Unveiling the Structural Proteome of an Alzheimer’s Disease Rat Brain Model
Bibliographic record
Abstract
Abstract Studying native protein structures at near-atomic resolution in crowded environment presents a challenge. Consequently, understanding the structural intricacies of proteins within pathologically affected tissues often relies on mass spectrometry and proteomic analysis. In this study, we utilized electron cryomicroscopy (cryo-EM) and a specific method of analysis called Build and Retrieve (BaR) to investigate structural characteristics of protein complexes such as post-translational modification, active site occupancy, and arrested conformational state in Alzheimer’s Disease (AD) using brain lysate from a rat model (TgF344-AD) of the disease. Our findings reveal novel insights into the architecture of these complexes, which we corroborate through mass spectrometry analysis. Interestingly, it has been shown that the dysfunction of these protein complexes extends beyond AD, implicating them in cancer, as well as other neurodegenerative disorders such as Parkinson’s disease, Huntington’s disease, and Schizophrenia. By elucidating the structural details of these complexes, our work not only enhances our understanding of disease pathology but also suggests new avenues for future approaches in therapeutic intervention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".