Provir/Latitude 45 study: A step towards a multi-epitopic CTL vaccine designed on archived HIV-1 DNA and according to dominant HLA I alleles
Bibliographic record
Abstract
One of the approaches by which the scientific community is seeking to cure HIV is the use of therapeutic vaccination. Previous studies have highlighted the importance of the virus-specific CD8+ T cell cytotoxic responses for the immune control of HIV and have oriented research on vaccine constructs based on CTL epitopes from circulating HIV-1 strains. The clinical trials with therapeutic vaccines to date have had limited success likely due to (i) a discrepancy between archived CTL epitopes in the viral reservoir and those in circulating viruses before antiretroviral therapy (ART) initiation and (ii) the lack of strong affinity between the selected CTL epitopes and the HLA grooves for presentation to CD8+ cells. To overcome these limitations, we launched the Provir/Latitude 45 study to identify conserved CTL epitopes in archived HIV-1 DNA according to the HLA class I alleles of aviremic patients, most of whom are under ART. The near full-length genomes or Gag, Pol and Nef regions of proviral DNA were sequenced by Sanger and/or Next Generation Sequencing (NGS). The HLA-A and B alleles were defined by NGS or molecular analysis. The TuTuGenetics software, which moves a sliding window of 8 to 10 amino acids through the amino acid alignment, was combined with the Immune Epitope Data Base (IEDB) to automatically calculate the theoretical binding affinity of identified epitopes to the HLA alleles for each individual. We identified 15 conserved epitopes in Pol (11), Gag (3), and Nef (1) according to their potential presentation by the dominant HLA-A and B alleles and now propose to use the corresponding conserved peptides in a multi-epitopic vaccine (HLA-fitted VAC, HFVAC).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".