Binding of the Mannose-Specific Lectin, Griffithsin, to HIV-1 gp120 Exposes the CD4-Binding Site
Bibliographic record
Abstract
The glycans on HIV-1 gp120 play an important role in shielding neutralization-sensitive epitopes from antibody recognition. They also serve as targets for lectins that bind mannose-rich glycans. In this study, we investigated the interaction of the lectin griffithsin (GRFT) with HIV-1 gp120 and its effects on exposure of the CD4-binding site (CD4bs). We found that GRFT enhanced the binding of HIV-1 to plates coated with anti-CD4bs antibodies b12 and b6 or the CD4 receptor mimetic CD4-IgG2. The average enhancement of b12 or b6 binding was higher for subtype B viruses than for subtype C, while for CD4-IgG2, it was similar for both subtypes, although lower than observed with antibodies. This GRFT-mediated enhancement of HIV-1 binding to b12 was reflected in synergistic neutralization for 2 of the 4 viruses tested. The glycan at position 386, which shields the CD4bs, was involved in both GRFT-mediated enhancement of binding and neutralization synergism between GRFT and b12. Although GRFT enhanced CD4bs exposure, it simultaneously inhibited ligand binding to the coreceptor binding site, suggesting that GRFT-dependent enhancement and neutralization utilize independent mechanisms. This study shows for the first time that GRFT interaction with gp120 exposes the CD4bs through binding the glycan at position 386, which may have implications for how to access this conserved site.
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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".