A Novel Family of Cyclic Peptide Antagonists Suggests That N-cadherin Specificity Is Determined by Amino Acids That Flank the HAV Motif
Bibliographic record
Abstract
The classical cadherins (e.g. N-, E-, and P- cadherin) are well established homophilic adhesion molecules; however, the mechanism that governs cadherin specificity remains contentious. The classical cadherins contain an evolutionarily conserved His-Ala-Val (HAV) sequence, and linear peptides harboring this motif are capable of inhibiting a variety of cadherin-dependent processes. We now demonstrate that short cyclic HAV peptides can inhibit N-cadherin function. Interestingly, the nature of the amino acids that flank the HAV motif determine both the activity and specificity of the peptides. For example, when the HAV motif is flanked by a single aspartic acid, which mimics the natural HAVD sequence of N-cadherin, the peptide becomes a much more effective inhibitor of N-cadherin function. In contrast, when the HAV motif is flanked by a single serine, which mimics the natural HAVS sequence of E-cadherin, it loses its ability to inhibit the N-cadherin response. Our results demonstrate that subtle changes in the amino acids that flank the HAV motif can account for cadherin specificity and that small cyclic peptides can inhibit cadherin function. An emerging role for cadherins in a number of pathological processes suggests that the cyclic peptides reported in this study might be developed as therapeutic agents.
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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 | 0.000 |
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".