Alternative Splicing of a Receptor Intracellular Domain Yields Different Ectodomain Conformations, Enabling Isoform-Selective Functional Ligands
Bibliographic record
Abstract
Events at a receptor ectodomain affect the intracellular domain conformation, activating signal transduction (out-to-in conformational effects). We investigated the reverse direction (in-to-out) where the intracellular domain may impact on ectodomain conformation. The primary sequences of naturally occurring TrkC receptor isoforms (TrkC-FL and TrkC.T1) only differ at the intracellular domain. However, owing to their differential association with Protein Disulfide Isomerase the isoforms have different disulfide bonding and conformations at the ectodomain. Conformations were exploited to develop artificial ligands, mAbs, and small molecules, with isoform-specific binding and biased activation. Consistent, the physiological ligands NT-3 and PTP-sigma bind both isoforms, but NT-3 activates all signaling pathways, whereas PTP-sigma activates biased signals. Our data support an "in-to-out" model controlling receptor ectodomain conformation, a strategy that enables heterogeneity in receptors, ligands, and bioactivity. These concepts may be extended to the many wild-type or oncogenic receptors with known isoforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".