Aminopeptidase Substrate Preference Affects HIV Epitope Presentation and Predicts Immune Escape Patterns in HIV-Infected Individuals
Bibliographic record
Abstract
Viruses evade immune detection partly through immune-associated mutations. Analyses of HIV sequences derived from infected individuals have identified numerous examples of HLA-associated mutations within or adjacent to T cell epitopes, but the potential impact of most mutations on epitope production and presentation remains unclear. The multistep breakdown of proteins into epitopes includes trimming of N-extended peptides into epitopes by aminopeptidases before loading onto MHC class I molecules. Definition of sequence signatures that modulate epitope production would lead to a better understanding of factors driving viral evolution and immune escape at the population level. In this study, we identified cytosolic aminopeptidases cleavage preferences in primary cells and its impact on HIV Ag degradation into epitopes in primary human cell extracts by mass spectrometry and on epitope presentation to CTL. We observed a hierarchy of preferred amino acid cleavage by cytosolic aminopeptidases. We demonstrated that flanking mutations producing more or less cleavable motifs can increase or decrease epitope production and presentation by up to 14-fold. We found that the efficiency of epitope production correlates with cleavability of flanking residues. These in vitro findings were supported by in vivo population-level analyses of clinically derived viral sequences from 1134 antiretroviral-naive HIV-infected individuals: HLA-associated mutations immune pressures drove the selection of residues that are less cleavable by aminopeptidases predominantly at N-flanking sites, leading to reduced epitope production and immune recognition. These results underscore an important and widespread role of Ag processing mutations in HIV immune escape and identify molecular mechanisms underlying impaired epitope presentation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".