Conjugation of an scFab domain to the oligomeric HIV envelope protein for use in immune targeting
Bibliographic record
Abstract
A promising strategy for the enhancement of vaccine-mediated immune responses is by directly targeting protein antigens to immune cells. Targeting of antigens to the dendritic cell (DC) molecule Clec9A has been shown to enhance antibody affinity and titers for model antigens, and influenza and enterovirus antigens, and may be advantageous for immunogens that otherwise fail to elicit antibodies with sufficient titers and breadth for broad protection, such as the envelope protein (Env) of HIV. Previously employed targeting strategies often utilize receptor-specific antibodies, however it is impractical to conjugate a bivalent IgG antibody to oligomeric antigens, including HIV Env trimers. Here we designed single chain variable fragment (scFv) and single chain Fab (scFab) constructs of a Clec9A-targeting antibody, expressed as genetically fused conjugates with the soluble ectodomain of Env, gp140. This conjugation did not affect the presentation of Env neutralising antibody epitopes. The scFab moiety was shown to be more stable than scFv, and in the context of gp140 fusions, was able to mediate better binding to recombinant and cell surface-expressed Clec9A, although the level of binding to cell-surface Clec9A was lower than that of the anti-Clec9A IgG. However, binding to Clec9A on the surface of DCs was not detected. Mouse immunization experiments suggested that the Clec9A-binding activity of the scFab-gp140 conjugate was insufficient to enhance Env-specific antibody responses. This is an important first proof of principle study demonstrating the conjugation of a scFab to an oligomeric protein antigen, and that an scFab displays better antigen binding than the corresponding scFv. Future developments of this technique that increase the scFab affinity will provide a valuable means to target oligomeric proteins to cell surface antigens of interest, improving vaccine-generated immune responses.
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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".