Development of novel enzyme immobilization methods employing formaldehyde or triethoxysilylbutyraldehyde to fabricate immobilized enzyme microreactors for peptide mapping
Bibliographic record
Abstract
The digestion of proteins with proteolytic enzymes has expedited the analysis of peptide mapping. Here, we compared the digestion efficiency of soluble chymotrypsin (CT) with two immobilized CT preparations using bovine serum albumin (BSA) as the substrate. An efficient method of immobilizing chymotrypsin using formaldehyde (FA) was optimized and the conditions were applied to assess a novel immobilization reagent, triethoxysilylbutaraldehyde (TESB). Efforts to determine the best enzyme-to-substrate (E : S) ratios during digestion of denatured BSA with single-use FA-CT enzyme particles were performed by adjusting the amount of substrate used. An E : S ratio of 10 : 1 was found to be best based on the LC-MS/MS analysis data showing sequence coverage of 67%. Fabrication of immobilized enzyme microreactors (IMERs) was carried out using both (3-aminopropyl)triethoxysilane (APTES) with the idealized conditions with FA, as well as the novel procedure utilizing TESB for a proof of concept open-tubular IMER. It was found that the FA-APTES IMER had a sequence coverage of 6%, while the TESB IMER had 29% sequence coverage from MS analysis. The application of TESB in enzyme immobilization has the potential to facilitate a greater degree of enzymatic digestion with higher sequence coverage than traditional immobilization or crosslinking reagents for bottom-up proteomics.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".