Improving Identification of In-organello Protein-Protein Interactions Using an Affinity-enrichable, Isotopically Coded, and Mass Spectrometry-cleavable Chemical Crosslinker
Bibliographic record
Abstract
chemical crosslinking and mass spectrometry (CLMS) has been developed that takes advantage of the specific characteristics of cyanurbiotindipropionylsuccinimide (CBDPS), an affinity-tagged isotopically coded mass spectrometry (MS)-cleavable crosslinking reagent. Utilizing this reagent in combination with a crosslinker-specific data-dependent acquisition strategy based on MS2 scans, and a software pipeline designed for integrating crosslinker-specific mass spectral information led to demonstrated improvements in the application of the CLMS technique, in terms of the detection, acquisition, and identification of crosslinker-modified peptides. This approach was evaluated on intact yeast mitochondria, and the results showed that hundreds of unique protein-protein interactions could be identified on an organelle proteome-wide scale. Both known and previously unknown protein-protein interactions were identified. These interactions were assessed based on their known sub-compartmental localizations. Additionally, the identified crosslinking distance constraints are in good agreement with existing structural models of protein complexes involved in the mitochondrial electron transport chain.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".