Antigen design and selection are foundational steps in vaccine development, determining the success of a vaccine in eliciting a robust and targeted immune response. This process involves intricate scientific methodologies, innovative engineering techniques, and increasingly, the integration of artificial intelligence (AI) to identify and optimize antigens that can effectively train the immune system to combat pathogens. This blog delves into the detailed stages of antigen design and selection, highlighting how AI is revolutionizing vaccine development.

      The journey of antigen design begins with comprehensive profiling of the pathogen. This step involves sequencing the genome of the pathogen to identify potential antigenic targets, such as surface proteins, enzymes, or toxins. Proteomic studies are then conducted to map the proteome and detect immunodominant proteins expressed during infection. Additionally, virulence factor identification is performed to pinpoint components critical for pathogen survival and virulence, which are ideal targets for vaccines. AI-driven bioinformatics tools play a significant role in this stage by leveraging machine learning models to predict antigenic regions based on pathogen structure and function. These tools improve the accuracy and speed of target identification compared to traditional methods.

      Once potential antigens are identified, they are evaluated for their ability to stimulate an immune response. This process includes epitope prediction using algorithms to identify B-cell and T-cell epitopes within antigenic proteins. AI-powered platforms like NetMHC and IEDB are employed to predict major histocompatibility complex (MHC) binding affinity with greater precision. Conservation analysis is carried out to ensure that the selected antigens are conserved across multiple strains of the pathogen to provide broad protection. Furthermore, AI models are utilized to screen for potential cross-reactivity with host proteins, minimizing the risk of autoimmunity. By incorporating large datasets, these models enhance the predictive accuracy of immunogenic targets.

NetMHC: Enhancing MHC Binding Predictions

NetMHC is a cutting-edge tool specifically designed to predict the binding affinity between peptides and major histocompatibility complex (MHC) molecules, a critical process in antigen presentation to T-cells. The platform employs advanced neural network algorithms to determine which peptides are most likely to bind to MHC class I and class II molecules, a crucial step in identifying epitopes capable of eliciting an adaptive immune response. NetMHC’s accuracy lies in its ability to analyze large datasets of peptide sequences, scoring each peptide based on its likelihood of forming stable complexes with specific MHC alleles.

Its updated version, NetMHCpan, extends its applicability to a broader range of MHC alleles, including those found in genetically diverse populations, thus making it a globally relevant tool for vaccine development. This expanded reach is especially significant for designing vaccines targeting infectious diseases prevalent in regions with high genetic variability.

How to Use NetMHC for Epitope Prediction

Step 1: Access the NetMHC Platform

Start by navigating to the NetMHC web server. Depending on your research needs, choose the appropriate version of the tool (e.g., NetMHCpan for analyzing diverse MHC alleles).

Step 2: Prepare Your Input Data

Protein Sequences: Collect the protein or peptide sequences you want to analyze, typically derived from pathogen genomes or proteomes.

Format: Ensure your sequences are saved in FASTA format, the standard input for NetMHC.

Identify Relevant MHC Alleles: Select specific MHC molecules based on your target population (e.g., HLA-A*02:01 for human studies).

Step 3: Input Your Data

Paste your protein sequences directly into the input box on the NetMHC server or upload a FASTA file.

Choose the relevant MHC alleles from the dropdown menu or analyze all available alleles for broader insights.

Define the peptide length, commonly ranging from 8–11 amino acids for MHC class I predictions.

Step 4: Run the Analysis

Click the “Submit” button to start the prediction process. The platform will analyze your data, identifying peptides with strong binding affinities. Processing times may vary depending on the size of your dataset and the selected alleles.

Step 5: Review the Results

The output is presented in a user-friendly table format that includes:

Predicted Peptides: The amino acid sequences analyzed.

Binding Affinity Scores: Lower scores (in nanomolars, nM) indicate stronger peptide-MHC binding.

