Predicting Electrophoretic Mobility of Protein–Ligand Complexes for Ligands from DNA-Encoded Libraries of Small Molecules
Bibliographic record
Abstract
Selection of target-binding ligands from DNA-encoded libraries of small molecules (DELSMs) is a rapidly developing approach in drug-lead discovery. Methods of kinetic capillary electrophoresis (KCE) may facilitate highly efficient homogeneous selection of ligands from DELSMs. However, KCE methods require accurate prediction of electrophoretic mobilities of protein-ligand complexes. Such prediction, in turn, requires a theory that would be applicable to DNA tags of different structures used in different DELSMs. Here we present such a theory. It utilizes a model of a globular protein connected, through a single point (small molecule), to a linear DNA tag containing a combination of alternating double-stranded and single-stranded DNA (dsDNA and ssDNA) regions of varying lengths. The theory links the unknown electrophoretic mobility of protein-DNA complex with experimentally determined electrophoretic mobilities of the protein and DNA. Mobility prediction was initially tested by using a protein interacting with 18 ligands of various combinations of dsDNA and ssDNA regions, which mimicked different DELSMs. For all studied ligands, deviation of the predicted mobility from the experimentally determined value was within 11%. Finally, the prediction was tested for two proteins and two ligands with a DNA tag identical to those of DELSM manufactured by GlaxoSmithKline. Deviation between the predicted and experimentally determined mobilities did not exceed 5%. These results confirm the accuracy and robustness of our model, which makes KCE methods one step closer to their practical use in selection of drug leads, and diagnostic probes from DELSMs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".