Design and Solution Structure of Functional Peptide Mimetics of Nerve Growth Factor
Bibliographic record
Abstract
The C-D loop in nerve growth factor (NGF) is involved in binding to the NGF receptor, TrkA. It is flexible and adopts several different types conformations in different NGF crystal forms. We have previously shown that a small cyclic peptide derived from the C-D loop of NGF binds to the TrkA receptor by mimicking the structure of this loop. To understand structure-function relationships in NGF C-D loop mimetics, we have produced a series of peptides predicted to form different types of beta-turns. The peptides were tested for their ability to promote cell survival in serum-free medium and to induce TrkA tyrosine phosphorylation. NMR structural studies were used to determined the backbone conformation and the spatial orientation of side chains involved in binding to the TrkA receptor. Peptides that form type I or type gammaL-alphaR beta-turns were the most active. The variety of active loop conformations suggests that the mimetics (and NGF) accommodate the binding site on TrkA by an 'induced fit' mechanism. In agreement with this hypothesis, NMR relaxation measurements detected both fast and slow motion in the peptides. We also characterized a retro-inverso peptide derived from the NGF C-D loop. This D-amino acid cyclic peptide did not adopt a conformation homologous to the NGF C-D loop and was inactive. This may be representative of difficulties in producing structural and functional mimetics by retro-inverso schemes.
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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".