SARS-CoV-2 Epitope Presentation by Class II HLA Genotypes Common in North American Populations: A Proposed Computational Approach for Vaccine Efficacy Evaluation
Bibliographic record
Abstract
Background: Human Leukocyte Antigen (HLA) gene polymorphisms between ethnic groups have been shown to play a role in the heterogeneity of response to SARS-CoV-2, in terms of COVID-19 disease severity and susceptibility, in addition to socioeconomic factors. It was predicted that this finding may extend to vaccine responsiveness. Purpose: To the best of our knowledge, this study was the first that aimed to predict and evaluate the effectiveness of four COVID-19 vaccines across North American ethnic groups, in terms of their ability to trigger CD4+ T cell help, based on class II HLA allele frequencies. Methods: Various databases including the Immune Epitope Database (IEDB) were used in this computational approach. The number of peptide-HLA high-affinity pairs between the most common HLA II haplotypes and SARS-CoV-2 peptides in various vaccine types were retrieved and compared between ethnicities. From this, the efficiency of antigen presentation to CD4+ T cells was evaluated, a crucial component in the context of vaccination for cellular immunity and support in antibody generation. Results: Multiple discrepancies in vaccine effectiveness for ethnic minorities relative to the Caucasian group, overrepresented in vaccine clinical trials, were highlighted. Recommendations were issued in terms of which vaccine types could be most effective for particular ethnicities. Conclusion: There exists a genetic basis for differential responses to vaccines among ethnic groups in North America. However, given the multifactorial nature of vaccine responsiveness and limitations of computational methods, this study offers future research directions to undertake before the findings can be transferred to clinical and public health settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".