Identification of Trichloroethanol Visualized Proteins from Two-Dimensional Polyacrylamide Gels by Mass Spectrometry
Bibliographic record
Abstract
Proteins visualized by 2,2,2-trichloroethanol (TCE) on two-dimensional electrophoresis gels are efficiently identified by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) and MS/MS. In a previous study, a method was developed that placed TCE in the polyacrylamide gel so that protein bands can be visualized without staining in less than 5 min. A visible fluorophore is generated by reaction of TCE with tryptophan that allows for protein visualization. In this study, MALDI-TOF MS and LC-MS/MS are used to identify randomly selected Escherichia coli proteins. The identification of TCE visualized proteins is compared to the identification of Coomassie brilliant blue (CBB) stained proteins from two-dimensional gel electrophoresis of E. coli proteins. This study demonstrated that TCE visualized proteins are compatible with protein identification by MALDI-TOF peptide mass fingerprinting. For 10 randomly selected spots, TCE visualization lead to statistically significant identification of 5 proteins and CBB visualization lead to identification of 6 proteins. TCE visualized proteins are also shown to be well suited for protein identification using LC-MS/MS. In 16 spots selected for MS/MS analysis, TCE samples lead to the identification of 79 peptides; while CBB samples lead to the identification of 65 peptides. TCE samples also supported the identification of more proteins. The low stoichiometry of labeling of tryptophan residues does not require inclusion of this modification for database searches. In addition to being a rapid visualization technique compatible with MS, TCE visualization utilizes rapid washing conditions for sample preparation of proteins spots excised from polyacrylamide gels.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".