Methodological advances and strategies for high resolution structure determination of cellular protein aggregates
Bibliographic record
Abstract
Aggregation of proteins is at the nexus of molecular processes crucial to aging, disease, and employing proteins for biotechnology and medical applications. There has been much recent progress in determining the structural features of protein aggregates that form in cells; yet, owing to prevalent heterogeneity in aggregation, many aspects remain obscure and often experimentally intractable to define. Here, we review recent results of structural studies for cell-derived aggregates of normally globular proteins, with a focus on high-resolution methods for their analysis and prediction. Complementary results obtained by solid-state NMR spectroscopy, FTIR spectroscopy and microspectroscopy, cryo-EM, and amide hydrogen/deuterium exchange measured by NMR and mass spectrometry, applied to bacterial inclusion bodies and disease inclusions, are uncovering novel information on in-cell aggregation patterns as well as great diversity in the structural features of useful and aberrant protein aggregates. Using these advances as a guide, this review aims to advise the reader on which combination of approaches may be the most appropriate to apply to their unique system.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".