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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".