Percentile Ranks: Lower ranks (e.g., below 2%) suggest high-affinity binders.

Step 6: Refine Your Findings

Filter peptides based on binding affinity thresholds (e.g., <500 nM) and their ability to elicit an immune response.

Use conservation analysis to ensure selected epitopes are effective across multiple pathogen strains.

Step 7: Export and Integrate Results

Export the results in a format compatible with other platforms for further analysis. Consider integrating the data with tools like AlphaFold for 3D structural modeling or IEDB for cross-validation.

IEDB: A Comprehensive Resource for Epitope Data

The Immune Epitope Database (IEDB) is a versatile platform that provides an extensive repository of epitope data and advanced computational tools for epitope analysis. IEDB hosts a wealth of experimentally validated epitopes derived from infectious diseases, autoimmune conditions, and allergens, along with computational predictions for new epitopes. Its comprehensive nature makes it indispensable for researchers seeking to identify, analyze, and validate immunogenic epitopes. The platform’s suite of tools includes predictors for T-cell and B-cell epitopes, MHC binding affinity analyzers, and tools to evaluate population coverage of selected epitopes. By incorporating machine learning algorithms, IEDB enhances the accuracy of these predictions, helping researchers focus on epitopes with the highest potential for triggering robust immune responses.

One of IEDB’s key contributions is its ability to integrate experimental data with computational outputs, providing a balanced approach to epitope design. Additionally, IEDB’s population coverage analysis enables the design of vaccines tailored to specific demographic groups, addressing the challenge of genetic diversity. Despite its strengths, IEDB faces limitations such as biases in its data sources, which can affect predictions for underrepresented pathogens.

Fig.1 The Immune Epitope Database and Analysis Resource 

How to Use IEDB for Epitope Prediction

Step 1: Access the IEDB Website

Visit the IEDB homepage at IEDB.
IEDB provides various tools and resources for different tasks, including epitope prediction, epitope analysis, and data browsing. Choose the appropriate section based on your needs.

Step 2: Select the Right Tool for Your Analysis

IEDB offers several prediction tools that can be used for epitope analysis. The tools are designed for different immune system components (e.g., B-cell epitopes, T-cell epitopes), and for predicting MHC class I and MHC class II interactions.

T-cell Epitope Prediction: Choose this tool if you are interested in predicting peptides that will bind to MHC class I or class II molecules and activate T-cell responses.

B-cell Epitope Prediction: Choose this for identifying peptide sequences that will be recognized by B-cells, leading to antibody production.

MHC Binding Predictions: If you\’re working with specific MHC molecules, select this option to predict how peptides will bind to MHC class I or class II molecules.

Step 3: Prepare Your Input Data

Peptide Sequence:
For MHC prediction, you will need the amino acid sequence of the protein or peptide that you want to analyze. You can obtain this sequence from FASTA format files.
For B-cell epitopes, the sequence can also be in FASTA format or just individual peptide sequences.

MHC Alleles:
For T-cell prediction, you must select the relevant MHC alleles (e.g., HLA-A, HLA-B, or HLA-DR for human studies).
Select alleles that are most relevant to your research population or species.

Step 4: Input Data into IEDB

Paste or Upload Sequence:
Paste your sequence into the provided text box or upload a FASTA file containing the peptide sequence(s).

Select MHC Alleles:
Choose the MHC allele(s) of interest. You can either pick specific alleles for a focused study or select a wider range of alleles for a more comprehensive analysis.

Set Parameters:
You can adjust the peptide length and other relevant settings. For example, MHC class I peptides are typically 8-11 amino acids long, while MHC class II peptides can be 12-25 amino acids long.

Step 5: Run the Analysis

Once you’ve input all the necessary information, click “Submit” to run the prediction.

Step 6: Review the Results

Predicted Epitope List:
The output will provide a list of peptides along with their predicted binding affinities (typically in nanomolar values). Lower values indicate stronger binding.

