Strategies for structural proteomics of prokaryotes: Quantifying the advantages of studying orthologous proteins and of using both NMR and X‐ray crystallography approaches
Bibliographic record
Abstract
Only about half of non-membrane-bound proteins encoded by either bacterial or archaeal genomes are soluble when expressed in Escherichia coli (Yee et al., Proc Natl Acad Sci USA 2002;99:1825-1830; Christendat et al., Prog Biophys Mol Biol 200;73:339-345). This property limits genome-scale functional and structural proteomics studies, which depend on having a recombinant, soluble version of each protein. An emerging strategy to increase the probability of deriving a soluble derivative of a protein is to study different sequence homologues of the same protein, including representatives from thermophilic organisms, based on the assumption that the stability of these proteins will facilitate structural analysis. To estimate the relative merits of this strategy, we compared the recombinant expression, solubility, and suitability for structural analysis by NMR and/or X-ray crystallography for 68 pairs of homologous proteins from E. coli and Thermotoga maritima. A sample suitable for structural studies was obtained for 62 of the 68 pairs of homologs under standardized growth and purification procedures. Fourteen (eight E. coli and six T. maritima proteins) samples generated NMR spectra of a quality suitable for structure determination and 30 (14 E. coli and 16 T. maritima proteins) samples formed crystals. Only three (one E. coli and two T. maritima proteins) samples both crystallized and had excellent NMR properties. The conclusions from this work are: (1) The inclusion of even a single ortholog of a target protein increases the number of samples for structural studies almost twofold; (2) there was no clear advantage to the use of thermophilic proteins to generate samples for structural studies; and (3) for the small proteins analyzed here, the use of both NMR and crystallography approaches almost doubled the number of samples for structural studies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".