Refolding out of guanidine hydrochloride is an effective approach for high‐throughput structural studies of small proteins
Bibliographic record
Abstract
Abstract Low in vivo solubility of recombinant proteins expressed in Escherichia coli can seriously hinder the purification of structural samples for large‐scale proteomic NMR and X‐ray crystallography studies. Previous results from our laboratory have shown that up to one half of all bacterial and archaeal proteins are insoluble when overexpressed in E. coli. Although a number of strategies may be used to increase in vivo protein solubility, there are no generally applicable methods, and the expression of each insoluble recombinant protein must be individually optimized. For this reason, we have tested a generic denaturation/refolding protein purification procedure to assess the number of structural samples that could be generated by using this methodology. Our results show that a denaturation/refolding protocol is appropriate for many small proteins (≤18 kD) that are normally soluble in vivo. In addition, refolding the purified proteins by using dialysis against a single buffer allowed us to obtain soluble protein samples of 58% of small proteins that were found in the insoluble fraction in vivo, and 10% of the initial number of proteins provided good heteronuclear single quantum coherence (HSQC) NMR spectra. We conclude that a denaturation/refolding protocol is an efficient way to generate structural samples for high‐throughput studies of small proteins.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".