Integrating protein language and geometric deep learning models for enhanced vaccine antigen prediction

Domain: biology
Published: 2025-12-21
Editorial outcome: major_revision

Abstract
Vaccines are the most effective tool in preventing and managing infectious diseases. One of the critical challenges in vaccine development is the selection of suitable target antigens from the thousands of proteins produced by pathogens. Artificial intelligence is anticipated to play a significant role in addressing this challenge. In this study, we develop a framework termed PLGDL for protective antigen prediction that employs Protein Language and Geometric Deep Learning models. This framework leverages both primary sequence features and three-dimensional structural features of protein antigens, thereby reducing the biases associated with manually curated features. Our integrated model exhibits robustness across both constructed and public datasets and is applicable to viruses, bacteria, and eukaryotic pathogens. Notably, when applied to the ongoing Mpox outbreak, our model not only quickly identifies multiple known antigens but also discovers a protective antigen: G10R. Here, our study provides a high-performance screening tool for protective vaccine antigen prediction by synergistically utilizing the capabilities of protein language and geometric deep learning models, providing substantive insights and methodological advancements for rapid vaccine development.

Introduction
Vaccination is the cornerstone of global public health since it offers the most effective means to prevent and control infectious diseases1. The precise discovery of candidate antigens, which elicits specific protective immune responses, is fundamental to successful vaccine development11,12. Artificial intelligence has already led to incredible breakthroughs in various domains: from the game of Go to self-driving vehicles, to formal mathematical proofs3–6. It is also poised to play a significant role in the fight against infectious diseases, particularly in vaccine development7–10. Reverse vaccinology (RV), which leverages bioinformatics and machine learning approaches to systematically predict and identify candidate antigens from pathogen genomes, has emerged as a powerful filtering and prioritization tool that directs limited experimental resources toward the most promising candidates, thereby improving the overall efficiency of vaccine development2. Previous research has shown that the probability of proteins being vaccine antigens is strongly correlated with their biological and physicochemical properties13. Accordingly, existing machine learning RV approaches, such as the widely used VaxiJen and Vaxign-ML models, have relied on manually engineered features derived from protein sequences. These features include physicochemical properties (like molecular weight, isoelectric point, and hydrophobicity) and biological characteristics (like subcellular localization, adhesin probability, transmembrane helices, and signal peptides)14,15. While these handcrafted features demonstrate utility, they are inherently constrained by prior knowledge and may fail to capture more complex, multifaceted relations between sequence and antigenicity. The advent of deep learning models, which can automatically learn hierarchical feature representations from raw data, offers a promising alternative16. Such models can uncover intricate patterns and dependencies that are not readily apparent through traditional feature engineering. This shift from manual to automated feature extraction represents a major advancement in the field of RV, enabling more accurate and robust predictions of vaccine candidates, as evidenced by recent models like Vaxign-DL and Vaxi-DL17,18.

In recent years, it has been shown that features extracted from pre-trained, task-independent protein language models can greatly improve classification performance in many biological problems19–22. These models, often based on architectures such as transformers, are pre-trained on large datasets of protein sequences, enabling them to learn intricate patterns and dependencies inherent to the sequences. This pretraining equips the models with a deep understanding of the biochemical properties and evolutionary relationships that govern protein function23. One of the key advantages of protein language models is their ability to capture long-range dependencies and complex interactions within protein sequences. Traditional sequence-based features are generally limited by their reliance on predefined characteristics. In contrast, protein language models can autonomously derive high-dimensional feature representations that encapsulate both local and global sequence information24. By capturing the contextual and semantic information encoded in protein sequences, these models have the potential to improve antigen prediction beyond what can be achieved with traditional sequence-based features.

Furthermore, a protein’s structure is a critical determinant of its antigenic properties because the spatial arrangement of amino acids governs the accessibility and presentation of epitopes to the immune system25,26. However, current antigen prediction models primarily focus on one-dimensional (1D) sequence features and thus potentially neglect valuable structural information. This holistic approach, combining sequence and structure-based features, may offer a robust framework for advancing the field of RV. The recent progress in protein structure prediction, as exemplified by AlphaFold2 and AlphaFold3, provides an opportunity to extract structural features in an automated fashion27,28. This advancement opens new avenues for extracting structural features that were previously inaccessible. Although manually designing appropriate structural features is inherently difficult because of the complexity of protein folding, geometric deep learning models, which extend traditional deep learning methods to non-Euclidean spaces such as graphs and manifolds, have made significant strides in representing the three-dimensional (3D) characteristics of proteins29–33.

In the present study, we propose a framework called PLGDL, as shown in Fig. 1. It simultaneously leverages protein language and geometric deep learning models to integrate both sequence features and structural features for predicting protective vaccine antigens. On both constructed and public datasets, PLGDL outperforms traditional methods that rely solely on primary sequence features. Moreover, in response to the recent Mpox outbreak, declared a “Public Health Emergency of International Concern” by the WHO. Our model not only identifies multiple known antigens but also discovers a previously uncharacterized antigen, G10R, which shows neutralizing and partial protective efficacy against a lethal orthopoxvirus challenge. This finding highlights G10R’s potential as a component of new Mpox vaccines and supports ongoing vaccine development efforts. Our study presents a high-performance protective antigen prediction model by leveraging the combined strengths of protein language and geometric deep learning models. This integrated RV approach not only provides substantive insights but also represents a significant advancement in vaccine development. The practical value of our model is exemplified by its successful application to the Mpox virus, underscoring its potential to rapidly respond to emerging infectious disease threats.

