Robustness of In Vitro Selection Assays of DNA-Encoded Peptidomimetic Ligands to CBX7 and CBX8
Bibliographic record
Abstract
The identification of protein ligands from a DNA-encoded library is commonly conducted by an affinity selection assay. These assays are often not validated for robustness, raising questions about selections that fail to identify ligands and the utility of enrichment values for ranking ligand potencies. Here, we report a method for optimizing and utilizing affinity selection assays to identify potent and selective peptidic ligands to the highly related chromodomains of CBX proteins. To optimize affinity selection parameters, statistical analyses (Z' factors) were used to define the ability of selection assay conditions to identify and differentiate ligands of varying affinity. A DNA-encoded positional scanning library of peptidomimetics was constructed around a trimethyllysine-containing parent peptide, and parallel selections against the chromodomains from CBX8 and CBX7 were conducted over three protein concentrations. Relative potencies of off-DNA hit molecules were determined through a fluorescence polarization assay and were consistent with enrichments observed by DNA sequencing of the affinity selection assays. In addition, novel peptide-based ligands were discovered with increased potency and selectivity to the chromodomain of CBX8. The results indicate low DNA tag bias and show that affinity-based in vitro selection assays are sufficiently robust for both ligand discovery and determination of quantitative structure-activity relationships.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".