Liquid chromatography–tandem mass spectrometry glycoproteomic study of porcine IgG and detection of subtypes
Bibliographic record
Abstract
RATIONALE: While high-throughput proteomic methods have been widely applied to monoclonal antibodies and human immunoglobulin gamma (IgG) samples, less information is available on porcine IgG. As pigs are considered one of the most suitable species for xenotransplantation, it is important to characterize IgG amino acid sequences and glycosylation profiles, which is the focus of this study. METHODS: Three different purified porcine IgG samples, including wild-type and knockout species, were digested with trypsin and enriched for glycopeptides. Digestion mixtures were spiked with a mixture of six standard peptides. Analysis was performed using electrospray ionization liquid chromatography-tandem mass spectrometry (MS/MS) in standard MS/MS data-dependent acquisition mode on a hybrid triple quadrupole time-of-flight mass spectrometer. RESULTS: To facilitate the classification of subtypes detected experimentally, UniprotKB database entries were organized using comparative alignment scores. Sequences were grouped based on 11 different subtypes as translated from GenBank entries. Proteomic searches were accomplished automatically using specialized software, whereas glycoprotein searches were performed manually by monitoring the extracted chromatograms of diagnostic MS/MS glycan fragments and studying their corresponding mass spectra; 40-50 non-glycosylated peptides and 4-5 glycosylated peptides were detected in each sample, with several glycoforms per sequence. CONCLUSIONS: Proteomic analysis of porcine IgG is complicated by factors such as the presence of several subtypes, redundant heavy chain (HC) sequences in protein databases, and the lack of consistent cross-referencing between databases. Aligning and comparing HC sequences were necessary to eliminate redundancy. This study highlights the complexity of pig IgG and shows the importance of MS in proteomics and glycoproteomics.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".