Results
Establishment and evaluation of the antigen dataset
To develop our machine learning framework for vaccine antigen prediction, it was essential to first establish a comprehensive and diverse dataset of positive and negative protective antigens encompassing various typical pathogen types. In this study, we exclusively included protective antigens defined as proteins that meet the following criteria: demonstrated ability to induce protection against pathogen challenge in vivo or capacity to stimulate antigen-specific immune responses directly linked to protective outcomes34. This stringent criterion, applied to data from the Protegen Database and PubMed-curated studies, yielded a high-confidence set of 600 protective antigens after removing redundancies (composition: Virus 119, Bacteria 386, Eukaryota 95; Fig. 2a; Supplementary Fig. 1).

To reflect the natural scarcity of protective antigens, we constructed an imbalanced negative dataset by extracting all annotated protein sequences from the genomes of the pathogens listed in the UniProt database, randomly selecting and excluding homologous sequences to positive antigens35. The final curated dataset contains 6000 negative samples after removing redundancies (composition: Viral 481, Bacterial 4493, Eukaryotic 1026; Fig. 2a and Supplementary Fig. 1), establishing a 1:10 positive-negative ratio that mirrors biological reality. The antigen dataset encompasses a broad spectrum of sequence lengths (ranging from 60 to 1923 amino acids), ensuring its broad applicability across diverse biological contexts (Fig. 2b and Supplementary Data 1).

All 6600 protein structures were reanalyzed using AlphaFold3 (Fig. 2c)28. Structural confidence was evaluated using key metrics—pLDDT (Predicted Local Distance Difference Test), pTM (Predicted TM-Score), and Predicted Aligned Error (PAE), resulting in high-quality predictions (mean pLDDT = 81.69). Analyses revealed a weak negative correlation between pLDDT and protein length (R² = 0.0135, Fig. 2d), confirming consistent prediction quality across antigen sizes. Notably, pTM (mean = 0.731) and PAE (mean = 9.925 Å) exhibited a strong inverse correlation (R² = 0.8137, Fig. 2e), indicating that antigens with higher global structural confidence (pTM) display lower inter-domain positional uncertainty (PAE). This dataset represents a comprehensive structural resource for protective antigens, now publicly available to advance vaccine research (Supplementary Data 2).

Establishment of the PLGDL framework based on integrating sequence and structural features
Through systematic evaluation of state-of-the-art protein embedding methods, including ESM-223, ProTrans36, and AMPLIFY37, we identified ESM-2 as the optimal choice for antigen prediction tasks (Supplementary Table 1). Consequently, ESM-223 was adopted to generate residue-level feature embeddings from antigen sequences. These high-dimensional embeddings were subsequently refined through several feature selection pipelines, reducing dimensionality to 255 discriminative features, which were ultimately utilized as input for our antigen prediction framework (Supplementary Fig. 2 and Supplementary Table 2). The 3D structure of a protein ultimately determines its biological characteristics and physicochemical parameters, and it contains far more information than a 1D amino acid sequence. Compared to sequence features, protein structural features may be more indicative of the innate properties of antigens25,26. Therefore, the neighborhood-enhanced graph convolutional network (NEGCN) was established to accurately characterize protein structures and extract relevant feature vectors. To achieve this, we obtained a substantial dataset of 805,000 high-quality protein structures from the AlphaFold Protein Structure Database38. This extensive dataset enabled the development of a robust graph-based model capable of detailed structural characterization.

The NEGCN framework transformed the initial adjacency graphs, which represented spatial relationships among amino acids, into edge graphs. This transformation allowed us to encapsulate various connectivity types and the spatial arrangement of amino acids, categorized based on inter-amino acid angles. A four-layer neural network was employed to process these edge graphs and generate comprehensive feature vectors that effectively represented the 3D structure of proteins. Our model utilized three distinct message-passing methods to update node embeddings and produce a detailed and multi-faceted representation of each node. By averaging the cascaded representations across all nodes, we derived a singular, robust representation for the entire protein structure. The NEGCN model was trained using a contrastive learning approach to optimize the proximity of positive sample representations to target samples while distancing negative sample representations. This training method employed a contrastive loss function that facilitated the effective differentiation between structurally similar and dissimilar protein representations. The successful pre-training of the NEGCN model established a solid foundation for the downstream machine learning task of classifying and predicting vaccine antigens.

Using the pre-trained NEGCN model, we obtained 960-dimensional feature vectors of the structures for each protein in the antigen dataset. The learned structure feature vectors were then reduced to 27 dimensions via the feature selection pipeline. Subsequently, based on the protein sequence and structure feature vectors in the training set, multiple typical machine learning classification algorithms (i.e., ridge regression, balanced bagging, linear SVC, random forest, and XGBoost) were used for model training39–44.

The comparative evaluation of machine learning algorithms across viral, bacterial, and eukaryotic pathogens demonstrated the superior performance of XGBoost in antigen prediction tasks (Fig. 3 and Table 1). In bacterial antigen prediction, XGBoost exhibited exceptional discriminative power, attaining the highest Accuracy (0.946) and PR-AUC (0.660), alongside superior F1-score (0.624) and Matthew’s correlation coefficient (MCC = 0.598), outperforming random forest (F1 = 0.524, MCC = 0.507) and linear models (Fig. 3b). For eukaryotic pathogens, XGBoost maintained robust performance with the highest accuracy (0.942) and balanced MCC (0.600), significantly exceeding random forest (MCC = 0.383) in generalization capability (Fig. 3c). For viral targets, XGBoost achieved the highest accuracy (0.867) and Recall (0.458) among all classifiers, coupled with a competitive MCC of 0.532 despite dataset imbalance (Fig. 3d). Across all pathogen categories, XGBoost consistently delivered the strongest integrated metrics, including peak accuracy (0.949), F1-score (0.683), and MCC (0.663), while achieving the highest PR-AUC (0.692), a critical indicator of reliability in imbalanced classification (Fig. 3a). These data collectively validate XGBoost as the optimal model for cross-pathogen antigen prediction, particularly due to its ability to synergistically optimize discriminative power (ROC-AUC) and class-imbalance resilience (PR-AUC) through effective integration of sequence-structural features.

