Glycosylation of HIV-1 gp120 V3 Loop: Towards the Rational Design of a Synthetic Carbohydrate Vaccine
Bibliographic record
Abstract
A wide variety of proteins can bind high-mannose oligosaccharides and are broadly neutralizing against HIV-1. However, success in eliciting broadly neutralizing antibodies against HIV-1 has been limited to date. The rational design of an HIV-1 vaccine is based on the information gained through the structural analysis of antibodies complexed with their epitopes. Of particular interest to this review are the binding of mannosides to human monoclonal antibody 2G12 recognizing Man(9)GlcNAc(2) from HIV-1 gp120. It is widely recognized that T-cell-independent antigens carbohydrates are poorly immunogenic, and fail to induce memory. To increase the immunogenicity, carbohydrate antigens have to be coupled to a highly immunogenic carrier. The design of peptide carbohydrate mimotopes (mimetics of carbohydrate antigens) is one approach that is currently explored to elicit neutralizing antibodies. This work is concerned with existing structural data on Man(9)GlcNAc(2) as the most promising epitope (or glycotope). Structural analysis of various torsion angles of Man(9)GlcNAc(2) is explored. The focus is made primarily on the third variable region (V3 loop) of gp120 due to its crucial relevance for coreceptor usage, as a principal neutralizing determinant (PND), and for its conserved glycosylation sites N295, N302 and N332. Valuable structural information from glycosylation effects is taken into account for the development of a V3 loop rational structure-based vaccine strategy using N295 and N302 as potential conformational epitope.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".