Interrogating macromolecular complex assembly by systematically analyzing the composition of highly heterogeneous structural ensembles
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".