Detection of <i>Listeria monocytogenes</i> with Short Peptide Fragments from Class IIa Bacteriocins as Recognition Elements
Bibliographic record
Abstract
We employed a direct peptide-bacteria binding assay to screen peptide fragments for high and specific binding to Listeria monocytogenes. Peptides were screened from a peptide array library synthesized on cellulose membrane. Twenty four peptide fragments (each a 14-mer) were derived from three potent anti-listerial peptides, Leucocin A, Pediocin PA1, and Curvacin A, that belong to class IIa bacteriocins. Fragment Leu10 (GEAFSAGVHRLANG), derived from the C-terminal region of Leucocin A, displayed the highest binding among all of the library fragments toward several pathogenic Gram-positive bacteria, including L. monocytogenes, Enterococcus faecalis, and Staphylococcus aureus. The specific binding of Leu10 to L. monocytogenes was further validated using microcantilever (MCL) experiments. Microcantilevers coated with gold were functionalized with peptides by chemical conjugation using a cysteamine linker to yield a peptide density of ∼4.8×10(-3) μmol/cm2 for different peptide fragments. Leu10 (14-mer) functionalized MCL was able to detect Listeria with same sensitivity as that of Leucocin A (37-mer) functionalized MCL, validating the use of short peptide fragments in bacterial detection platforms. Fragment Leu10 folded into a helical conformation in solution, like that of native Leucocin A, suggesting that both Leu10 and Leucocin A may employ a similar mechanism for binding target bacteria. The results show that peptide-conjugated microcantilevers can function as highly sensitive platforms for Listeria detection and hold potential to be developed as biosensors for pathogenic bacteria.
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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) | 0.000 | 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 | 0.001 | 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".