Systematic Analysis of Host Immunological Pressure on the Envelope Gene of Human Immunodeficiency Virus Type 1 by an Immunobioinformatics Approach
Bibliographic record
Abstract
As the number of HIV-1 sequences has increased in the public database and new tools of immunological bioinformatics have become available, making it possible to better understand at a population level how host immune response drives the evolution of HIV-1 envelope (Env). We analyzed 1100 unique full-length envelope sequences and systematically determined positive selection (PS) sites by QUASI analysis and found that PS sites were widely dispersed across Env. The frequency of Env PS sites appears to be relatively stable over time. Moreover, between 25% and 61% of PS sites are shared between subtypes A, B, C, and D, suggesting that host immune responses target the same regions of Env gene across different clades at the population level. Significant correlations were observed between PS sites and Neutralizing antibody (NAb) response, as well as PS sites and Th epitopes. Furthermore, NAb sites in combination with cytotoxic-T lymphocyte (CTL) epitopes and proteasome cleavage sites were also significantly associated with PS sites, suggesting NAb may be the major force driving the evolution of HIV-1 Env. We also identified regions that are free from PS, but heavily targeted by CTL or NAb, implying that functional constraints may be responsible for the lack of positive selection in these regions. These findings should help researchers to identify epitopes or regions of HIV-1 that may aid in designing vaccines.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".