Influence of Non-natural Cationic Amino Acids on the Biological Activity Profile of Innate Defense Regulator Peptides
Bibliographic record
Abstract
Abstract Non-natural amino acids can be incorporated into synthetic host defense peptides (HDPs) to modulate their susceptibility to proteolytic degradation. However, the impact of non-natural amino acids on the antibiofilm and immunomodulatory activities of synthetic HDPs remains unclear. Using SPOT-synthesized peptide arrays, non-natural cationic amino acids of varying side chain lengths were incorporated into a synthetic HDP, IDR-1018, and the impact of these substitutions on the antibiofilm activity toward methicillin resistant Staphylococcus aureus biofilms was assessed. Multiply-substituted derivatives were designed that incorporated favorable non-natural cationic amino acid moieties throughout IDR-1018. The antibiofilm and immunomodulatory activities of these derivatives were assessed in vitro, revealing that the incorporation of non-natural amino acids modulated (either positively or negatively) these activities of IDR-1018. Furthermore, the tryptic stability of the IDR-1018 derivatives was assessed revealing that proteolytic stability was favored for shorter cationic side chains and was influenced by the primary peptide sequence.
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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) | 0.001 | 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".