Specificity and versatility of SH3 and other proline-recognition domains: structural basis and implications for cellular signal transduction
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
Protein-protein interactions occurring via the recognition of short peptide sequences by modular interaction domains play a central role in the assembly of signalling protein complexes and larger protein networks that regulate cellular behaviour. In addition to spatial and temporal factors, the specificity of signal transduction is intimately associated with the specificity of many co-operative, pairwise binding events upon which various pathways are built. Although protein interaction domains are usually identified via the recognition code, the consensus sequence motif, to which they selectively bind, they are highly versatile and play diverse roles in the cell. For example, a given interaction domain can bind to multiple sequences that exhibit no apparent identity, and, on the other hand, domains of the same class or different classes may favour a given consensus motif. This promiscuity in ligand selection is typified by the SH3 (Src homology 3) domain and several other interaction modules that commonly recognize proline-rich sequences. Furthermore, interaction domains are highly adaptable, a property that is essential for the evolution of novel pathways and modulation of signalling dynamics. The ability of certain interaction domains to perform multiple tasks, however, poses a challenge for the cell to control signalling specificity when cross-talk between pathways is undesired. Extensive structural and biochemical analysis of many interaction domains in recent years has started to shed light on the molecular basis underlying specific compared with diverse binding events that are mediated by interaction domains and the role affinity plays in affecting domain specificity and regulating cellular signal transduction.
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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 | 0.000 |
| 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 it