Mix-it-up: Accessible time-resolved cryo-EM on the millisecond timescale
Bibliographic record
Abstract
Biological reactions often involve macromolecules that undergo substrate-induced conformational changes in under a second, yet capturing these transient states remains challenging. While high-resolution structural techniques such as X-ray crystallography and cryo-electron microscopy (cryo-EM) have advanced our mechanistic understanding of protein-substrate interactions, traditional sample preparation methods are too slow to capture rapid biochemical events. Time-resolved cryo-EM has emerged as a promising approach to visualize structural dynamics on microsecond-to-millisecond timescales, but its widespread adoption has been limited by costly equipment and challenges in achieving rapid mixing, application, and vitrification of samples in a reproducible manner. To address these limitations, we developed "Mix-it-up" (MIU), a modified spray device designed for rapid on-grid mixing and vitrification of cryo-EM samples. By manually applying one sample onto the EM grid, blotting, and subsequently spraying the second sample, we achieve on-grid mixing with a vitrification delay of as low as ~120 milliseconds. We demonstrate MIU's time-resolved capabilities through high-resolution structure determination of mixed samples, pH-induced viral capsid contraction, and ligand-dependent complex formation. These findings establish MIU as a cost-effective, versatile tool for studying rapid biochemical processes and lay the groundwork for future applications to time-resolved cryo-EM.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".