NMR and X-ray Crystallography, Complementary Tools in Structural Proteomics of Small Proteins
Bibliographic record
Abstract
NMR spectroscopy and X-ray crystallography, the two primary experimental methods for protein structure determination at high resolution, have different advantages and disadvantages in terms of sample preparation and data collection and analysis. It is therefore of interest to assess their complementarity when applied to small proteins. Structural genomics/proteomics projects provide an ideal opportunity to make such comparisons as they generate data in a systematic manner for large enough numbers of proteins to allow firm conclusions to be drawn. Here we report a comparison for 263 unique proteins screened by both NMR spectroscopy and X-ray crystallography in our structural proteomics pipeline. Only 21 targets (8%) were deemed amenable to both methods based on an initial 2D 15N-HSQC NMR spectrum and optimized crystallization trials. However, the use of both methods in the pipeline increased the total number of targets amenable to structure determination to 107, with 43 amenable to NMR only and 43 amenable to X-ray crystallographic methods only. We did not observe a correlation between 15N-HSQC spectral quality and the success of the same protein in crystallization screens. Similar results were found for an independent set of 159 proteins as reported in the accompanying paper by Snyder et al. Thus, we conclude that both methods are highly complementary, and in order to increase the number of proteins suited for structure determination, we suggest that both methods be used in parallel in screening of all small proteins for structure determination.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".