Pros and cons of cryocrystallography: should we also collect a room-temperature data set?
Bibliographic record
Abstract
High-resolution protein structures are becoming more common owing to the availability of increasingly brilliant synchrotron X-ray sources. However, to withstand the increased X-ray dose the crystals must be held at cryogenic temperatures. To compare the benefit of increased resolution with the drawback of potential temperature-induced changes, three room-temperature and three cryogenic data sets for PAK pilin have been collected at resolutions between 1.8 and 0.78 A. The results show that although the high-resolution cryogenic structures are more precise and more detailed, they also show systematic deviations from the room-temperature structures. Small but significant differences are even observed in the structural core, whilst more extensive changes occur at the protein surface. These differences can affect biological interpretations, especially because many important biological processes take place at the protein surface. Accordingly, although high-quality cryogenic synchrotron data is extremely valuable to protein crystallography, room-temperature structures are still desirable, especially if the research question involves protein features that are sensitive to temperature-induced changes.
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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 | 0.036 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".