]. DPCIPI incorporates gene alignment and deduplication algorithms to preprocess sequences and a mutual information inference operator to focus model capacity on distinguishing features between input gene pairs [6]. This approach outperformed state-of-the-art models in predicting hemagglutination inhibition titer from influenza viral gene sequences, with improvements in binary cross-immunity prediction of 1.58% in F1 score, 2.34% in precision, 1.57% in recall, and 1.57% in accuracy [6]. For multilevel cross-immunity prediction, improvements were 2.12%, 3.50%, 2.19%, and 2.19% respectively [6]. The success of pre-trained models highlights the potential for transfer learning to revolutionize sequence-based prediction tasks in virology [6, 4].

Predicting Antibody Escape Sites and Evolutionary Trajectories

Identifying Escape Sites with Deep Learning Attribution Methods

Deep learning models provide interpretability through attribution methods that map predictive importance to individual residues [7, 2, 5]. Integrated gradients and SHAP (SHapley Additive exPlanations) are two widely used techniques that decompose the model's output into contributions from each input feature [3, 7]. These methods have been applied to identify mutation sites linked to antigenic cluster transitions that align with previous laboratory findings [2]. For A(H3N2), key mutations primarily occurred within major antigenic epitopes, including residues in the Sa, Sb, Ca1, Ca2, and Cb antigenic sites of hemagglutinin [2, 5]. For A(H1N1)pdm, fewer key mutations were identified, and some occurred outside recognized antigenic regions, suggesting alternative mechanisms of immune evasion [2].

The integrated antigenicity score in PREDAC-Transformer systematically ranks sites by their contribution to antigenic drift, enabling the identification of both canonical and previously overlooked residues that influence immune escape via cooperative effects or glycosylation [5]. This approach has been used to identify cluster-transition determining sites that drive antigenic changes between successive evolutionary clusters [5].

Predicting Clade-Level Fitness and Evolutionary Trajectories

Deep learning models can also predict relative transmission fitness of viral clades from complete genome sequences [7]. Convolutional neural networks trained on whole viral genomes predicted differential population growth rate (DPGR) with high accuracy: R-squared values of 0.9577 for H3N2 and 0.9871 for H1N1 [7]. Interpretation of these models using SHAP highlighted contributions from known hemagglutinin antigenic sites together with contributions from internal genes, suggesting that fitness is influenced by both antibody-driven selection and internal gene compatibility [7]. The Antigenic Change Risk Index (ACRI) provides a composite score identifying periods of elevated drift risk, integrating genetic and epidemiological signals [3].

flowchart TD
    A[Genomic Sequence Surveillance Data], > B[Sequence Preprocessing and Alignment]
    B, > C[Protein Language Model Embedding Generation]
    C, > D[Transformer Self-Attention Encoding]
    D, > E{Output Task}
    E, > F[Antigenic Cluster Classification]
    E, > G[Hemagglutination Inhibition Titer Regression]
    E, > H[Clade Fitness / DPGR Prediction]
    E, > I[Escape Site Identification]
    
    F, > J[Antigenic Map Construction]
    G, > J
    H, > J_2[Evolutionary Trajectory Forecasting]
    I, > K[3D Structural Mapping of Escape Mutations]
    
    subgraph Interpretability
        L[Integrated Gradients / SHAP / Integrated Antigenicity Score]
    end
    
    C, > L
    D, > L
    L, > M[Residue-Level Attribution]
    M, > K
    M, > N[Antigenic Change Risk Index (ACRI)]
    N, > J_2

Highlighting Predicted Escape Sites in Three-Dimensional Structure

The mapping of predicted antibody escape sites onto three-dimensional protein structures is a critical step for translating sequence-based predictions into mechanistic virological insight [4, 5]. Predicted escape residues identified by attribution methods are mapped onto experimentally determined or computationally predicted structures (e.g., via AlphaFold or homology modeling) [4, 5]. The structural context reveals whether predicted sites are solvent-exposed on antibody-accessible surfaces, buried within the protein core, or located at subunit interfaces [4]. Key mutation sites for influenza A hemagglutinin that drive antigenic cluster transitions consistently map to surface-exposed loops and helices within the globular head domain that constitute canonical antigenic epitopes Sa, Sb, Ca1, Ca2, and Cb [2, 5].

