Decoding proline‐rich sequence recognition by epitope‐based proteomics
Bibliographic record
Abstract
Multifunctional protein surfaces pose a major challenge for the understanding of an individual protein's contribution to cellular signalling. Deconvolution of network connectivities for individual epitopes by combining site‐specific inhibition with stable isotope labelling of amino acids in cell culture (SILAC)/mass spectrometry provides a powerful tool of investigation and has been applied to signalling adaptors of the proline‐rich sequence (PRS) recognition family. We show that formation of the pre‐spliceosome and of mRNA surveillance complexes depend on PRS recognition and that PRS hubs within these complexes drive the assembly of dynamic initiator complexes (Kofler et al., Mol. Cell. Proteom 8, 2461–2473; Schlundt et al., Mol. Cell. Proteom. 8, 2474–2486 (2009)). Our approach thereby provides complementary information to knock‐down and knock‐out models of protein function and it also reveals the “moonlighting” potential of a given protein surface within the cellular context. This work was supported by grants FG806, SFB740 and SFB765 of the Deutsche Forschungsgemeinschaft.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".