Kinetic Size-Exclusion Chromatography with Mass Spectrometry Detection: An Approach for Solution-Based Label-Free Kinetic Analysis of Protein–Small Molecule Interactions
Bibliographic record
Abstract
Studying the kinetics of reversible protein-small molecule binding is a major challenge. The available approaches require that either the small molecule or the protein be modified by labeling or immobilization on a surface. Not only can such modifications be difficult to do but also they can drastically affect the kinetic parameters of the interaction. To solve this problem, we present kinetic size-exclusion chromatography with mass spectrometry detection (KSEC-MS), a solution-based label-free approach. KSEC-MS utilizes the ability of size-exclusion chromatography (SEC) to separate any small molecule from any protein-small molecule complex without immobilization and the ability of mass spectrometry (MS) to detect a small molecule without a label. The rate constants of complex formation and dissociation are deconvoluted from the temporal pattern of small molecule elution measured with MS at the exit from the SEC column. This work describes the concept of KSEC-MS and proves it in principle by measuring the rate constants of interaction between carbonic anhydrase and acetazolamide.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".