Enhanced Neutralization of HIV by Antibodies Displayed on the S-Layer of Caulobacter crescentus
Bibliographic record
Abstract
Innovative methods of prevention are needed to stop the more than two million new HIV-1 infections annually, particularly in women. Local application of anti-HIV antibodies has been shown to be effective at preventing infection in nonhuman primates; however, the concentrations needed are cost prohibitive. Display of antibodies on a particulate platform will likely prolong effectiveness of these anti-HIV agents and lower the cost of goods. Here, we demonstrate that the bacterium Caulobacter crescentus and its highly expressed surface-layer (S-layer) protein can provide this antibody display platform. Caulobacters displaying protein G, alone or with CD4 codisplay, successfully captured HIV-1-specific antibodies and demonstrated functional neutralization. Compared to soluble antibodies, a neutralizing anti-HIV antibody displayed on Caulobacter was as effective or more effective at neutralizing diverse HIV-1 isolates. Moreover, when an antibody reactive with an epitope induced by CD4 binding (CD4i) was codisplayed with CD4, there was significant enhancement in HIV-1 neutralization. These results suggest that caulobacters displaying anti-HIV antibodies offer a distinct improvement in the use of antibodies as microbicides. Furthermore, these reagents can specifically evaluate anti-HIV antibodies in concert with other HIV-1 blocking agents to assess the most suitable tools for conversion to scFvs, allowing for direct display within the S-layer protein and further reducing cost of goods. In summary, C. crescentus, which can be easily produced and chemically stabilized at low cost, is well suited for engineering as an effective platform, offering an inexpensive way to produce and deliver HIV-1-specific microbicides.
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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) | 0.000 | 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".