To empirically test our central hypothesis—that there exist learnable, pathogen-agnostic physicochemical and structural features correlated with protective antigenicity that are best captured from a diverse dataset—a rigorous cross-validation analysis was performed. In this analysis, “specialist” models were trained exclusively on pathogen-specific subsets of the data (i.e., a “Virus-only” model, a “Bacteria-only” model, and a “Eukaryota-only” model). The performance of these specialist models was then compared against our original “generalist” PLGDL model, which was trained on the complete, combined dataset, when evaluated on each pathogen-specific test set. The results, detailed in Supplementary Table 3, unequivocally demonstrate that the generalist model achieves superior or comparable performance across all pathogen classes, validating our integrated training strategy.

Evaluation of the PLGDL model on the constructed and third-party datasets
To evaluate the ability of our classification model to effectively distinguish between positive and negative protective antigens in the dataset, we conduct an embedding analysis. Considering that the representation embedding of such pre-trained models cannot measure the similarity between samples with naive metrics (e.g., Euclidean distance or inner product)45,46, we used the deep clustering technique for embedding analysis47. Specifically, we generate an unsupervised two-dimensional visualization of the embedding space by combining a contrastive learning framework with a neighbor embedding method. A visualization of the results is given in Fig. 4.

All four plots exhibited a clustering phenomenon between positive protective antigen and negative non-protective antigen groups, indicating that the features space learned by the model contains significant antigen information. The pronounced cluster boundaries in the embedding plots for the antigen database imply a higher degree of separability in the feature space, which directly translates to better classification performance. This suggests that PLGDL can effectively classify protective antigens and non-protective antigens based on these features.

Furthermore, we use the metric of cluster purity to quantify the quality of the clusters, i.e., quantifying how homogeneous each cluster is with respect to the ground truth labels. A high cluster purity value means the data points within each cluster predominantly belong to the same ground truth class, implying that the feature space is well-separated. The consistency between the clustering observed in the embedding analysis and the high classification accuracy reported in Table 1 reinforces the validity of the model’s approach and its applicability to real-world pathogen datasets. Notably, the Bacterial category achieved the highest cluster purity (0.980), aligning precisely with its superior classification accuracy in Table 1, a finding that underscores the model’s exceptional discriminative capability for bacterial antigen prediction. This robust clustering performance suggests that bacterial antigen features are both distinct and cohesive in the embedding space, enabling highly reliable classification. In contrast, the Viruses category exhibited slightly weaker clustering (purity = 0.957), which is likely attributable to their comparatively limited antigen dataset. The observed cluster homogeneity—particularly pronounced in bacterial and eukaryotic subgroups—validates the model’s ability to encode biologically meaningful protective antigenic signatures.

To further validate our methods, we conducted comparisons using a classical third-party standard dataset (Including 131 positive antigens and 115 negative antigens; Supplementary Data 3) against several benchmark antigens prediction methods: Vaxign, VaxiJen, Vaxijen3, Vaxign-ML, Vaxi-DL and Vaxign-DL14,15,17,18,48,49. Vaxign is the most traditional out of the group, and it leverages a rule-based approach combined with bioinformatics tools to predict bacterial vaccine antigens. Unlike Vaxign, VaxiJen does not rely on sequence alignment, which makes it faster and less computationally intensive. Moreover, it focuses on intrinsic properties like hydrophobicity and molecular weight. VaxiJen3 improves upon VaxiJen by incorporating more sophisticated algorithms and larger datasets for better predictive performance. Vaxign-ML represents a hybrid approach that combines the heuristic-based methods of Vaxign with the predictive power of machine learning, thus enabling more accurate antigen prediction. Meanwhile, Vaxi-DL and Vaxign-DL leverage the capabilities of deep learning to capture intricate sequence features that traditional and machine learning models might miss. It is especially effective in dealing with large and complex datasets, and it is a more robust and accurate prediction model.

The results detailed in Table 2 demonstrate that the PLGDL model exhibited superior predictive ability in identifying antigenic candidates for vaccine development; this is evidenced by its Precision, F1 score and MCC. Additionally, the deep learning algorithms based on amino acid sequence features, i.e., Vaxign-DL and Vaxi-DL, also showed promising performance. Overall, these results highlight the robustness and enhanced performance of the PLGDL model, which leverages the complementary strengths of sequence and structural feature fusion.

Prediction of candidate antigens for Mpox
Mpox virus is an enveloped double-stranded DNA virus with a brick-shaped morphology, and it is one of the largest viral particles50. It can propagate within a transmission cycle involving primary hosts, such as squirrels and rats, as well as incidental hosts, including non-human primates (Fig. 5a)50. Since 2022, it has continued to spread in Africa and threatens global health security51. The recent Mpox outbreak has been declared a “Public Health Emergency of International Concern” by the World Health Organization, marking the second such announcement in two years. Currently available vaccines to combat the outbreak include ACAM2000, JYNNEOS, and LC16m8, all of which are attenuated live vaccinia virus vaccines52–55. Limited by safety and accessibility, these vaccines are rarely used in high-risk areas, especially in Africa and other regions56,57. There are no approved, safer subunit vaccine options available yet58. The Mpox virus has a large genome (>200K) and expresses a complex array of proteins (>190 ORFs, Supplementary Data 4). Since vaccine antigens for the Mpox virus have not been fully identified, the virus is a typical complex pathogen suitable for real-world validation of prediction models.

