Imaging and diffraction of protein crystallization using TEM
Bibliographic record
Abstract
Structural biology relies on good-quality protein crystals in order for structure determination. Many factors affect the growth process of a protein crystal including the way it nucleates and the types of damage and contamination during its growth. Although the nucleation process and quality of a crystal is vital to structure determination, they are both under-studied areas of research. Our research begins to explore ways of measuring the quality of protein crystals, using TEM, thus overcoming the problems associated with viewing wet specimens in a vacuum. Our current understanding of nucleation is that it is a two-step mechanism involving the formation of nuclei from dense liquid clusters; however; it is still unclear whether nuclei first start as amorphous aggregates or as crystalline lattices. Potentially, electron diffraction may be capable of uncovering this process. Using TEM imaging and diffraction of lysozyme as a model protein crystal, we report the internal two-dimensional strain and the density of crystallites in a protein crystal, at a resolution never seen before. The TEM diffraction shows unique features of crystal mosaicity that can be directly correlated to TEM images.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".