Analysis of Intact Monoclonal Antibody IgG1 by Electron Transfer Dissociation Orbitrap FTMS
Bibliographic record
Abstract
The primary structural information of proteins employed as biotherapeutics is essential if one wishes to understand their structure-function relationship, as well as in the rational design of new therapeutics and for quality control. Given both the large size (around 150 kDa) and the structural complexity of intact immunoglobulin G (IgG), which includes a variable number of disulfide bridges, its extensive fragmentation and subsequent sequence determination by means of tandem mass spectrometry (MS) are challenging. Here, we applied electron transfer dissociation (ETD), implemented on a hybrid Orbitrap Fourier transform mass spectrometer (FTMS), to analyze a commercial recombinant IgG in a liquid chromatography (LC)-tandem mass spectrometry (MS/MS) top-down experiment. The lack of sensitivity typically observed during the top-down MS of large proteins was addressed by averaging time-domain transients recorded in different LC-MS/MS experiments before performing Fourier transform signal processing. The results demonstrate that an improved signal-to-noise ratio, along with the higher resolution and mass accuracy provided by Orbitrap FTMS (relative to previous applications of top-down ETD-based proteomics on IgG), is essential for comprehensive analysis. Specifically, ETD on Orbitrap FTMS produced about 33% sequence coverage of an intact IgG, signifying an almost 2-fold increase in IgG sequence coverage relative to prior ETD-based analysis of intact monoclonal antibodies of a similar subclass. These results suggest the potential application of the developed methodology to other classes of large proteins and biomolecules.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".