Crosslinking combined with mass spectrometry for structural proteomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The method of crosslinking combined with mass spectrometry is being gradually accepted as a technology enabling detailed structural information on proteins and protein complexes. Intrinsic challenges of the method, which have prevented its widespread use, are being progressively addressed by improvements in mass spectrometry instrumentation capabilities, by the development of new crosslinking reagents, and by the development of specialized software tools for processing of mass spectrometric crosslinking data. This review focuses on recent literature concerning the development of specialized crosslinking reagents and approaches for mass spectrometry-based applications. Critical features of crosslinking reagents for optimum mass spectrometric performance, such as isotopic coding, cleavability, affinity groups, structure of the linkers, and reactive groups, are assessed. Requirements for the design of crosslinking reagents to make them well suited for mass spectrometric detection and analysis are summarized.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it