Click-linking: a cell-compatible protein crosslinking method based on click chemistry
Bibliographic record
Abstract
Crosslinking mass spectrometry (XL-MS) has the potential to map the human interactome at high resolution and with high fidelity, replacing indirect, error-prone sampling methods such as affinity pulldown MS. However, the sampling depth of XL-MS remains stubbornly low. We present a crosslinking strategy that splits the crosslinking reaction into two sequential and orthogonal coupling events. The method involves pre-stabilizing the spatial proteome with a fixation protocol inspired by immunofluorescence imaging, followed by a stepwise process that begins with extensively labeling surface-accessible lysines in the cell with N-hydroxysuccinimide (NHS)-modified click reagents. We show that a subsequent copper-catalyzed azide-alkyne cycloaddition (CuAAC) reaction of the installed precursors generates crosslinks at levels approaching 30% of the total signal, as demonstrated by a subtractive approach. The method generates no detectable side reactions or obvious distortions of the spatial proteome. Protein-protein interactions (PPIs) are detected at levels approximately 20 times higher than a conventional DSS-based method, outperforming even enrichable crosslinkers. Crosslinking mass spectrometry offers direct PPI mapping but suffers from low yields. Here, the authors develop a two-step click-linking strategy that improves crosslinking efficiency and interactome coverage in fixed cells, outperforming conventional methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".