Slow-equilibration approximation in studying kinetics of protein adsorption on capillary walls
Bibliographic record
Abstract
Adsorption of proteins on inner capillary walls affects the quality of capillary electrophoresis (CE) analyses. Coating the capillary surface with an anti-adhesive layer is a method typically used to suppress protein adsorption. The successful development of methods for prevention of protein adsorption requires quantitative characterization of the surface ability to adsorb and desorb a protein. It can be done by determining kinetic rate constants of adsorption, kad, and desorption, kdes. We have recently developed a pattern-based method for determination of kad and kdes for protein interaction with a capillary wall. The protein is moved through the capillary in a CE instrument by pressure and a temporal pattern of protein propagation through the detector is recorded. The experimental pattern is fitted with a numerical solution of the protein mass transfer to find kad and kdes. The fitting procedure is not "transparent" and can be complicated. In the present work, we obtained approximate analytical solutions of the protein mass transfer equations in the case of slow-equilibration during adsorption and desorption of the protein. These analytical solutions allow us to introduce a fitting-free parameter based method for determination of kad and kdes. It uses simple explicit expressions for kad and kdes in terms of experimental characteristics easily measured in capillaries. We tested the accuracy of the method by applying it to signals simulated with numerical solutions of protein mass-transfer equations. For the slow equilibration approximation the accuracy of kad and kdes was better than 12%.
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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 | 0.000 |
| 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".