Bacterial Virus Lambda Gpd-Fusions to Cathelicidins, α- and β-Defensins, and Disease-Specific Epitopes Evaluated for Antimicrobial Toxicity and Ability to Support Phage Display
Bibliographic record
Abstract
We showed that antimicrobial polypeptides, when translated as gene fusions to the bacteriophage lambda capsid decoration protein gpD, formed highly toxic molecules within E. coli, suggesting that they can retain their antimicrobial activity conformation when fused to gpD. These include gpD-fusions to human and porcine cathelicidins LL37 and PR39, β-defensins HBD3 and DEFB126-Δ (deleted for its many COOH-terminal glycosylation sites), and α-defensin HD5. Antimicrobial toxicity was only observed when the peptides were displayed from the COOH-terminal, and not the NH2-terminal end, of gpD. This suggests that COOH-terminal displayed polypeptides of gpD-fusions can more readily form an active-state conformation than when they are displayed from the NH2-terminal end of gpD. The high toxicity of the COOH-displayed gpD-defensins suggests either that the fused defensin peptides can be oxidized, forming three correct intramolecular disulfide bonds within the cytosol of bacterial cells, or that the versions without disulfide bonds are highly toxigenic. We showed the high efficiency of displaying single epitope 17 amino-acid fusions to gpD on LDP (lambda display particles), even when the gpD-fusion protein was toxic. The efficient formation of high display density LDP, displaying a single disease specific epitope (DSE), suggests the utility of LDP-DSE constructs for use as single epitope vaccines (SEV).
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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".