Peptide-Bacteria Interactions using Engineered Surface-Immobilized Peptides from Class IIa Bacteriocins
Bibliographic record
Abstract
Specificity of the class IIa bacteriocin Leucocin A (LeuA), an antimicrobial peptide active against Gram-positive bacteria, including Listeria monocytogenes , is known to be dictated by the C-terminal amphipathic helical region, including the extended hairpin-like structure. However, its specificity when attached to a substrate has not been investigated. Exploiting properties of LeuA, we have synthesized two LeuA derivatives, which span the amphipathic helical region of the wild-type LeuA, consisting of 14- (14AA LeuA, CWGEAFSAGVHRLA) and 24-amino acid residues (24AA LeuA, CSVNWGEAFSAGVHRLANGGNGFW). The peptides were purified to >95% purity, as shown by analytical RP-HPLC and mass spectrometry. By including an N-terminal cysteine group, the tailored peptide fragments were readily immobilized at the gold interfaces. The resulting thickness and molecular orientation, determined by ellipsometry and grazing angle infrared spectroscopy, respectively, indicated that the peptides were covalently immobilized in a random helical orientation. The bacterial specificity of the anchored peptide fragments was tested against Gram-positive and Gram-negative bacteria. Our results showed that the adsorbed 14AA LeuA exhibited no specificity toward the bacterial strains, whereas the surface-immobilized 24AA LeuA displayed significant binding toward Gram-positive bacteria with various binding affinities from one strain to another. The 14AA LeuA did not show binding as this fragment is most likely too short in length for recognition by the membrane-bound receptor on the target bacterial cell membrane. These results support the potential use of class IIa bacteriocins as molecular recognition elements in biosensing platforms.
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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 | 0.000 | 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".