In-Gel Microwave-Assisted Acid Hydrolysis of Proteins Combined with Liquid Chromatography Tandem Mass Spectrometry for Mapping Protein Sequences
Bibliographic record
Abstract
We report an enabling method for mapping the protein sequence with high sequence coverage. This method combines the high separation power of gel electrophoresis for protein separation with the high sequence coverage capability of microwave-assisted acid hydrolysis (MAAH) mass spectrometry (MS). In-gel MAAH using 25% trifluoroacetic acid was developed and optimized for degrading the gel-separated protein into small peptides suitable for tandem MS sequencing. For bovine serum albumin (BSA) (∼67 kDa), with 4 μg of protein loading onto a gel for separation, followed by excising the protein gel band for in-gel MAAH and then injecting ∼2 μg of the resultant peptides into a liquid chromatography quadrupole time-of-flight mass spectrometer for analysis, 689 ± 54 (n = 3) unique peptides were identified with a protein sequence coverage of 99 ± 1%. Both the number of peptides detected and sequence coverage decreased as the sample amount decreased, mainly due to background interference: 316 ± 59 peptides and 94 ± 3% coverage for 2 μg loading, 136 ± 19 and 76 ± 5% for 1 μg loading, and 30 ± 2 and 32 ± 2% for 0.5 μg loading. To demonstrate the general applicability of the method, 10 gel bands from gel electrophoresis of an albumin-depleted human plasma sample were excised for in-gel MAAH LC-MS analysis. In total, 19 relatively high abundance proteins with molecular weights ranging from ∼8 to ∼160 kD could be mapped with coverage of 100% for six proteins (MW 8759 to 68 425 Da), 96-98% for five proteins (MW 11 458 to 36 431 Da), 92% for three proteins (MW 15 971 to 36 431 Da), 80-87% for four proteins (MW 42 287 to 162 134 Da), and 56% for one protein (MW 51 358 Da). Finally, to demonstrate the applicability of the method for more detailed analysis of complex protein mixtures, two-dimensional (2D) gel electrophoresis was combined with in-gel MAAH, affinity purification, and LC-MS/MS to characterize six bovine alpha-S1-casein phosphoprotein isoforms. Full sequence coverage was achieved for each protein, and six new modification sites were found.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".