Plug−Plug Kinetic Capillary Electrophoresis: Method for Direct Determination of Rate Constants of Complex Formation and Dissociation
Bibliographic record
Abstract
We present a method for direct determination of rate constants of complex formation, k(on), and dissociation, k(off). The method is termed plug-plug kinetic capillary electrophoresis (ppKCE). To explain the concept of the method, we consider the formation of a noncovalent complex C between molecules A and B; A is assumed to migrate slower in electrophoresis than B. In ppKCE, a short plug of A is injected into a capillary, followed by a short plug of B. When a high voltage is applied, the electrophoretic zone of B moves through that of A, allowing for the formation of C. When the zones of A and B are separated, C starts dissociating. The features of the resulting electropherogram are defined by both binding and dissociation. We developed a unique mathematical approach that allows finding k(on) and k(off) from a single electropherogram without nonlinear regression analysis. The approach uses algebraic functions with the only input parameters from electropherograms being areas and migration times of electrophoretic peaks. In this work, we explain theoretical bases of ppKCE and prove the principle of the method by finding k(on) and k(off) for a protein-ligand complex. The unique capability of the method to directly determine both k(on) and k(off) along with its simplicity make ppKCE highly attractive to a broad community of molecular scientists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".