The Structure of the Lingo-1 Ectodomain, a Module Implicated in Central Nervous System Repair Inhibition
Bibliographic record
Abstract
Nogo receptor (NgR)-mediated control of axon growth relies on the central nervous system-specific type I transmembrane protein Lingo-1. Interactions between Lingo-1 and NgR, along with a complementary co-receptor, result in neurite and axonal collapse. In addition, the inhibitory role of Lingo-1 is particularly important in regulation of oligodendrocyte differentiation and myelination, suggesting that pharmacological modulation of Lingo-1 function could be a novel approach for nerve repair and remyelination therapies. Here we report on the crystal structure of the ligand-binding ectodomain of human Lingo-1 and show it has a bimodular, kinked structure composed of leucine-rich repeat (LRR) and immunoglobulin (Ig)-like modules. The structure, together with biophysical analysis of its solution properties, reveals that in the crystals and in solution Lingo-1 persistently associates with itself to form a stable tetramer and that it is its LRR-Ig-composite fold that drives such assembly. Specifically, in the crystal structure protomers of Lingo-1 associate in a ring-shaped tetramer, with each LRR domain filling an open cleft in an adjacent protomer. The tetramer buries a large surface area (9,200 A2) and may serve as an efficient scaffold to simultaneously bind and assemble the NgR complex components during activation on a membrane. Potential functional binding sites that can be identified on the ectodomain surface, including the site of self-recognition, suggest a model for protein assembly on the membrane.
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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".