A Globally Representative Immunopeptidomics Approach to Identify Population-Wide Vaccine Candidates
Bibliographic record
Abstract
Abstract Vaccine development has historically relied on preclinical testing using one or two cell lines, typically of Caucasian descent, contributing to vaccines that may fail to provide adequate protection across genetically diverse populations. A major factor in these failures is the lack of consideration for human leukocyte antigen (HLA) diversity, with over 30,000 HLA alleles worldwide exhibiting distinct frequencies across ethnic groups and geographic regions. Here, we present a globally representative immunopeptidomics approach that addresses HLA diversity at the earliest stages of vaccine antigen discovery. We established a panel of 30 cell lines from the 1000 Genomes Project that represent 75% of the world’s most common HLA alleles. Using a combined computational and experimental approach, we identified 18 common binding motifs (7 MHC class I and 11 MHC class II) shared across 24 cell lines from the panel. We developed an accessible bioinformatic tool that predicts globally presentable antigenic regions from any pathogen proteome by identifying peptides matching these common binding motifs. Validation with Salmonella enterica serovar Typhimurium and SARS-CoV-2 Spike protein demonstrated that our method successfully identifies known immunogenic regions, with experimentally detected epitopes showing substantial overlap with predicted regions of interest. Notably, only three cell lines were required to validate highly immunogenic S. enterica targets, including OmpC—a porin with known 100% protective efficacy—detected across all tested lines. Our approach enables researchers to rapidly screen pathogen proteomes using freely available bioinformatic tools, with optional experimental validation requiring minimal resources. By identifying antigens with broad population coverage before clinical trials begin, this method has the potential to increase vaccine success rates while ensuring equitable protection across diverse populations. The cell line panel, common binding motifs, and bioinformatic workflow are publicly available, providing an accessible pathway for developing vaccines that truly serve global populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".