Bivalent Peptidomimetic Ligands of TrkC Are Biased Agonists and Selectively Induce Neuritogenesis or Potentiate Neurotrophin-3 Trophic Signals
Bibliographic record
Abstract
This study was initiated to find small molecule ligands that would induce a functional response when docked with neurotrophin Trk receptors. "Minimalist" mimics of beta-turns were designed for this purpose. These mimics are (i) rigid, yet easily folded into turn-like conformations, and (ii) readily accessible from amino acids bearing most of the natural side chains. Gram quantities of 16 of these turn mimics were prepared and then assembled into 152 fluorescein-labeled bivalent peptidomimetics via a solution-phase combinatorial method. Fluorescence-based screening of these molecules using cells transfected with the Trk receptors identified 10 potential ligands of TrkC, the receptor for neurotrophin-3. Analogues of these bivalent peptidomimetics with biotin replacing the fluorescein label were then prepared and tested to confirm that binding was not due to the fluorescein. Several assays were conducted to find the mode of action of these biotinylated compounds. Thus, direct binding, survival and neuritogenic, and biochemical signal transduction assays showed 8 of the original 10 hits were agonistic ligands binding to the ectodomain of TrkC. Remarkably, some peptidomimetics afford discrete signals leading to either cell survival or neuritogenic differentiation. The significance of this work is three-fold. First, we succeeded in finding small, selective, proteolytically stable ligands for the TrkC receptor; there are very few of these in the literature. Second, we show that it is possible to activate distinct and biased signaling pathways with ligands binding at the ectodomain of wild-type receptors. Third, the discovery that some peptidomimetics initiate different modes of cell signaling increases their potential as pharmacological probes and therapeutic leads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".