Yeast two-hybrid screening of cyclic peptide libraries using a combination of random and PI-deconvolution pooling strategies
Bibliographic record
Abstract
We developed a high throughput yeast two-hybrid (Y2H) assay for screening pools of combinatorial cyclic peptide preys against pools of bait proteins. The assay used the PI (pooling with imaginary tags) deconvolution pooling strategy to generate pools of baits and a random pooling strategy to generate pools of cyclic peptide preys. Haploid yeast, expressing pools of baits or preys, were arrayed and mated to each other to generate diploid arrays, where the yeast express both baits and preys. Diploid arrays were scored for activation of the Y2H reporter genes. We used this Y2H pooling strategy, referred to as 'PI-pool-on-random pool', to screen a cyclic peptide library for interactions against Bcr-Abl domains. Seven Bcr-Abl domain baits and LexA control were pooled using the PI deconvolution pooling strategy. The cyclic peptide library was randomly arrayed into pools of ~1000 members. Cyclic peptides were isolated for six of seven Bcr-Abl domain baits. The PI-pool-on-random pooling Y2H assay using high stringency Y2H reporter genes produced no false positives, while missing 20% of real interactions. The high specificity of the PI-pool-on-random pooling Y2H assay eliminates the need to validate interactions. Pooling of baits and preys allows large prey libraries to be screened against multiple baits and takes advantage of PI-deconvolution to determine protein interactions with high sensitivity and specificity. The scalability of this assay allows the peptide preys to be isolated in a high throughput manner against a large number of baits and provides an avenue for generating affinity agents against entire proteomes in the future.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".