Improved Protocol for the Production of the Low-Expression Eukaryotic Membrane Protein Human Aquaporin 2 in Pichia pastoris for Solid-State NMR
Bibliographic record
Abstract
Solid-state nuclear magnetic resonance (SSNMR) is a powerful biophysical technique for studies of membrane proteins; it requires the incorporation of isotopic labels into the sample. This is usually accomplished through over-expression of the protein of interest in a prokaryotic or eukaryotic host in minimal media, wherein all (or some) carbon and nitrogen sources are isotopically labeled. In order to obtain multi-dimensional NMR spectra with adequate signal-to-noise ratios suitable for in-depth analysis, one requires high yields of homogeneously structured protein. Some membrane proteins, such as human aquaporin 2 (hAQP2), exhibit poor expression, which can make producing a sample for SSNMR in an economic fashion extremely difficult, as growth in minimal media adds additional strain on expression hosts. We have developed an optimized growth protocol for eukaryotic membrane proteins in the methylotrophic yeast Pichia pastoris. Our new growth protocol uses the combination of sorbitol supplementation, higher cell density, and low temperature induction (LT-SEVIN), which increases the yield of full-length, isotopically labeled hAQP2 ten-fold. Combining mass spectrometry and SSNMR, we were able to determine the nature and the extent of post-translational modifications of the protein. The resultant protein can be functionally reconstituted into lipids and yields excellent resolution and spectral coverage when analyzed by two-dimensional SSNMR spectroscopy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".