Coevolution of PDZ domain–ligand interactions analyzed by high-throughput phage display and deep sequencing
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Bibliographic record
Abstract
The determinants of binding specificities of peptide recognition domains and their evolution remain important problems in molecular systems biology. Here, we present a new methodology to analyze the coevolution between a domain and its ligands by combining high-throughput phage display with deep sequencing. First, from a library of PDZ domains with diversity introduced at ten positions in the binding site, we evolved domains for binding to 15 distinct peptide ligands. Interestingly, for a given peptide many different functional domains emerged, which exhibited only limited sequence homology, showing that many different binding sites can recognize a given peptide. Subsequently, we used peptide-phage libraries and deep sequencing to map the specificity profiles of these evolved domains at high resolution, and we found that the domains recognize their cognate peptides with high affinity but low specificity. Our analysis reveals two aspects of evolution of new binding specificities. First, we were able to identify some common features amongst domains raised against a common peptide. Second, our analysis suggests that cooperative interactions between multiple binding site residues lead to a diversity of binding profiles with considerable plasticity. The details of intramolecular cooperativity remain to be elucidated, but nonetheless, we have established a general methodology that can be used to explore protein evolution in a systematic yet rapid manner.
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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