Determination of the degree of PEGylation of protein bioconjugates using data from proton nuclear magnetic resonance spectroscopy
Bibliographic record
Abstract
The average number of methoxy poly(ethylene glycol) (mPEG) chains grafted to a protein – also known as the degree of PEGylation – is a fundamental parameter for characterizing a bioconjugate. The degree of PEGylation is typically determined by chromatographic or electrophoretic methods, which are subject to certain biases. This contribution describes an analytical approach alongside technical precautions for quantitatively determining the degree of PEGylation of protein bioconjugates by 1 H NMR spectroscopy. An accompanying dataset, corresponding to the raw 1 H NMR spectra of thirteen bioconjugates with different degrees of PEGylation and different mPEG molecular weights, is provided for the reader to become familiar with the analysis. The exemplary bioconjugate system used in this Data article is the enzyme glutamate dehydrogenase (GDH) modified with multiple copies of mPEG (0.5–20 kDa). These bioconjugates correspond to those discussed in-depth in the article “Mechanisms of activity loss for a multi-PEGylated protein by experiment and simulation” by Zaghmi et al., 2019 The described approach to calculate degree of PEGylation is quantitative, applicable to other proteins, and can be adapted to other types of polymers.
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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 | 0.001 |
| 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) | 0.001 | 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".