Positional Scanning for Peptide Secondary Structure by Systematic Solid-Phase Synthesis of Amino Lactam Peptides
Bibliographic record
Abstract
Incorporation of amino lactams into biologically active peptides has been commonly used to restrict conformational mobility, enhance selectivity, and increase potency. A solid-phase method using a Fmoc-protection strategy has been developed for the systematic synthesis of peptides containing configurationally defined alpha- and beta-amino gamma-lactams. N-Alkylation of N-silyl peptides with five- and six-member cyclic sulfamidates 9 and 8 minimized bis-alkylation and provided N-alkyl peptides, which underwent lactam annulation under microwave heating. Employing this solid-phase protocol on the growth hormone secretagogue GHRP-6, as well as on the allosteric modulator of the IL-1 receptor 101.10, has furnished 16 lactam derivatives and validated the effectiveness of this approach on peptides bearing aliphatic, aromatic, branched, charged, and heteroatomic side chains. The binding affinity IC(50) values of the GHRP-6 lactam analogues on both the GHS-R1a and CD36 receptors are reported as well as inhibition of thymocyte proliferation measurements for the 101.10 lactam analogues. In these cases, lactam analogues were prepared exhibiting similar or improved properties compared with the parent peptide. Considering the potential for amino lactams to induce peptide turn conformations, the effective method described herein for their supported construction on growing peptides, and for the systematical amino lactam scan of peptides, has proven useful for the rapid identification of the secondary structure necessary for peptide biological activity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".