Examining Protein Crystallization Using Scanning Electron Microscopy
Bibliographic record
Abstract
Elucidation of protein structure using X-ray crystallography relies on the quality of the crystal. Crystals suffer from many different types of disorder, some of which occur during crystal nucleation and early crystal growth. To date, there are few studies surrounding the quality and nucleation of protein crystals partly due to difficulties surrounding viewing biological samples at high resolution. Recent research has led our current understanding of nucleation to be a two-step mechanism involving the formation of nuclei from dense liquid clusters; it is still unclear whether nuclei first start as amorphous aggregate or as crystalline lattices. Our research examines this mechanism through the use of electron microscopy. Using scanning electron microscopy imaging of the protein crystal growth process, a stacking, spiraling manner of growth is observed. The tops of the pyramid-like tetragonal protein crystal structures measure ~0.2 μm across and contain ~125,000 lysozyme units. This noncrystalline area experiences strain due to growth of the protein crystal. Our work shows that it is possible to view detailed early stage protein crystal growth using a wet scanning electron microscopy technique, thereby overcoming the problem of viewing liquids in a vacuum.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".