Structures Frozen in Time: Application of Basic Cryogenic Electron Microscopy in an Undergraduate Lab
Bibliographic record
Abstract
Cryogenic electron microscopy (cryo-EM) is a powerful technique capable of characterizing large protein complexes that are otherwise impossible to characterize using traditional crystallography methods. Cryo-EM has played a pivotal role in our understanding of the structure–function relationship of the spike proteins on the COVID-19 viral capsule, allowing for the rapid development of therapeutics and vaccines in our quest to end the COVID-19 pandemic. The relevance of cryo-EM to this global event advocates for its incorporation into undergraduate chemistry curricula. In fact, highlighting the importance and applicability of a technique to students allows for a better retention of knowledge. Therefore, in this laboratory exercise, we introduce the concepts and application of cryo-EM as a structural characterization tool in an upper-year undergraduate class. Furthermore, with the aid of three-dimensional (3D) printing, the concepts of tomography are introduced, allowing students to understand the construction of a 3D model from a collection of 2D images through cryo-EM. According to student responses, this lab module was well-received, and the 3D-printed model indeed added to their learning experience. While cryo-EM is a specialized technique that may not be available to some educational institutions, those with access should consider its incorporation into their curriculum, thereby exposing students to a breadth of structural characterization techniques that broaden their learning experience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".