Design and optimization of porous polymer enzymatic digestors for proteomics
Bibliographic record
Abstract
Effective protein characterization and identification are demanding and time-consuming operations in proteomics because of long-protein purification/separation procedures, and even longer enzymatic digestions. In this work, polymer-based monolithic enzyme reactors were fabricated in fused-silica capillaries, and performance was characterized through protein digestion and identification by MALDI-MS and ESI-MS. Reactors were prepared by fabricating a porous methacrylate base monolith followed by photografting with glycidyl methacrylate, and immobilization of the enzyme(s) with carbonyldiimidazole. Trypsin and Staphylococcus aureus V-8 protease (Glu-C) were used to produce three types of reactors: trypsin-based, Glu-C-based, and trypsin combined with Glu-C. Protein digestions, performed by perfusing protein solutions through the reactor under pressure, were evaluated based on the peptide map generated when directly coupled to an ESI mass spectrometer. Excellent digestion was observed over flow rates from 0.2 to 1 microL/min, which corresponds to reactor residence times of 0.24-1.4 min. As a proof of principle, chromatographic separation of model proteins followed by the digestion of specific fractions using these proteolytic enzyme reactors and ESI-MS is demonstrated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".