High sensitivity proteome-scale search for crosslinked peptides using CRIMP 2.0
Bibliographic record
Abstract
Abstract Crosslinking mass spectrometry (XL-MS) is a valuable technique for the generation of point-to-point distance measurements in protein space. Applications involving in situ chemical crosslinking have created the possibility of mapping whole protein interactomes with high spatial resolution. However, an XL-MS experiment carried out directly on cells requires highly efficient software that can detect crosslinked peptides with sensitivity and controlled error rates. Many algorithmic approaches invoke a filtering strategy designed to reduce the size of the database prior to mounting a search for crosslinks, but concern has been expressed over the possibility of reduced sensitivity with such strategies. Here we present a full upgrade to CRIMP, the crosslinking app in the Mass Spec Studio, which implements a new strategy for the detection of both component peptides in the MS 2 spectrum. Using several published datasets, we demonstrate that this pre-searching method is sensitive and fast, permitting whole proteome searches on a conventional desktop computer for both cleavable and noncleavable crosslinkers. We introduce a new strategy for scoring crosslinks, adapted from computer vision algorithms, that properly resolves conflicting XL hits from other crosslinking reaction products, and we present a method for enhancing the detection of protein-protein interactions that relies upon compositional data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".