The application of the proposed model was rapidly carried out on the Mpox virus. First, the 3D structures of all 190 annotated proteins of the Mpox virus (MPXV-USA-2022-MA001 (ID: ON563414.3)) were predicted, and the output Protein Data Bank (PDB) files were obtained. The pre-trained protein language and geometric deep learning models were used to extract the sequence and structural feature vectors of the 190 proteins. The extracted feature vectors were then input into the trained PLGDL model. Upon running the model, we generated a distribution table of antigen probability scores (Fig. 5b and Supplementary Data 5). Subsequent analysis revealed that fewer than 10% of the proteins achieved a score above 0.5.

A top 10 candidate antigens list was generated and is presented in Table 3. Dominating the ranking, experimentally validated protective antigens—B6R (Rank 1, Score = 0.9194), M1R (Rank 2, Score = 0.8841), A35R (Rank 4, Score = 0.8348), H5R (Rank 6, Score = 0.8247), D8L (Rank 8, Score = 0.7162), and A5L (Rank 9, Score = 0.6887)—are corroborated by their documented roles in viral entry, membrane fusion, and immune modulation50,59,60. Among these, three (M1R, B6R, A35R) have already been successfully utilized in the development of mRNA vaccines and achieved preliminary clinical success61,62. This validates the efficacy of our antigen prediction model in real-world intricate viral scenarios.

Notably, the model identified potential candidates such as J3R (Rank 3, Score = 0.8836), a chemokine-binding homolog of vaccinia B29R/C23L implicated in immune evasion; A13L (Rank 5, Score = 0.8343), a virion core protease involved in capsid maturation, mirroring vaccinia A12L’s proteolytic activity, G10R (Rank 7, Score = 0.7711), a fusion complex component critical for viral membrane integration, and A41L (Rank 10, Score = 0.6782), a Chemokine binding protein reducing infiltration of inflammatory cells into the infected area; both of which lack prior vaccine validation despite their conserved functional roles63–66.

Immune evaluation of candidate antigens for Mpox
To verify the accuracy of the model’s predictions for Mpox, we successfully expressed four antigens and performed immune evaluation in mouse models, including the validated vaccine antigen controls M1R (Score = 0.8841), two high-scoring candidates (J3R, Score = 0.8836; G10R, Score = 0.7711), and a low-scoring antigen B9R (Score = 0.4434) (Fig. 5c, d)67. Immunological results indicated that all candidate antigens could elicit robust specific humoral immunity. Antibody levels reached or surpassed those of the positive control antigen M1R (Fig. 6b). Notably, G10R and B9R demonstrated exceptional performance, with antibody levels reaching 18-fold and 37-fold, respectively, compared to M1R after booster immunization. Subclass analysis of the antibodies revealed a balanced Th1 and Th2 immune response, thus indicating the broad immunogenic potential of these antigens.

However, critical divergence emerged in protective efficacy: G10R generated neutralizing antibody levels comparable to M1R in microneutralization assays against ectromelia virus (ECTV), a surrogate for Mpox, and conferred partial protection in lethal challenge models (15-day survival vs. 7-day mortality in controls)50. Crucially, sequence alignment confirmed exceptionally high conservation (>96% identity) between the potential MPXV antigen (G10R) and its ECTV homolog (Supplementary Fig. 3), strongly supporting the biological relevance of the observed immune responses. In stark contrast, B9R, despite its exceptional immunogenicity as a secretory protein, failed to elicit protective effects, validating the model’s capacity to discriminate between immunogenic yet non-protective antigens and true protective candidates67.

Methods
Antigen dataset establishment
Positive dataset construction. The positive dataset was rigorously curated primarily from the Protegen Database, which provides experimentally validated protective antigens meeting one of two criteria: (1) demonstrated in vivo protective efficacy (e.g., statistically significant survival improvement in pathogen challenge models), or (2) induction of antigen-specific immune responses directly associated with protective outcomes (e.g., neutralizing antibodies with confirmed pathogen inhibition)34. Additional protective antigens were identified through PubMed-curated studies that met stringent inclusion criteria: demonstrated in vivo protection. The CD-HIT program (V4.8.1), which clusters proteins at various sequence identity levels, was utilized to eliminate redundant sequences with amino acid homology exceeding 50%71. This process yielded a dataset of 600 unique vaccine antigens comprising 119 viral, 386 bacterial, and 95 eukaryotic antigens.

Negative dataset construction. The negative dataset was constructed by extracting all annotated protein sequences from UniProt-listed pathogens corresponding to those represented in the positive antigen dataset. To ensure rigorous quality control, sequences underwent a two-step homology reduction process. First, intra-dataset redundancy was minimized by applying a 50% sequence identity cutoff using CD-HIT (V4.8.1), consistent with the homology threshold applied to the positive dataset. Subsequently, all sequences exhibiting >20% sequence similarity to any positive antigen were excluded through BLASTP (V2.14.0) alignment (E < 1e-5), effectively eliminating potential cross-dataset biases. This dual-filter approach yielded 6,000 non-redundant negative samples (composition: Viral 481, Bacterial 4493, Eukaryotic 1026; Fig. 2a), intentionally maintaining a 1:10 positive-to-negative ratio to reflect the natural scarcity of protective antigens.

