Nonequilibrium Capillary Electrophoresis of Equilibrium Mixtures − A Single Experiment Reveals Equilibrium and Kinetic Parameters of Protein−DNA Interactions
Bibliographic record
Abstract
We introduce a novel electrophoretic method, nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM), and demonstrate its use for studying protein-DNA interactions. The equilibrium mixture of protein and DNA contains three components: free protein, free DNA, and the protein-DNA complex. A short plug of such a mixture is injected into the capillary, and the three components are separated under nonequilibrium conditions. The resulting electropherograms are composed of characteristic peaks and exponential curves. An easy nonnumerical analysis of a single electropherogram reveals two parameters: the equilibrium binding constant and the monomolecular rate constant of complex decay. The bimolecular rate constant of complex formation can then be calculated as the product of the two experimentally determined constants. NECEEM was applied to study the interaction between single-stranded DNA binding protein and a fluorescently labeled 15-mer oligonucleotide. It allowed us to measure for the first time the rate constant of complex decay for this important protein-DNA pair, k-1 = 0.03 s-1. The value of the equilibrium binding constant, Kb = 3.6 x 10-6 M-1, was in good agreement with those measured by other methods. As low as 10-18 mol of the protein was sufficient for the measurements. Thus, the new method is simple, informative, and highly sensitive. Moreover, it can be equally applied to other noncovalent protein-ligand complexes. These features of NECEEM make this method an indispensable tool in studies of macromolecular interactions. They also emphasize the potential role of NECEEM in the development of extremely sensitive protein assays using nucleotide aptamers.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".