Strategies for the production of isotopically labelled Fab fragments of therapeutic antibodies in Komagataella phaffii (Pichia pastoris) and Escherichia coli for NMR studies
Bibliographic record
Abstract
The importance and fast growth of therapeutic monoclonal antibodies, both innovator and biosimilar products, have triggered the need for the development of characterization methods at high resolution such as nuclear magnetic resonance (NMR) spectroscopy. However, the full power of NMR spectroscopy cannot be unleashed without labelling the mAb of interest with NMR-active isotopes. Here, we present strategies using either Komagataella phaffii (Pichia pastoris) or Escherichia coli that can be widely applied for the production of the antigen-binding fragment (Fab) of therapeutic antibodies of immunoglobulin G1 kappa isotype. The E. coli approach consists of expressing Fab fragments as a single polypeptide chain with a cleavable linker between the heavy and light chain in inclusion bodies, while K. phaffii secretes a properly folded fragment in the culture media. After optimization, the protocol yielded 10-45 mg of single chain adalimumab-Fab, trastuzumab-Fab, rituximab-Fab, and NISTmAb-Fab per liter of culture. Comparison of the 2D-1H-15N-HSQC spectra of each Fab fragment, without their polyhistidine tag and linker, with the corresponding Fab from the innovator product showed that all four fragments have folded into the correct conformation. Production of 2H-13C-15N-adalimumab-scFab and 2H-13C-15N-trastuzumab-scFab (>98% enrichment for all three isotopes) yielded NMR samples where all amide deuterons have completely exchanged back to proton during the refolding procedure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".