Quality assessment of the structures. The structural prediction of the antigens was performed using AlphaFold3 (release 3.0.1), a state-of-the-art deep learning algorithm for protein structure prediction28. The input sequences for the predictions were derived from both the positive and negative datasets. Each antigen sequence was processed individually to generate its three-dimensional structure. Once the structural predictions were generated, the corresponding PDB files for each protein were obtained. These files contain the atomic coordinates of the proteins’ structures, which are essential for subsequent quality assessments. Structural confidence was evaluated using pLDDT (Predicted Local Distance Difference Test), pTM (Predicted TM Score), and PAE28.

Feature extraction based on protein language models for characterizing protein sequences. The evaluation of three advanced protein language models for sequence feature extraction was conducted: ESM-2, ProTrans, and AMPLIFY23,36,37. ESM-2, pre-trained via masked language modeling on the UniRef50 database, captures intricate sequence patterns and relationships, generating 1280-dimensional contextual embeddings per protein sequence. ProTrans produces 1024-dimensional context-aware embeddings that simultaneously encode local residue interactions and global sequence properties, demonstrating strong performance in protein function prediction and structural inference tasks. AMPLIFY employs a hybrid architecture integrating GNNs and attention mechanisms to model sequence-structure relationships, yielding 960-dimensional embeddings optimized for higher-order feature extraction.

Establishing the neighborhood enhanced graph convolutional network (NEGCN) for characterizing protein structures
Data acquisition and pre-processing. High-quality protein structure data totaling 805,000 entries were obtained from the AlphaFold Protein Structure Database38. This comprehensive dataset provides a solid foundation for developing robust graph-based models to characterize protein structures comprehensively.

Graph construction. We implemented a graph construction approach to represent protein structures spatially. Initially, an amino acid from a given protein (protein A) was selected randomly to serve as the center of a radius (R) (Fig. 1). All amino acids within this radius were included to form an adjacency graph that was then designated as the target sample. Subsequently, another amino acid from protein A was similarly used to generate a corresponding positive sample graph. Conversely, an amino acid from a different protein (protein B) served as the center for constructing a negative sample graph. In these graphs, nodes represent individual amino acids and edges denotes spatial adjacency between pairs of amino acids.

Graph editing via random masking. To introduce variability and test the robustness of our model, we applied a random mask method whereby a predetermined proportion of edges was randomly omitted from the target, positive, and negative sample graphs. This resulted in modified graph structures.

Graph transformation and feature extraction. Our NEGCN model transforms the initial adjacency graph (graph A) into an edge graph (graph B). This transformation encapsulates connectivity through different adjacency relationships as edges, cascades node attributes, and categorizes edge types based on the angles between amino their spatial positions. This enables precise classification of edge types into various categories according to their spatial positions.

Neural network architecture. A four-layer neural network was constructed to process the edge graph by cascading outputs from each layer. In this way, we can generate a comprehensive feature vector representing the 3D structure of the protein.

Node embedding updates. Three distinct message-passing methods were utilized to update each node in the edge graph, as shown in Eq. 1.

hm i = Wθ hm i,1 + hm i,2 + hm i,3
hm i,1 = X θ2T Wτ Xj2Nθ i hm j (2) 1 τ2k ^ Xj2Nτ i (1)
hm i,2 = W hm k j TP,FP,TN,andFNare the number of true positive samples, false positive samples, true negative samples, and false negative samples, respectively. These metrics were chosen to provide a comprehensive overview of the models’ predictive performances by capturing both the effectiveness and reliability of predictions. Using a third-party standard dataset, additional comparisons are made with several benchmark antigen prediction models: Vaxign, VaxiJen, VaxiJen3, Vaxign-ML, Vaxi-DL, and Vaxign-DL.

Cross-validation on pathogen-specific subsets. To empirically evaluate the hypothesis that a model trained on a diverse, combined dataset would achieve superior or comparable performance to models trained on homogeneous, pathogen-specific datasets, a cross-validation analysis was conducted. The full dataset, comprising 600 positive and 6000 negative samples, was partitioned while maintaining the original training-test split ratio. The training data was further subdivided into three mutually exclusive sets based on pathogen origin: “Virus-only” “Bacteria-only” and “Eukaryota-only”. Three separate “specialist” XGBoost models were trained, one on each of these pathogen-specific training sets. For evaluation, each specialist model was assessed only on the test set corresponding to its pathogen type (e.g., the Virus-only model was tested on the Virus test set). The original “generalist” PLGDL model, trained on the combined “Virus + Bacteria + Eukaryota” training set, was evaluated on the same pathogen-specific test sets to provide a direct performance comparison across the same seven metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC, PR-AUC, and MCC.

Embedding analysis. In this study, we used a deep clustering technique for embedding analysis47. Initially, embeddings were extracted from the pre-trained model for all samples. These embeddings were then refined using contrastive learning, which enhances the separability of similar and dissimilar samples in the latent space. The contrastive learning framework optimizes the embedding space by bringing similar samples closer while pushing dissimilar samples apart through a contrastive loss function. Subsequently, a neighbor embedding method was used, specifically t-distributed stochastic neighbor embedding or Uniform Manifold Approximation and Projection, to reduce the high-dimensional embeddings to a two-dimensional space. This step facilitates unsupervised visualization, allowing us to observe the clustering patterns in the embedding space. The resulting 2D visualization effectively highlights the clustering phenomenon, providing insights into the model’s ability to distinguish between different sample categories. Subsequently, we use the metric of cluster purity to quantify the quality of the clusters, i.e., quantifying how homogeneous each cluster is with respect to the ground truth labels.

Prediction of candidate antigens for Mpox
Antigen prediction. The structures of all 190 proteins of the Mpox virus epidemic strain (MPXV USA 2022 MA001) are predicted, and the output PDB files are obtained. The sequence and structural feature vectors are extracted using the pre-trained protein language and geometric deep learning models, and then input into the previously established PLGDL model. Note that the classifier model was not trained with known Mpox antigens to avoid overfitting. The output is a list of the top candidate vaccine antigens for Mpox.

