Systematic identification of SH3 domain‐mediated human protein–protein interactions by peptide array target screening
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
Systematic identification of direct protein-protein interactions is often hampered by difficulties in expressing and purifying the corresponding full-length proteins. By taking advantage of the modular nature of many regulatory proteins, we attempted to simplify protein-protein interactions to the corresponding domain-ligand recognition and employed peptide arrays to identify such binding events. A group of 12 Src homology (SH) 3 domains from eight human proteins (Swiss-Prot ID: SRC, PLCG1, P85A, NCK1, GRB2, FYN, CRK) were used to screen a peptide target array composed of 1536 potential ligands, which led to the identification of 921 binary interactions between these proteins and 284 targets. To assess the efficiency of the peptide array target screening (PATS) method in identifying authentic protein-protein interactions, we examined a set of interactions mediated by the PLCgamma1 SH3 domain by coimmunoprecipitation and/or affinity pull-downs using full-length proteins and achieved a 75% success rate. Furthermore, we characterized a novel interaction between PLCgamma1 and hematopoietic progenitor kinase 1 (HPK1) identified by PATS and demonstrated that the PLCgamma1 SH3 domain negatively regulated HPK1 kinase activity. Compared to protein interactions listed in the online predicted human interaction protein database (OPHID), the majority of interactions identified by PATS are novel, suggesting that, when extended to the large number of peptide interaction domains encoded by the human genome, PATS should aid in the mapping of the human interactome.
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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 | 0.001 | 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