Heat treatment of thioredoxin fusions increases the purity of α‐helical transmembrane protein constructs
Bibliographic record
Abstract
Membrane proteins play key roles in cellular signaling and transport, represent the majority of drug targets, and are implicated in many diseases. Their relevance renders them important subjects for structural, biophysical, and functional investigations. However, obtaining membrane proteins in high purities is often challenging with conventional purification steps alone. To address this issue, we present here an approach to increase the purity of α-helical transmembrane proteins. Our approach exploits the Thioredoxin (Trx) tag system, which is able to confer some of its favorable properties, such as high solubility and thermostability, to its fusion partners. Using Trx fusions of transmembrane helical hairpin constructs derived from the human cystic fibrosis transmembrane conductance regulator (CFTR) and a bacterial ATP synthase, we establish conditions for the successful implementation of the selective heat treatment procedure to increase sample purity. We further examine systematically its efficacy with respect to different incubation times and temperatures using quantitative gel electrophoresis. We find that minute-timescale heat treatment of Trx-tagged fusion constructs with temperatures ranging from 50 to 90°C increases the purity of the membrane protein samples from ~60 to 98% even after affinity purification. We show that this single-step approach is even applicable in cases where regular selective heat purification from crude extracts, as reported for Trx fusions to soluble proteins, fails. Overall, our approach is easy to integrate into existing purification strategies and provides a facile route for increasing the purity of membrane protein constructs after purification by standard chromatography approaches.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".