Antigen expression. The selected candidate antigens of Mpox were prepared using the eukaryotic expression system. Briefly, the amino acid sequence of the target antigens was selected, and then the tissue plasminogen activator signal peptide and Hexa-His tag were added to its N-terminus. Frequently used mammalian cell codons were used to optimize expression. The gene sequence was inserted into the pcDNA3.1 eukaryotic expression vector (Invitrogen, Cat V79520, USA). We used the Expi293F mammalian cell expression system to express the protein. Next, 72 h after transfection, the cell culture medium was centrifuged (3000 × g, 15 min), and the retained supernatant was filtered with a 0.45 µm syringe filter to remove cell debris. The His-tagged recombinant mature proteins were then purified using a 5-ml HisTrap HP (GE Healthcare, Cat 17524801, Sweden) chromatography column according to the manufacturer’s instructions. The purity of the protein antigens was further evaluated using SDS-PAGE.

Immunity evaluation of candidate vaccine antigens for Mpox
Immunization assessment process. The experiments involving animals were approved and carried out according to the Institutional Animal Care and Use Committee guidelines of the Laboratory Animal Center (IACUC-DWZX-2024-P008). Specific pathogen-free female BALB/c mice (6–8 weeks) were purchased from Charles River and were used for intramuscular immunization. Mice were housed in individually ventilated cages. Housing conditions were maintained at a controlled temperature (22 ± 2°C), humidity (55 ± 10%), and a 12-h light/dark cycle, with ad libitum access to food and water. Each mouse was injected with 100 µL of vaccine sample, including 5 µg protein antigen, 50 µg alum adjuvant (AH, Brenntag Biosector, Denmark) with 25 µg CpG (Takara, Japan), or PB solution (Sham group). The mice were immunized at day 0 and day 14 and bled from the tail vein at day 0, day 14, day 28, and day 56. The blood was centrifuged at room temperature to obtain serum for subsequent immunological evaluation.

Binding IgG antibody. Mouse serum antibody (IgG) levels were detected by enzyme-linked immunosorbent assay. First, the antigen protein was diluted to 2 µg/ml in a carbonate solution. Then, the 96-well plate (Solarbio, China) was coated with 100 µL per well and incubated overnight at 4°C. PBST was used to wash the 96-well plate three times; the liquid inside the plate was discarded, 100 µL blocking solution (2% BSA dissolved in PBST) was added per well, and the plate was incubated at 37°C for 1 h. The cleaning step was repeated, and a 100 µL dilution (0.2% BSA dissolved in PBST) with gradient diluted serum was added to the plate, which was incubated at 37°C for 1 h and washed three times. Next, HRP-conjugated goat anti-mouse IgG (Abcam, Cat ab97265, UK, 1:10,000 dilution) was added to the plates, which were incubated at 37°C for 1 h and washed with PBST three times. Then, 100 µL of TMB substrate solution (Solarbio, China) was added to each well, and the plates were incubated in the dark for 6 min. Finally, 50 µL of stop solution (Solarbio, China) was added to end the color reaction. Measurements were made at 450 nm/630 nm (SPECTRA MAX 190, Molecular Device, USA). The endpoint titers were calculated as the dilution that exceeded the 2.1-fold value of the background.

Ectromelia virus neutralization. The assessment of serum neutralizing activity against live ECTV was conducted through a microneutralization assay72. Serial dilutions of serum were incubated with tissue culture infective dose 50% of ECTV at 37°C for 1 h. Following incubation, the serum-virus complexes were introduced to pre-plated BS-C-1 cells (ATCC, Cat CCL-26) monolayers cultivated in 96-well plates and subjected to a further 48 h of incubation. After that, cells treated with ECTV were stained with 0.05% crystal violet for 40 min. Optical density was measured at 570 nm/630 nm after adding the decolorization solution.

Ectromelia virus challenge. On day 90, mice were challenged with a lethal dose of ECTV72. The viral stock was propagated and titrated on BS-C-1 cells using plaque assay to ensure accurate quantification of the viral load. Each mouse received an intranasal inoculation with a 25 µL suspension containing 200 plaque-forming units of ECTV. Following inoculation, the mice were monitored daily for a period of 15 days for clinical signs of infection. The primary outcome measure was the percentage of survival, calculated at the end of the observation period. This survival data was used to evaluate the protective efficacy of the vaccine antigens against the lethal viral challenge.

