Integrated Sample Processing System Involving On-Column Protein Adsorption, Sample Washing, and Enzyme Digestion for Protein Identification by LC−ESI MS/MS
Bibliographic record
Abstract
An automated system has been developed for protein identification using mass spectrometry that incorporates sample cleanup, preconcentration, and protein digestion in a single stage. The procedure involves the adsorption of a protein or a protein mixture from solution onto a hydrophobic medium that is contained within a microcolumn. The protein is digested while still bound to the hydrophobic support. The peptides are then eluted from surface digestion to an electrospray ionization mass spectrometer for detection and sequencing. The entire system is fully automated wherein the mass spectrometer is collecting data continuously. We demonstrate that this system is capable of identifying standard protein samples at concentrations down to 100 nM. Further development of this technique may offer a potential solution for proteomics applications that require unattended operation, such as on-line monitoring and identification of microorganisms on the basis of the detection of their protein biomarkers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".