Retrocyclins neutralize bacterial toxins by potentiating their unfolding
Bibliographic record
Abstract
Defensins are a class of immune peptides with a broad range of activities against bacterial, fungal and viral pathogens. Besides exerting direct anti-microbial activity via dis-organization of bacterial membranes, defensins are also able to neutralize various unrelated bacterial toxins. Recently, we have demonstrated that in the case of human α- and β-defensins, this later ability is achieved through exploiting toxins' marginal thermodynamic stability, i.e. defensins act as molecular anti-chaperones unfolding toxin molecules and exposing their hydrophobic regions and thus promoting toxin precipitation and inactivation [Kudryashova et al. (2014) Immunity 41, 709-721]. Retrocyclins (RCs) are humanized synthetic θ-defensin peptides that possess unique cyclic structure, differentiating them from α- and β-defensins. Importantly, RCs are more potent against some bacterial and viral pathogens and more stable than their linear counterparts. However, the mechanism of bacterial toxin inactivation by RCs is not known. In the present study, we demonstrate that RCs facilitate unfolding of bacterial toxins. Using differential scanning fluorimetry (DSF), limited proteolysis and collisional quenching of internal tryptophan fluorescence, we show that hydrophobic regions of toxins normally buried in the molecule interior become more exposed to solvents and accessible to proteolytic cleavage in the presence of RCs. The RC-induced unfolding of toxins led to their precipitation and abrogated activity. Toxin inactivation by RCs was strongly diminished under reducing conditions, but preserved at physiological salt and serum concentrations. Therefore, despite significant structural diversity, α-, β- and θ-defensins employ similar mechanisms of toxin inactivation, which may be shared by anti-microbial peptides from other families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".