Design and optimization of porous polymer enzymatic digestors for proteomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it