Statistical analysis. Data analysis was performed using Excel and GraphPad Prism 9.0. Antibody titer data were log10-transformed and analyzed as independent measurements from distinct biological replicates (n = 6 mice per group). One-way analysis of variance followed by Tukey’s post hoc test (for multiple group comparisons) and unpaired two-tailed t-test (for pairwise comparisons) were used to analyze differences between groups. Data are presented as mean ± SEM (error bars in figures).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, the authors developed a machine learning framework, PLGDL, for antigen prediction, utilizing Protein Language and Geometric Deep Learning models. This framework incorporates both primary sequence features and three-dimensional structural features of protein antigens, reducing biases associated with manually curated features. The integrated model demonstrates robustness across proprietary and public datasets and is applicable to viruses, bacteria, and eukaryotic pathogens. As part of their work, they conducted a case study, identifying and experimentally validating a novel antigen, G10R. While the proposed approach is promising, several methodological concerns need to be addressed:\n1. The authors used databases such as the Immune Epitope Database, the Antigen Database, and the Protegen Database to extract positive samples, removing sequences with over 80% homology, resulting in 1,000 vaccine antigen samples. Given the high sequence homology, this threshold should be reduced to 50% to minimize redundancy. For negative samples, 20% similarity was maintained, introducing a bias that likely inflated the model's accuracy. To address this, the authors should include all positive and negative samples, reduce homology to below 50%, and train the model with an expanded pool of negative samples rather than limiting them to match the positive sample count.\n2. The authors extracted features using ESM-2 and reduced them to 40 dimensions using PCA, which is an unsatisfactory approach. A systematic method for feature selection, such as sequential forward search, should be employed to identify the optimal feature set instead of relying on PCA alone.\n3. Multiple feature descriptors were utilized, but their individual contributions to the PLGDL framework were not assessed. An ablation study is essential to quantify the importance of each feature. This analysis would help exclude redundant features and refine the framework by testing the impact of the optimal feature set.\n4. While ESM-2 was used for feature extraction, the study did not explore other recent embeddings, such as ProTrans and similar advanced approaches. A systematic evaluation of multiple embeddings is critical in machine learning research, particularly for publication in high-quality journals. This omission reflects a lack of rigor in model-building methodology.\n5. The dataset's positive samples shared less than 80% homology, while negative samples exhibited less than 20% similarity. This led to a consistent fold structure in positive samples and significant differences in the 3D folds of negative samples, introducing bias into the PLGDL framework. This bias should be mitigated by ensuring a more balanced and representative dataset to improve the framework's generalizability.", "reviewer_2": "The ability of this model to predict antigens is noteworthy especially since it appears to be able to do this is a very direct fashion, moving from sequence to antigen identification as the prediction task itself versus most alternative methods that require multiple intermediate steps of antigen scoring and ranking in order to do this. It is also worth noting that being able to identify antigens from broad corpora such as organism proteomes is essentially the core task of vaccine design and so advancements here are important. The work does demonstrate how this model compares to alternative predictive approaches which is an important aspect.\nThis methodology described here is sound but the overall number of validation antigens is small. The authors do acknowledge this too.\nImportantly it does appear that this work could be reproduced as the requisite code is accessible via a link from the manuscript and they also appear to have included key data elements such as their antigen tables.\nThe code is well-organized with a README as well as an organized repository across data, evaluation, sequence, and structure categories.", "reviewer_3": "Zai and coauthors develop a ML architecture that takes a sequence of a protein and predicts it if is an “antigen” or not, with the aim to help prioritizing protein vaccine antigen candidates. They use three separate databases that register proteins that are immunogenic and/or that induce protective immunity when used in vaccines (without specification of the host organism) and formulate a binary classification problem: “antigen” or “not antigen”. They obtain around 1000 positives, and the authors then take any protein of known genomes as negative but only keep 1000 for balancing the problem (which is a bit sad). They separate subproblems by groups of bacteria, virus and eukaryotes. An interesting step of the ML architecture is the combined usage of input sequence and structure (predicted by alphafold) as embedding, and the pre-training of the structural part of the ML on the alphafold protein structure database with extremely large numbers of protein structures, to create a latent space which would not have been possible with the 2x1000 proteins in the main dataset of interest. After validation on left out and independent datasets, the authors apply their method to the genome of MPOX virus that contains ~190 ORFs as potential proteins and propose a prioritization of antigens to be used (irrespective whether they are surface or intracellular antigens). They pick one antigen and perform immunizazions in mice. They perform either a micro-neutralization assay in vitro of the serum with live viruses, or infect the mice with a related virus of MPOX and the mice all die like in PBS but one-two days later as the non-vaccined mice, which may show a slight delay effect but that could be nonspecific to innate immune activation rather than real protection.\nGenerally speaking, the biological problem is not clearly stated nor really well thought through the study, which is a shame because it makes it impossible to evaluate the quality of the biological predictions. Being an Antigen or not is not defined. The authors mix proteins that are immunogenic (develop an immune response, in IEDB) and those that confer a protective immunity (protegen database), and mix different host species. The selection of the antigens themselves is not really described. The problem is ill-defined because:\n- An antigen that is immunogenic in rats may not be immunogenic in human or may be even a human antigen that was used in rats.\n- Many antigens do not confer protection but are relevant for vaccine design. For instance, so far, HIV vaccine antigens have failed to give protective immunity due to virus escape, is HIV considered or not in this study?. I am pretty sure that yes. So then the authors need to explain if they work on protective or immunogenic antigens.\n- The same protein can be cut into domains, for instance influenza HA has domains poorly recognized by the immune system because of glycans. Not all domains are immunogenic and/or protective. If one would include sometimes the full protein and sometimes its domains, the same protein would appear in both positive and negative datasets within their problem formulation, which shows that the definition of positives and negatives is not biologically clear.\n- In theory, most protein antigens should be immunogenic, unless they are related to self maybe, but the domains of the proteins will have high differences.\n- How does it help if a pathogen intracellular protein is immunogenic and leads to a strong immune response, since in vivo the antibodies are likely to never bind their target that is intracellular? So I don’t see how using the IEDB immunogenic proteins helps predicting antigens that will induce protective immunity.