Degenerate specificity of PDZ domains from RhoA-specific nucleotide exchange factors PDZRhoGEF and LARG.
Bibliographic record
Abstract
PDZ domains are ubiquitous protein-protein interaction modules which bind short, usually carboxyterminal fragments of receptors, other integral or membrane-associated proteins, and occasionally cytosolic proteins. Their role in organizing multiprotein complexes at the cellular membrane is crucial for many signaling pathways, but the rules defining their binding specificity are still poorly understood and do not readily explain the observed diversity of their known binding partners. Two homologous RhoA-specific, multidomain nucleotide exchange factors PDZRhoGEF and LARG contain PDZ domains which show a particularly broad recognition profile, as suggested by the identification of five diverse biological targets. To investigate the molecular roots of this phenomenon, we constructed a phage display library of random carboxyterminal hexapeptides. Peptide variants corresponding to the sequences identified in library selection were synthesized and their affinities for both PDZ domains were measured and compared with those of peptides derived from sequences of natural partners. Based on the analysis of the binding sequences identified for PDZRhoGEF, we propose a sequence for an 'optimal' binding partner. Our results support the hypothesis that PDZ-peptide interactions may be best understood when one considers the sum of entropic and dynamic effects for each peptide as a whole entity, rather than preferences for specific residues at a given position.
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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".