Site‐specific photo‐crosslinking/cleavage for protein–protein interface identification reveals oligomeric assembly of lysosomal‐associated membrane protein type 2A in mammalian cells
Bibliographic record
Abstract
Abstract Genetic code expansion enables site‐specific photo‐crosslinking by introducing photo‐reactive non‐canonical amino acids into proteins at defined positions during translation. This technology is widely used for analyzing protein–protein interactions and is applicable in mammalian cells. However, the identification of the crosslinked region still remains challenging. Here, we developed a new method to identify the crosslinked region by pre‐installing a site‐specific cleavage site, an α‐hydroxy acid (Nε‐allyloxycarbonyl‐α‐hydroxyl‐l‐lysine acid, AllocLys‐OH), into the target protein. Alkaline treatment cleaves the crosslinked complex at the position of the α‐hydroxy acid residue and thus helps to identify which side of the cleavage site, either closer to the N‐terminus or C‐terminus, the crosslinked site is located within the target protein. A series of AllocLys‐OH introductions narrows down the crosslinked region. By applying this method, we identified the crosslinked regions in lysosomal‐associated membrane protein type 2A (LAMP2A), a receptor of chaperone‐mediated autophagy, in mammalian cells. The results suggested that at least two interfaces are involved in the homophilic interaction, which requires a trimeric or higher oligomeric assembly of adjacent LAMP2A molecules. Thus, the combination of site‐specific crosslinking and site‐specific cleavage promises to be useful for revealing binding interfaces and protein complex geometries.
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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".