Electron diffraction captures high-resolution structures from <i>in vivo</i> protein nanocrystals of <i>Bacillus thuringiensis</i>
Bibliographic record
Abstract
Abstract Bacillus thuringiensis is one of the most widely used biopesticides worldwide. This is owing to the highly-specific pesticidal-proteins various strains produce in the form of nanocrystals. Structure determination from such crystals remains difficult because their small size makes them unsuitable for conventional X-ray crystallography. Here we explore two emerging (cryo-) electron diffraction techniques, namely Microcrystal electron diffraction and serial electron diffraction, as tools for studying the structures of these crystals. Using the mosquitocidal protein Cry11Aa as an example, we compare electron diffraction with state of the art results obtained with an X-ray free electron laser. Our work demonstrates that electron diffraction is a viable alternative for structure determination from such challenging crystals, matching previous results obtained with X-ray free electron lasers. We present a workflow based on readily available instrumentation enabling structure determination directly from the crystals grown in vivo , unperturbed by dissolution and therefore preserved in their native state.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".