Integrative Structural Biology to Understand Biological Complexity
Bibliographic record
Abstract
Cryogenic electron microscopy (cryoEM) has been advanced to resolve atomic structures of biochemically purified macromolecules with details equivalent to X-ray crystal structures.A unique aspect of cryoEM is to use image processing methods to sort out images of particles with heterogeneous compositions and conformations.These capabilities have been demonstrated across a spectrum of macromolecules including viruses, membrane channels, protein folding machines with and without substrates, and RNA.The reliability of these structures can be assured by using rigorous criteria that the atomic model obeys expected stereochemistry of the molecules and simultaneously matches well with the experimentally observed density maps.The resolvability of the cryoEM structures have been shown in many cases to be sufficiently good to resolve carbohydrates, lipids, ligands, ions and water molecules that are critical to understand the chemical basis of their tertiary structures and their functions involving conformational variations.We will present examples of integrating cryoEM, mass spectrometry and biological assays to guide interpretation of cryoEM structures in terms of their contents and functions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".