Capillary Electrophoretic Peptide Mapping to Probe the Immobilization/ Digestion Conditions of Glutaraldehyde-crosslinked Chymotrypsin
Bibliographic record
Abstract
The use of immobilized enzymes in proteomics studies has increased over the last decade not only due to their ease of use in fluidic systems but also because autoproteolysis is suppressed allowing use of high ratios of enzyme-to-substrate for rapid digestion. Chymotrypsin immobilization by crosslinking with excess aqueous glutaraldehyde (GA) has been used to produce a gel-like agglomerated pellet of high enzyme loading capable of digesting protein. The simple procedure involved dropwise addition of 80 L GA (2.5% in water) to 80 L chymotrypsin in pH 6.4 phosphate buffer, reaction for 2 h at room temperature without stirring, washing with buffer and NaCl, then deactivation of any free aldehyde groups by reaction with 200 mM glycine for 3 h. The enzyme was ready for use after washing with buffer and water. The efficiency of the immobilized enzyme for protein digestion was followed by capillary electrophoretic (CE) peptide mapping with absorbance detection at 200 nm. A study comparing preparation methods showed that making a small immobilized enzyme pellet leads to better digestions (i.e., peptide maps) compared to using a five-fold larger pellet or a part of the latter. When three different buffers for the chymotrypsin crosslinking reaction were compared, sodium phosphate at pH 6.4 showed better batch-to-batch reproducibility and activity of the immobilized enzyme: overall peak area RSD was 27% versus 39% and 43% for the other pHs (with 11% of this variability due to the CE method) and up to 3 times more peptide peaks were detected. The immobilized chymotrypsin pellet could be used for two consecutive digestions of denatured BSA and was able to partially digest native BSA. Keywords: Capillary electrophoresis, chymotrypsin, glutaraldehyde, immobilized enzymes, peptide mapping.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".