High Resolution Structural Analysis Of ALS‐Associated Mutant SOD1 Inclusion Bodies
Bibliographic record
Abstract
Inclusion bodies (IBs) – insoluble structures that can form upon overexpression of proteins‐ have wide relevance in research, industrial settings, and disease. In addition to their use in protein purification, IBs provide a tractable model to elucidate the molecular mechanisms of protein aggregation in cells, as also occurs in neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Parkinson's, and Alzheimer's diseases. The aggregation of Cu, Zn superoxide dismutase (SOD1) variants is associated with ALS, yet the relationships between mutant characteristics and disease properties remain obscure. Here, we report a systematic investigation of SOD1 IBs using two powerful complementary methods to analyze the structures of these cellular aggregates and their changes upon mutation. Quenched amide hydrogen‐deuterium exchange (HDX) measurements by NMR for individual residues throughout SOD1 reveal IB structural features and similarities, whereas quantitative conformation specific antibody binding assays highlight notable differences in surface features between mutant IBs. Taken together, these data provide a valuable new high‐resolution view of cellular aggregation. These powerful HDX and quantitative antibody binding methods are widely applicable to other proteins to define the molecular determinants of their aggregation and solubility.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".