Expression of <sup>2</sup>H, <sup>13</sup>C, <sup>15</sup>N-labeled NIST-Fab fragment in the methylotrophic yeast <i>Komagataella phaffii</i> for nuclear magnetic resonance studies
Bibliographic record
Abstract
Labeling of proteins with deuterium is an essential tool in overcoming size limitations in the application of nuclear magnetic resonance (NMR) spectroscopy to proteins larger than 30 kilodaltons (kDa). A non-originator antigen-binding fragment (Fab) of NIST RM 8671 NISTmAb, so called yNIST-Fab, is a ~ 50 kDa protein, with 5 native disulfide linkages, that can be expressed in properly folded form in methylotrophic Komagataella phaffii (formerly Pichia pastoris). Further, the K. phaffii host can support the production of perdeuterated yNIST-Fab which is necessary to obtain well-resolved TROSY-based tripleresonance NMR spectra for chemical shift assignment of the peptide backbone resonances. Here, we examined growth conditions and effects of media composition to maximize biomass generation and expression yield of the 2H, 13C, 15N-enriched NIST-Fab fragment. Triple-labeled yNIST-Fab with ~93% deuteration reduced the 1HN, 15N and 13C-linewidths in the NMR spectra, allowing sequential NMR assignment of backbone resonance a key step toward sequence-specific structural and dynamic studies of Fab fragments and intact antibodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".