Maximizing Cumulative Trypsin Activity with Calcium at Elevated Temperature for Enhanced Bottom-Up Proteome Analysis
Bibliographic record
Abstract
Bottom-up proteomics relies on efficient trypsin digestion ahead of MS analysis. Prior studies have suggested digestion at elevated temperature to accelerate proteolysis, showing an increase in the number of MS-identified peptides. However, improved sequence coverage may be a consequence of partial digestion, as higher temperatures destabilize and degrade the enzyme, causing enhanced activity to be short-lived. Here, we use a spectroscopic (BAEE) assay to quantify calcium-stabilized trypsin activity over the complete time course of a digestion. At 47 °C, the addition of calcium contributes a 25-fold enhancement in trypsin stability. Higher temperatures show a net decrease in cumulative trypsin activity. Through bottom-up MS analysis of a yeast proteome extract, we demonstrate that a 1 h digestion at 47 °C with 10 mM Ca2+ provides a 29% increase in the total number of peptide identifications. Simultaneously, the quantitative proportion of peptides with 1 or more missed cleavage sites was diminished in the 47 °C digestion, supporting enhanced digestion efficiency with the 1 h protocol. Trypsin specificity also improves, as seen by a drop in the quantitative abundance of semi-tryptic peptides. Our enhanced digestion protocol improves throughput for bottom-up sample preparation and validates the approach as a robust, low-cost alternative to maximized protein digestion efficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".