Molecular Mechanisms of Fibrillogenesis and the Protective Role of Amyloid P Component: Two Possible Avenues for Therapy
Bibliographic record
Abstract
Amyloid deposits regress when the supply of fibril precursor proteins is sufficiently reduced, indicating that amyloid fibrils are degradable in vivo. Serum amyloid P component (SAP), a universal constituent of amyloid deposits, efficiently protects amyloid fibrils from proteolysis in vitro, and may contribute to persistence of amyloid in vivo. Drugs that prevent binding of SAP to amyloid fibrils in vivo should therefore promote regression of amyloid and we are actively seeking such agents. A complementary strategy is identification of critical molecular processes in fibrillogenesis as targets for pharmacological intervention. All amyloidogenic variants of apolipoprotein AI contain an additional positive charge in the N-terminal fibrillogenic region of the protein. This is unlikely to be a coincidence and should be informative about amyloidogenesis by this protein. The two amyloidogenic variants of human lysozyme, caused by the first natural mutations found in its gene, provide a particularly powerful model system because both the crystal structure and folding pathways of wild-type lysozyme are so well characterized. The amyloidogenic variant lysozymes have similar 3D crystal structures to the wild type, but are notably less thermostable. They unfold on heating, lose enzymic activity, and aggregate to form amyloid fibrils in vitro.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".