Protein mass measurement combined with mass spectrometric sequencing of protein digests for detection and characterization of protein modifications<sup>1</sup>
Bibliographic record
Abstract
A method for the characterization of modifications of low molecular weight proteins (<20 kDa) extracted from a microorganism based on the use of multiple separation tools and mass spectrometric techniques is described. In this method, intact proteins from cell extracts are first separated and fractionated by liquid chromatography (LC). Individual fractions are then analyzed by matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) to provide intact protein mass information. The fractions are further characterized by using trypsin digestion and LC electrospray ionization (ESI) MS/MS analysis of the resultant peptides to identify the proteins. Gel electrophoresis of a fraction is also carried out to estimate the molecular masses of the proteins. The gel bands are identified by in-gel digestion and peptide mass mapping and sequencing using MALDI-MS and MALDI-MS/MS. The combined information generated from these experiments is interpreted for detecting and characterizing modified proteins. This method has been developed and applied to the analysis of posttranslational modifications (PTMs) of low-mass proteins (5–20 kDa) extracted from a relatively well-characterized microorganism, Escherichia coli. Using this method, not only previously reported PTMs involving acetylation, methylation, oxidation, and the removal of signal peptides, but also two novel PTMs, namely loss of N-terminal Met-Thr-Met (MTM) and hydroxylation of arginine, were identified. It is envisaged that this method should be applicable to other relatively simple microorganisms for the discovery of new PTMs.Key words: top-down proteomics, protein modification, HPLC, gel electrophoresis, tandem mass spectrometry.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".