Slow-Equilibration Approximation in Kinetic Size Exclusion Chromatography
Bibliographic record
Abstract
Kinetic size exclusion chromatography with mass spectrometry detection (KSEC-MS) is a solution-based label-free approach for studying kinetics of reversible binding of a small molecule to a protein. Extraction of kinetic data from KSEC-MS chromatograms is greatly complicated by the lack of separation between the protein and protein-small molecule complex. As a result, a sophisticated time-consuming numerical approach was used for the determination of rate constants in the proof-of-principle works on KSEC-MS. Here, we suggest the first non-numerical (analytical) approach for finding rate constants of protein-small molecule interaction from KSEC-MS data. The approach is based on the slow-equilibration approximation, which is applicable to KSEC-MS chromatograms that reveal two peaks. The analysis of errors shows that the slow-equilibration approximation guarantees that the errors in the rate constants are below 20% if the ratio between the characteristic separation and equilibration times does not exceed 0.1. The latter condition can typically be satisfied for specific interactions such as receptor-ligand or protein-drug. The suggested analytical solution equips analytical scientists with a simple and fast tool for processing KSEC-MS data. Moreover, a similar approach can be potentially developed for kinetic analysis of protein-small molecule binding by other kinetic-separation methods such as nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".