Percentile Ranks:
The tool will also display percentile ranks to help you assess the relative strength of binding. Peptides with low percentile ranks (e.g., <1%) are likely to bind strongly to the MHC molecule.

Cross-species Information:
If you’ve selected a broad range of alleles, you may also receive cross-species binding information, which can be helpful for research on different host species.

Step 7: Export and Analyze the Data

Export Results:
You can export the results as a CSV or Excel file for further analysis or integration with other tools.

Further Analysis:
Combine with other platforms like NetMHC for additional epitope screening or AlphaFold for structural modeling of the predicted epitopes.

   Epitope engineering optimizes antigens to enhance their immunogenicity and stability. AI-based structure prediction tools, such as AlphaFold, are transforming structure-based design by providing detailed 3D models of antigen structures. This allows researchers to model and modify antigen structures for improved immune recognition. Designing antigens with multiple epitopes, known as multivalent epitopes, helps elicit a broader immune response. AI also assists in selecting and designing fusion proteins that combine epitopes with carrier proteins or adjuvants to boost immune activation. Synthetic peptides, which mimic critical epitopes, are synthesized with the help of AI-driven synthesis optimization for targeted immune responses.

Antigen candidates undergo rigorous testing to confirm their ability to induce an immune response. AI is increasingly integrated into this stage, analyzing data from in vitro assays that assess antigen-antibody interactions and T-cell activation through techniques such as ELISA and flow cytometry. In preclinical studies, AI models are used to predict immune responses in animal models, enhancing the efficiency of in vivo evaluations. Humanized models, including transgenic animals or organoids, benefit from AI algorithms that predict human immune responses more accurately, reducing reliance on trial-and-error approaches.

Adjuvants are often incorporated to enhance the immunogenicity of the antigen. This involves selecting compatible adjuvants that complement the antigen’s mode of action, such as alum, MF59, or AS01. AI models are applied to design delivery systems, such as nanoparticles, liposomes, or viral vectors, that improve antigen stability and delivery.

References:

Dhanda, S. K., Mahajan, S., Paul, S., Yan, Z., Kim, H., Jespersen, M. C., Jurtz, V., Andreatta, M., Greenbaum, J. A., Marcatili, P., Sette, A., Nielsen, M., & Peters, B. (2019). IEDB-AR: immune epitope database—analysis resource in 2019. Nucleic Acids Research, 47(W1), W502–W506. https://doi.org/10.1093/nar/gkz452 

Dormitzer, P. R., Ulmer, J. B., & Rappuoli, R. (2008). Structure-based antigen design: a strategy for next generation vaccines. Trends in Biotechnology, 26(12), 659–667. https://doi.org/10.1016/j.tibtech.2008.08.002

Graham, B. S., Gilman, M. S., & McLellan, J. S. (2019). Structure-Based vaccine antigen design. Annual Review of Medicine, 70(1), 91–104. https://doi.org/10.1146/annurev-med-121217-094234

Lundegaard, C., Lamberth, K., Harndahl, M., Buus, S., Lund, O., & Nielsen, M. (2008). NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Research, 36(suppl_2), W509–W512. https://doi.org/10.1093/nar/gkn202

Olawade, D. B., Teke, J., Fapohunda, O., Weerasinghe, K., Usman, S. O., Ige, A. O., & David-Olawade, A. C. (2024). Leveraging artificial intelligence in vaccine development: A narrative review. Journal of Microbiological Methods, 224, 106998. https://doi.org/10.1016/j.mimet.2024.106998

Yu, M., Zhu, Y., Li, Y., Chen, Z., Li, Z., Wang, J., Li, Z., Zhang, F., & Ding, J. (2022). Design of a recombinant multivalent epitope vaccine based on SARS-COV-2 and its variants in immunoinformatics approaches. Frontiers in Immunology, 13. https://doi.org/10.3389/fimmu.2022.884433