Localized reconstruction in Scipion expedites the analysis of symmetry mismatches in cryo-EM data
Bibliographic record
Abstract
Technological advances in transmission electron microscopes and detectors have turned cryogenic electron microscopy (cryo-EM) into an essential tool for structural biology. A commonly used cryo-EM data analysis method, single particle analysis, averages hundreds of thousands of low-dose images of individual macromolecular complexes to determine a density map of the complex. The presence of symmetry in the complex is beneficial since each projection image can be assigned to multiple views of the complex. However, data processing that applies symmetry can average out asymmetric features and consequently data analysis methods are required to resolve asymmetric structural features. Scipion is a cryo-EM image processing framework that integrates functions from different image processing packages as plugins. To extend its functionality for handling symmetry mismatches, we present here a Scipion plugin termed LocalRec implementing the localized reconstruction method. When tested on an adenovirus data set, the plugin enables resolving the symmetry-mismatched trimeric fibre bound to the five-fold vertices of the capsid. Furthermore, it improves the structure determination of the icosahedral capsid by dealing with the defocus gradient across the particle. LocalRec is expected to be widely applicable in a range of cryo-EM investigations of flexible and symmetry mismatched complexes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".