Nonequilibrium Capillary Electrophoresis of Equilibrium Mixtures (NECEEM)-Enabled Mechanistic Analysis of Cooperative Binding: Application to C-Reactive Protein–SOMAmer Complexes
Bibliographic record
Abstract
Understanding cooperative binding is essential for characterizing interactions between multimeric proteins and their ligands, because biological function often depends on binding stoichiometry or allosteric regulation. Detailed characterization of cooperativity, in turn, requires determination of the equilibrium dissociation constants for each binding step ( K d1, K d2,...). However, most experimental methods rely on ensemble-averaged signals that cannot resolve coexisting complexes, forcing stepwise constants to be inferred from model-dependent fits that cannot be validated with ensemble data alone. To date, direct (model-independent) determinations of these constants have been reported only with spectral-resolution techniques such as native MS and slow-exchange NMR; no physical-separation method has yet delivered K d1, K d2, etc. Here, we present a nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM)-based approach that physically resolves and quantifies stoichiometric complexes formed at equilibrium. A protein and its ligand are pre-equilibrated in solution, and the resulting complexes of different stoichiometries are separated from one another and from free ligand according to their electrophoretic mobilities. Quantitative peak analysis yields the equilibrium fractions of each species, providing step-resolved thermodynamic data from which individual K d values are obtained directly, without global model fitting; their relative magnitudes reveal the presence and extent of cooperativity. As proof of concept, we studied the interaction between C-reactive protein (CRP), a homopentameric acute-phase protein of the innate immune system, and a slow off-rate modified aptamer (SOMAmer). The electropherograms resolved and quantified free ligand as well as 1:1 and 2:1 SOMAmer–CRP complexes, allowing determination of the 95% accuracy confidence intervals (ACI) for the first two dissociation constants: K d1 = 3.0–7.8 nM and K d2 = 41–160 nM, consistent with strong negative cooperativity. At high ligand-to-target ratios, 3:1 SOMAmer–CRP complex was also detected, but its peak could not be baseline-resolved, precluding reliable determination of K d3 . This study establishes NECEEM as the first solution-phase physical-separation method capable of directly quantifying stepwise affinities and dissecting cooperativity in multivalent systems.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".