Prediction of Protein–DNA Complex Mobility in Gel-Free Capillary Electrophoresis
Bibliographic record
Abstract
Selection of protein binders from highly diverse combinatorial libraries of DNA-encoded small molecules is a highly promising approach for discovery of small-molecule drug leads. Methods of kinetic capillary electrophoresis provide the high efficiency of partitioning required for such selection but require the knowledge of electrophoretic mobility of the protein-ligand complex. Here we present a theoretical approach for an accurate estimate of the electrophoretic mobility of such complexes. The model is based on a theory of the thin double layer and corresponding expressions used for the mobilities of a rod-like short oligonucleotide and a sphere-like globular protein. The model uses empirical values of mobilities of free protein, free ligand, and electroosmotic flow. The model was tested with a streptavidin-dsDNA complex linked through biotin (small molecule). The deviation of the prediction from the experimental mobility did not exceed 4%, thus confirming that not only is the model adequate but it is also accurate. This model will facilitate reliable use of KCE methods for selection of drug leads from libraries of DNA-encoded small molecules.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".