Functional Modulation of Peptide Structural Interaction Fingerprints upon Stereochemical Diversification
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The total conformational search space of an l -amino acid in a polypeptide chain is limited to 21% with structural variation including helices, sheets, turns, and random coils. The conformational search space of a polypeptide will expand to astronomical propositions if both l - and d -chiral amino acids are used to design novel polypeptide chains with altogether new structures and functions. This possibility has been systematically investigated in this study by making designed mutations that result in novel architectures. We hypothesize that the electrostatic interaction fingerprints of these peptides are distinctly different with stereochemical mutations of the polypeptide chain, and this will consequently reflect in their ability to interact and penetrate the membrane. The designed peptide structures are examined for their ability to interact with bacterial and mammalian cell membranes by employing molecular dynamics simulations. Mammalian triple-negative breast cancer (MDA-MB-231) and noncancerous (MCF-10A) cell lines were treated with 5(6)-carboxyfluorescein-labeled peptides, and the cellular uptake was measured quantitatively by flow cytometry for objective comparison of their ability to be used as anticancer or drug delivery agents. Antibacterial properties of the peptides were tested for Gram-positive Staphylococcus aureus ( S. aureus ) and Gram-negative Escherichia coli ( E. coli ) species. Both in silico and in vitro experimental results are largely supportive of the hypothesis tested, and they further point to the possibility of expanding the polypeptide conformational space in the search for novel functional molecular constructs. Our study also identified two promising therapeutic peptides for further development: YRC03 as a viable antibacterial agent against Gram-negative bacteria and YRC01 as a promising drug delivery vehicle for breast cancer treatment.
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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".