A systematic study of glycopeptide esterification for the semi‐quantitative determination of sialylation in antibodies
Bibliographic record
Abstract
RATIONALE: In the expression of recombinant proteins, an important parameter to control or influence is their level of sialylation. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometric (MS) methods tend to either underestimate (positive mode) or overestimate (negative mode) the content of sialylated vs. neutral glycans in glycoproteins. Esterification methods have been developed for free sialylated glycans and sialylated Asn-glycans, allowing these acidic groups to ionize with the same efficiency as neutral sugars. METHODS: Here we describe a method which modifies glycopeptides by esterification. This simple procedure is applied to glycopeptides isolated from tryptic digests of monoclonal antibodies (mAbs), some highly sialylated. To better understand the effect of esterification on the peptide backbone, synthetic EEQYNSTYR was esterified and studied by tandem mass spectrometry (MS/MS). Acetamidation of EEQYNSTYR was also studied as some mAb samples had been overalkylated prior to tryptic digestion. RESULTS: As a general trend, ethyl-esterification or lactonization is observed for each sialic acid on glycoforms of EEQYNSTYR (the N-glycosylated tryptic peptide of IgG Fc), depending on the branching position of the sialic acid (α2,3 or α2,6). Esterification also affects the carboxyl groups in the peptide, including the C-terminal COOH. CONCLUSIONS: For antibody analysis, MALDI-MS ion abundances give a better semi-quantitative estimate of sialylation levels for esterified than for unreacted glycopeptides. The method is simple to use and helps to differentiate the branching patterns of sialic acids in antibodies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".