Microwave-Assisted Protein Solubilization for Mass Spectrometry-Based Shotgun Proteome Analysis
Bibliographic record
Abstract
Protein solubilization is a key step in mass spectrometry-based shotgun proteome analysis. We describe a microwave-assisted protein solubilization (MAPS) method to dissolve proteins in reagents, such as NH(4)HCO(3) and urea, with high efficiency and with an added benefit that the solubilized proteins are denatured to become more susceptible to trypsin digestion, compared to other conventional protein solubilization techniques. In this method, a sample vial containing proteins suspended in a solubilization reagent is placed inside a domestic microwave oven and subjected to microwave irradiation for 30 s, followed by cooling the sample on ice to room temperature (~40 s) and then intermittent homogenization by vortex for 2 min. This cycle of microwave irradiation, cooling, and homogenization is repeated six times. In this way, sample overheating can be avoided, and a maximum amount of protein can be dissolved. It was shown that in the case of trypsin digestion of bovine serum albumen (BSA) more peptides and higher sequence coverage could be obtained from the protein dissolved by the MAPS method than the conventional heating, sonication, or vortex method. Compared to the most commonly used vortex-assisted protein solubilization method, MAPS reduces the solubilization time significantly, increases the amount of protein dissolvable in a reagent, and increases the number of proteins and peptides identified from a proteome sample. For example, in the proteome analysis of an Escherichia coli K-12 integral membrane protein extract, the MAPS method in combination with sequential protein solubilization and shotgun two-dimensional liquid chromatography tandem mass spectrometry analysis identified a total of 1291 distinct proteins and 10363 peptides, compared to 1057 proteins and 6261 peptides identified using the vortex method. Because MAPS can be done using an inexpensive microwave oven, this method can be readily adopted.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".