Signal Attrition in Whole Cell Crosslinking Mass Spectrometry
Bibliographic record
Abstract
Abstract Crosslinking mass spectrometry (XL-MS) could replace traditional techniques for sampling the cellular interactome, such as affinity pulldown MS. It generates superior interaction data that can be used to better model cellular structure and function. However, the sampling depth of current XL-MS techniques is disappointing. Poor sampling is often blamed on a low-yielding crosslinking reaction, estimated to be ∼0.1% based on relative ion abundance in the mass spectrometer. Here, using a new two-step crosslinker installation process, we demonstrate that the yield of the chemical reaction is not the limiting factor in sampling the interactome. Low crosslink detection levels persist even when crosslink yields approach 30% of total peptide. We show that crosslinked peptides are not preferentially lost during sample workup; they enter the mass spectrometer at least as efficiently as linear peptides. Low detection rates arise mostly from severe signal splitting that reduces the S/N of crosslinked peptides, which is likely exacerbated by low-sensitivity database search tools and compounded by ion suppression.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".