Structural mapping also highlights positions where mutations are unlikely to affect antigenicity, such as core residues that maintain protein stability [4]. The cooperative effects of mutations at spatially adjacent but sequence-distant residues can be evaluated by measuring the distance between predicted sites in the folded protein [5]. This integrative analysis strengthens the genotype-phenotype interpretation by linking sequence variation to structural biology and immune escape mechanisms [4, 5]. Deep learning approaches can further incorporate structural features directly, such as Multi-View Transformers that integrate sequence and structural information for structure-aware HA-NA drift risk scoring and mutation hotspot mapping [8].

Integrative Frameworks and Multi-Modal Architectures

Combining Genetic and Epidemiological Data

The most powerful antigenic drift prediction frameworks integrate genomic sequence data with epidemiological time-series data to improve forecasting accuracy [3, 7]. The Temporal Fusion Transformer (TFT) combines nucleotide-level LLM embeddings with ILI incidence data and laboratory-confirmed case counts to predict future incidence up to eight weeks ahead [3]. This multi-modal architecture uses variable selection networks, gating mechanisms, and attention layers to weigh the relative importance of genetic and epidemiological signals over time [3]. Explainability analyses confirm that the model attends to biologically meaningful features consistent with known antigenic sites [3].

Predictive Uncertainty and Conformal Prediction

Quantifying predictive uncertainty is essential for using deep learning models in operational surveillance and vaccine policy decisions [7]. Conformal prediction provides a distribution-free framework for generating prediction intervals with guaranteed coverage [7]. In the context of DPGR prediction, conformal prediction intervals quantify the reliability of fitness predictions for newly emerged clades, allowing decision-makers to assess confidence before acting on model outputs [7].

Challenges and Future Directions

Sequence Data Quality and Sampling Bias

The accuracy of deep learning models for antigenic drift prediction depends on the quality, diversity, and representativeness of training data [3, 4]. Geographic sampling bias, incomplete metadata, and uneven surveillance capacity across regions limit model generalizability [4]. In veterinary contexts, surveillance data for swine and avian influenza may be sparser and less temporally granular than human influenza data, requiring domain adaptation or transfer learning from well-characterized systems [4].

Model Interpretability and Biological Validation

While attribution methods provide candidate escape sites, experimental validation through deep mutational scanning or in vitro neutralization assays remains essential to confirm predicted effects [2, 4]. The relationship between predicted antigenic importance and actual antibody escape is complex, involving polyclonal sera, glycan shielding, and epistatic interactions among mutations [5]. Integrating predicted escape sites with Deep Learning-Driven Protein Language Models for Predicting Antigenic Drift in Influenza A Hemagglutinin structures can refine accuracy [4].

Transferability to Other Veterinary Pathogens

The architecture and methodologies developed for influenza A are theoretically transferable to other rapidly evolving veterinary viruses with surface antigens under immune selection, such as porcine respiratory coronavirus, equine influenza virus, and avian paramyxoviruses [2, 4]. Comparative cross-species studies using pre-trained models could accelerate antigenic characterization for these pathogens [4]. Detailed computational approaches described in resources such as Deep Learning-Driven Structural Prediction of Viral Envelope Glycoproteins can be adapted for novel viral families. Additionally, tools from Machine Learning for Predicting T-Cell Epitope Immunogenicity may help refine vaccine design by predicting cellular immune responses [9].

Summary

Deep learning provides a powerful computational framework for predicting viral antigenic drift from sequence surveillance data [1, 3, 6, 2, 5]. Transformer architectures and protein language models capture complex sequence dependencies and generate informative embeddings for downstream prediction tasks [3, 5]. Attribution methods map predictive importance to individual residues, enabling identification of antibody escape sites and cluster-transition determinants [7, 2, 5]. These predictions can be visualized in three-dimensional structural contexts to provide mechanistic insight [4, 5]. Multi-modal integration of genomic sequences with epidemiological data improves forecasting accuracy [3, 7], while conformal prediction frameworks provide uncertainty quantification [7]. Despite challenges related to data quality, sampling bias, and the need for experimental validation, deep learning approaches are becoming integral to surveillance systems for vaccine strain selection and antigenic monitoring in veterinary virology [2, 4, 8].

References

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[8] Agarwal P, Yogarayan S, Sayeed MS, et al. Multi-View Transformers for Structure-Aware HA-NA Drift Risk Scoring and Mutation Hotspot Mapping. Viruses. 2026. https://pubmed.ncbi.nlm.nih.gov/42043210/ *** 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.

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