Proteome-wide analysis of phospho-regulated PDZ domain interactions through phosphomimetic proteomic peptide phage display
Bibliographic record
Abstract
We report phosphomimetic proteomic peptide-phage display, a powerful large-scale method for finding ligands of short linear motif binding domains that simultaneously pinpoint functional Ser/Thr phosphosites in three steps. First, we computationally designed an oligonucleotide library encoding all human C-terminal peptides containing known or predicted Ser/Thr phosphosites and phosphomimetic variants thereof. Second, we incorporated these oligonucleotides into a phage library. Third, we screened the six PDZ (PSD-95/Dlg/ZO-1) domains of Scribble and DLG1 for binding and identified known and novel ligands from the human proteome, and whether these interactions may be regulated by ligand phosphorylation. We demonstrate that the Scribble PDZ domains preferentially bind to ligands with phosphomimetic mutations at two distinct positions, and show that the equilibrium dissociation constant for Scribble PDZ1 with the C-terminal peptide of RPS6KA2 is enhanced over four-fold by phosphorylation. We elucidate the molecular determinants of phosphopeptide binding through NMR structure determination and mutational analysis. Finally, we discuss the role of Ser/Thr phosphorylation as a switching mechanism of PDZ domain interactions.
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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 | 0.000 | 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) | 0.001 | 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 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".