Design of amyloidogenic peptide traps
Bibliographic record
Abstract
Abstract Segments of proteins with β-strand propensity can self associate to form amyloid fibrils associated with many diseases. These regions often adopt alternative structures in their folded states, or are intrinsically disordered in solution, making it difficult to generate binders or inhibitors with existing strategies. Here we describe a general approach to bind such segments in β-strand and β-hairpin conformations using de novo designed scaffolds that contain deep peptide binding clefts flanked by β-strands that form hydrogen bonds to the peptide upon binding. The designs bind their cognate peptides in vitro with nanomolar affinities and in mammalian cells. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide binding cleft is protected in the apo state. We use the approach to design binders to segments of the amyloid forming proteins Transthyretin, Tau, Serum amyloid A1 and Aβ42. The Aβ binders block assembly of Aβ fibrils as effectively as the most potent of the clinically tested antibodies to date.
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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 | 0.001 | 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".