Characterizing changes in snow crab ( <i>Chionoecetes opilio</i> ) cryptocyanin protein during molting using matrix‐assisted laser desorption/ionization mass spectrometry and tandem mass spectrometry
Bibliographic record
Abstract
RATIONALE: We report the matrix-assisted laser desorption/ionization mass spectrometric (MALDI-MS) characterization of the cryptocyanin proteins of the juvenile Chionoecetes opilio crabs during their molting and non-molting phases. In order to assess the structural cryptocyanin protein differences between the molting and non-molting phases, the obtained peptides were sequenced by MALDI low-energy collision-induced dissociation tandem mass spectrometry (CID-MS/MS). METHODS: The cryptocyanin protein was isolated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and analyzed by MALDI-TOF/TOF-MS. The purified cryptocyanin protein was sequenced, using the 'bottom-up' approach. After tryptic digestion, the peptide mixture was analyzed by MALDI-QqTOF-MS/MS and the data obtained were used for the peptide mass fingerprinting (PMF) identification by means of the Mascot database. RESULTS: It was demonstrated using MALDI-TOF/TOF-MS that the actual molecular weights of the non-molting and molting cryptocyanin proteins were different; these were, respectively, 67.6 kDa and 68.1 kDa. Using low-energy CID-MS/MS we have sequenced the trytic peptides to monitor the differences and similarities between the cryptocyanin molecular structures during the molting and non-molting stages. CONCLUSIONS: We have demonstrated for the first time that the actual molecular masses of the cryptocyanin protein during the molting and non-molting phases were different. The MALDI-CID-MS/MS analyses allowed the sequencing of the cryptocyanins after tryptic digestion, during the molting and non-molting stages, and showed some similarities and staggering differences between the identified cryptocyanin peptides.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".