Revealing Equilibrium and Rate Constants of Weak and Fast Noncovalent Interactions
Bibliographic record
Abstract
Rate and equilibrium constants of weak noncovalent molecular interactions are extremely difficult to measure. Here, we introduced a homogeneous approach called equilibrium capillary electrophoresis of equilibrium mixtures (ECEEM) to determine k(on), k(off), and K(d) of weak (K(d) > 1 μM) and fast kinetics (relaxation time, τ < 0.1 s) in quasi-equilibrium for multiple unlabeled ligands simultaneously in one microreactor. Conceptually, an equilibrium mixture (EM) of a ligand (L), target (T), and a complex (C) is prepared. The mixture is introduced into the beginning of a capillary reactor with aspect ratio >1000 filled with T. Afterward, differential mobility of L, T, and C along the reactor is induced by an electric field. The combination of differential mobility of reactants and their interactions leads to a change of the EM peak shape. This change is a function of rate constants, so the rate and equilibrium constants can be directly determined from the analysis of the EM peak shape (width and symmetry) and propagation pattern along the reactor. We proved experimentally the use of ECEEM for multiplex determination of kinetic parameters describing weak (3 mM > K(d) > 80 μM) and fast (0.25 s ≥ τ ≥ 0.9 ms) noncovalent interactions between four small molecule drugs (ibuprofen, S-flurbiprofen, salicylic acid and phenylbutazone) and α- and β-cyclodextrins. The affinity of the drugs was significantly higher for β-cyclodextrin than α-cyclodextrin and mostly determined by the rate constant of complex formation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".