\nAs an example, a clean definition of the problem could have been to restrict to hotspots / binding domains / epitopes that are known in antigens, then by cutting protein patches for instance as positive or negative, and the problem would then be “can one predict targetable epitopes of 3D proteins by the human immune system”. This would look more similar to an epitope prediction problem but the application proposed by the authors would be more cleanly defined.\nIndeed, I agree with the authors that a relevant problem: can we identify proteins that are sequence/structurally similar to proteins to proteins known to confer protection? But, unless the authors prove me wrong, I believe that the current study may learn surrogate properties of proteins that are extracellular and were chosen by humans to be tested in vaccines. I don’t expect many intracellular proteins have been tested. So at the end of the day I am really not sure which properties the ML model is learning.\nSome technical points:\n- The authors should describe the dataset (distribution of antigen species) and data leakage between train and test: is there influenza sequences in both train and test,\n- I didn’t understand if/how authors mitigate the risk to suggest self-similar antigens to humans. You don’t want to end-up with EBV sequences that are related to myelin…\n- I don’t see the reason for down-sampling the negative dataset to only 1000 proteins while hundred of thousands may be available. Oversampling of the positives would be a better way to balance the dataset while keeping diversity of negatives.\nRegarding the experimental setup,\nI understand that the mice can not be challenged by the real MPOX virus, and therefore testing reactivity to another virus is fine, and not seeing a strong phenotype might be due to the use of a different virus. The serum neutralization assay is fair. But the neutralization assay is never a proof of protection. I would question the general conclusion that the model predicts a “good antigen”. It could also be that any surface antigen of MPOX would equally induce an immune response and give the same experimental result irrespective of what the model predicted. I think a key result that would strengthen the model use is: taking an expressed surface antigen of MPOX that is predicted to be the least immunogenic and showing that the mice are not responsive to this antigen."}, "author_response": "We sincerely thank the reviewers for their time, expertise, and constructive critiques, which have profoundly improved the rigor and clarity of our work. In response to your insightful comments, we have conducted extensive computational experiments, refined methodologies, and revised the manuscript text to address each concern. Key updates include optimizing dataset composition (adjusting protective antigen criteria, sample ratios, and homology thresholds), reprocessing all antigen structures using AlphaFold3 to generate high-confidence 3D features, systematically evaluating feature selection and extraction approaches, and re-analyzed experimental validation with newly prioritized Mpox antigens. These revisions are supported by new ablation studies, predictive performance analyses, and a dedicated discussion of model limitations and future directions. All changes have been integrated into the text to enhance biological relevance, reduce bias, and strengthen translational implications. We deeply appreciate your guidance and welcome any further feedback.\nPoint-by-point response to the reviewers’ comments are as follows:\nReviewer #1: ... [Response text omitted for brevity as per instructions to include verbatim text]"}, {"round": 2, "reviewer_comments": {"reviewer_3": "The authors have globally taken my comments into account and created a more precisely defined positive dataset and defined their target as protective antigens (with in vitro neutralization). So the problem is a bit better defined as before, and jn view of the new data provided by the authors sequences from mammals are not included in the positives which sounds good to avoid cross-reactivity. I understand that the authors have performed new in vivo experiments showing that results from the model correlate with in vivo efficacy of the antigen, which is well appreciated.\nAltogether, I think the problem formulation is still not 100% well defined biologically speaking but I acknowledge that the authors can not do much better in view of the available poor quality databases and I don't know better ones. I think the global approach of the manuscript is creative and meaningful, and the computational background is flawless. I therefore support publication.", "reviewer_5": "The authors have addressed all the comments raised by Reviewer 1, and there are no further outstanding issues.", "reviewer_6": "The authors' answers to the reviewer comments are complete and exhaustive. They have made numerous changes to improve the study and the manuscript. However, the weakness of the study is evident in the authors' response to Reviewer 2's second comment, which weakness is also noted by Reviewer 1. The reliability of the derived models is undermined by the collection of the training and test sets and the validation of the results. The data collected from the Protegen database contain protective antigens proven through in vivo animal studies on viral, bacterial, and parasitic pathogens. The immune system's reaction to viral, bacterial, and parasitic infections follows different mechanisms. Combining antigens with such differences in immune mechanisms in training and test sets is inappropriate. For that reason, some existing tools that the authors compare their model against use separate models for pathogens with distinct origins. Another issue is that all the antigens in the sets are protective in animals, but are not proven to be protective in humans. Using such sets to develop and evaluate models for predicting human protective antigens is problematic because of the differences between human and animal immune systems. Protective antigens proven in animals can be used to create predictive models, but for applicability to humans, the models must be carefully validated—and ideally fine-tuned—with human-derived data; otherwise, their predictions could be misleading. Hence, the comparisons with other tools and methods listed in Table 2 for predicting protective antigens are unreliable.\nBased on the aforementioned issues, I do not recommend the manuscript for publication."}, "author_response": "We thank the reviewers for their time and insightful comments. We have carefully addressed each point below and believe the resulting revisions have significantly improved the manuscript. Additionally, as per request, we have prepared and submitted a comprehensive “Source Data” Excel file containing all underlying numerical data for the figures in the main manuscript and supplementary information, ensuring full transparency and reproducibility of our findings. Furthermore, we have taken the initiative to redraw all figures in the manuscript, enhancing their clarity and visual appeal while maintaining scientific accuracy. All changes in the revised manuscript are highlighted using the track changes feature for clarity. ... [Response text omitted for brevity]"}, {"round": 3, "reviewer_comments": {"reviewer_3": "I have no more requests.", "reviewer_6": "The revision is thorough and well-reasoned. The new analyses convincingly address previous concerns about dataset composition and model validation, and the discussion now provides a clear methodological rationale. Before final acceptance, I recommend a few minor editorial adjustments:\n(1) clarify in the Abstract that PLGDL is intended as a screening/prioritization tool, not a direct predictor of human efficacy;\n(2) Reiterate this scope and limitation in the Discussion.\nWith these small refinements, the manuscript will be ready for publication."}, "author_response": "We extend our sincere gratitude to the reviewers for their time and insightful feedback throughout the review process, which has improved the clarity and impact of this manuscript. ... [Response text omitted for brevity]"}]

Decision letter