Rapid Identification of Linear Protein Domain Binding Motifs Using Peptide SPOT Arrays
Why this work is in the frame
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Bibliographic record
Abstract
Understanding protein-protein interactions is a key step in unravelling the roles proteins play in cellular function. The ability to analyse protein-protein interactions rapidly and economically is a powerful research tool. Using peptide SPOT arrays, peptides of known sequence can be synthesized directly in discrete spots on a cellulose membrane and assayed for an interaction with a protein of interest. Several hundred peptides can be synthesized on each cellulose membrane; therefore, this method is amenable to designing high-throughput peptide binding studies. SPOT arrays are particularly well suited for deducing peptidic binding motifs within proteins that are difficult to purify in sufficient quantities for traditional biochemical analyses, as well as for determining binding specificities and targets for proteins of undefined function. Peptide SPOT arrays have been used extensively to define protein-protein interaction surfaces. In this chapter, we will outline the steps involved in designing and probing a peptide SPOT array to identify peptide binding motifs for a protein of interest.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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