p75 Co-receptors Regulate Ligand-dependent and Ligand-independent Trk Receptor Activation, in Part by Altering Trk Docking Subdomains
Bibliographic record
Abstract
Neurotrophins signal via Trk tyrosine kinase receptors and a common receptor called p75. Nerve growth factor is the cognate ligand for TrkA, brain-derived neurotrophic factor for TrkB, and neurotrophin-3 (NT-3) for TrkC. NT-3 also binds TrkA and TrkB as a heterologous ligand. All neurotrophins bind p75, which regulates ligand affinity and Trk signals. Trk extracellular domain has five subdomains: a leucine-rich motif, two cysteine-rich clusters, and immunoglobulin-like subdomains IgG-C1 and IgG-C2. The IgG-C1 subdomain is surface exposed in the tertiary structure and regulates ligand-independent activation. The IgG-C2 subdomain is less exposed but regulates cognate ligand binding and Trk activation. NT-3 as a heterologous ligand of TrkA and TrkB optimally requires the IgG-C2 but also binds other subdomains of these receptors. When p75 is co-expressed, major changes are observed; NGF-TrkA activation can occur also via the cysteine 1 subdomain, and brain-derived neurotrophic factor-TrkB activation requires the TrkB leucine-rich motif and cysteine 2 subdomains. We propose a two-site model of Trk binding and activation, regulated conformationally by the IgG-C1 subdomain. Moreover, p75 affects Trk subdomain utilization in ligand-dependent activation, possibly by conformational or allosteric control.
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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) | 0.001 | 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) | 0.002 | 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".