High-Throughput Screening of One-Bead–One-Compound Peptide Libraries Using Intact Cells
Bibliographic record
Abstract
Screening approaches based on one-bead-one-compound (OBOC) combinatorial libraries have facilitated the discovery of novel peptide ligands for cellular targeting in cancer and other diseases. Recognition of cell surface proteins is optimally achieved using live cells, yet screening intact cell populations is time-consuming and inefficient. Here, we evaluate the Complex Object Parametric Analyzer and Sorter (COPAS) large particle biosorter for high-throughput sorting of bead-bound human cell populations. When a library of RGD-containing peptides was screened against human cancer cells that express αvβ3 integrin, it was found that bead-associated cells are rapidly dissociated when sorted through the COPAS instrument. When the bound cells were reversibly cross-linked onto the beads, however, we demonstrated that cell/bead mixtures can be sorted quickly and accurately. This reversible cross-linking approach is compatible with matrix-assisted laser desorption ionization time-of-flight mass spectrometry-based peptide sequence deconvolution. This approach should allow one to rapidly screen an OBOC library and identify novel peptide ligands against cell surface targets in their native conformation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".