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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".