Capillary Electrophoresis-Based Immobilized Enzyme Microreactor Utilizing Pepsin for Bottom-Up Proteomics
Bibliographic record
Abstract
Abstract In this study, we introduced a novel method for pepsin immobilization through investigation into three different coupling reactions, aiming to simplify peptide mapping. We initially determined the optimal enzyme-to-substrate ratio to be 1:0.1 (mass:mass) for highest peptidic peak count via UV-detection. The study began with three coupling strategies, the triethoxysilylbutyraldehyde derivative, 4-triethoxysilylbutanoic acid (4-TESBA) approach proving most effective. The resultant coupled enzyme particles (CEPs) showcased robustness and fast digestion at varying temperatures. We streamlined the CEP wash procedure from our previous work, while maintaining the quality of digestions. The physical size of CEPs did not correlate to digestion efficiency, providing insights for potential cost-saving in enzyme utilization. Further optimization led to an immobilization efficiency of 50.8 ± 7.7% (SEM) as validated by Bradford’s assay. Adapting this method for in-situ immobilized enzyme microreactor (IMER) fabrication, we discovered that 4-TESBA could dual-serve by functionalizing the silica capillary’s inner wall while simultaneously acting as an enzyme coupler, plus being a safer alternative to 3-(Aminopropyl)triethoxysilane (APTES). A dipeptide was successfully fully cleaved, and a variety of proteins were digested with the IMER. The bovine serum albumin (BSA) digestion through the IMER, mirroring CEP digestion conditions, yielded a 33-40% primary sequence coverage per LC-MS/MS analysis in as short as 15 minutes. Our findings underscore the potential of our method in both CEP and IMER fabrication scenarios, paving the way for enhanced analysis and a reduction in enzyme usage, thereby contributing to more cost-effective and timely proteomic investigations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".