Pressure-Based Approach for the Analysis of Protein Adsorption in Capillary Electrophoresis
Bibliographic record
Abstract
Protein adsorption to inner capillary walls creates a major obstacle in all applications of capillary electrophoresis involving protein samples. The problem is especially severe in kinetic capillary electrophoresis (KCE) techniques, which are used to study protein-ligand interactions at physiological conditions and, thus, cannot utilize extreme pH. A variety of coatings exist to reduce protein adsorption in CE, each expressing a unique surface chemistry that interacts with individual proteins differently. Here we introduce a simple pressure-based method for the qualitative assessment of protein adsorption that can facilitate the direct antiadhesive ranking of several coatings toward a protein of interest. In this approach, a short plug of the protein is injected into a capillary and propagated through with a pressure low enough to ensure adequate Taylor dispersion. The experiment is performed with a nonmodified commercial instrument in a pseudo-two-detector approach. The two detectors are mimicked by using two different distances from the capillary inlet to a single detector. If the peak area and shape do not change with changing distance, the protein does not adsorb appreciably, while a decreasing peak area with increasing distance infers inner surface adsorption. The magnitude change of the peak area between the two distances along with the overall peak shape is used to gauge the extent of protein adsorption. By using this method, we ranked antiadhesive properties of different wall chemistries for a series of proteins. The described method will be useful for optimizing protein analysis by CE and, in particular, for KCE experiments that investigate how proteins interact with their respective ligands.
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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) | 0.000 | 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 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".