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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".