Proline-rich antimicrobial peptides targeting protein synthesis
Bibliographic record
Abstract
Covering: up to 2017The innate immune system employs a broad array of antimicrobial peptides (AMPs) to attack invading microorganisms. While most AMPs act by permeabilizing the bacterial membrane, specific subclasses of AMPs have been identified that pass through membranes and inhibit bacterial growth by targeting fundamental intracellular processes. One such subclass is the proline-rich antimicrobial peptides (PrAMPs) that bind to the ribosome and interfere with the process of protein synthesis. A diverse range of PrAMPs have been identified in insects, such as bees, wasps and beetles, and crustaceans, such as crabs, as well as in mammals, such as cows, sheep, goats and pigs. Mechanistically, the best-characterized PrAMPs are the insect oncocins, such as Onc112, and bovine bactenecins, such as Bac7. Biochemical and structural studies have revealed that these PrAMPs bind within the ribosomal exit tunnel with a reverse orientation compared to a nascent polypeptide chain. The PrAMPs allow initiation but prevent the transition into the elongation phase of translation. Insight into the interactions of PrAMPs with their ribosomal target provides the opportunity to further develop these peptides as novel antimicrobial agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".