Manipulating trypsin digestion conditions to accelerate proteolysis and simplify digestion workflows in development of protein mass spectrometric assays for the clinical laboratory
Bibliographic record
Abstract
For ease of measurement and accurate identification of proteins by mass spectrometry, protein targets are commonly cleaved into peptides. Protein digestion is a critical step in sample preparation, yielding peptides amenable to both chromatographic separation and mass spectrometric analysis. Trypsin is the most extensively used protease due to its high cleavage specificity; however, it can yield highly variable digestion profiles and is dependent on several factors including digestion buffer, denaturant, trypsin quality selected, and composition/complexity of the sample matrix. Historically, trypsin digestion protocols have relied on lengthy digestion times-which are unsuitable for many clinical applications-to ensure effective proteolysis. Here, we performed an iterative and comprehensive evaluation of digestion conditions for five structurally diverse proteins in plasma and serum: apolipoprotein A-1, retinol-binding protein 4, transthyretin, complement component 9 and C-reactive protein. Conditions were monitored for improvements in signal intensity, reproducibility of digestion profile, and rate of release of proteolytic peptides. This approach yielded an optimized digestion protocol for detection of all five proteins in a single workflow requiring a brief 20 min digestion, without the use of chemical denaturants or reduction/alkylation steps, and only 1 μl of plasma. It is our hope that this data can accelerate the development phase of targeted mass spectrometric protein assays by identifying practical approaches to accelerate and simplify digestion protocols for clinical applications and assist with the selection of tryptic peptides for protein quantitation.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".