TrkA Receptor “Hot Spots” for Binding of NT-3 as a Heterologous Ligand
Bibliographic record
Abstract
Neurotrophins signal via Trk tyrosine kinase receptors. Nerve growth factor (NGF) is the cognate ligand for TrkA, the brain-derived neurotrophic factor for TrkB, and NT-3 for TrkC. NT-3 also binds TrkA as a lower affinity heterologous ligand. Because neurotrophin-3 (NT-3) interactions with TrkA are biologically relevant, we aimed to define the TrkA "hot spot" functional docking sites of NT-3. The Trk extracellular domain consists of two cysteine-rich subdomains (D1 and D3), flanking a leucine-rich subdomain (D2), and two immunoglobulin-like subdomains IgC1(D4) and IgC2(D5). Previously, the D5 subdomain was defined as the primary ligand-binding site of neurotrophins for their cognate receptors (e.g. NGF binds and activates through TRKA-D5 hot spots). Here binding studies with truncated and chimeric extracellular subdomains show that TRKA-D5 also includes an NT-3 docking and activation hot spot (site 1), and competition studies show that the NGF and NT-3 hot spots on TRKA-D5 are distinct but partially overlapping. In addition, ligand binding studies provide evidence for an NT-3-binding/allosteric site on TRKA-D4 (site 2). NT-3 docking on sites 1 and/or 2 partially blocks NGF binding. Functional survival studies showed that sites 1 and 2 regulate TrkA activation. NT-3 docking on both sites 1 and 2 affords full agonism, which can be additive with NGF activation of Trk. However, NT-3 docking solely on site 1 is partially agonistic but noncompetitively antagonizes NGF binding and activation of Trk. This study demonstrates that Trk signaling is more complex than previously thought because it involves several receptor subdomains and hot spots.
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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) | 0.003 | 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".