Interrogating macromolecular complex assembly by systematically analyzing the composition of highly heterogeneous structural ensembles
Why this work is in the frame
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Bibliographic record
Abstract
Cryo-EM represents a unique and powerful opportunity to structurally characterize biomolecules at the singleparticle level, and to draw biological insights from the heterogeneity observed within structural ensembles.Doing so, however, represents a significant computational challenge, and necessitates improved methods for studying extremely heterogeneous datasets.Here, we present an approach that combines our recently-published cryoDRGN method to reconstruct highly heterogeneous structural ensembles with a high-throughput compositional analysis that allows us to quantify the presence and absence of individual domains or whole proteins across hundreds-tothousands of cryo-EM density maps.This analysis produces a highly interpretable representation of the compositional heterogeneity present within a dataset.Using this representation, we can identify cooperative and mutually-exclusive occupancy relationships between various subunits, extract subsets of particles for traditional high-resolution refinement, and define pathways of structural change including complex assembly.We have applied this approach to understand the role of a universally-conserved methyltransferase in biogenesis of the 30S ribosomal subunit.By comparing the structural ensembles observed in the presence and absence of this factor, we have uncovered that this factor performs a novel proof-reading role in ribosome assembly.In sum, this work establishes a framework for systematically interrogating compositionally heterogeneous structural ensembles produced by tools such as cryoDRGN, and it highlights the value of this framework in illuminating underlying biological mechanisms.
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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 | 0.001 | 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 it