Cryo-EM structures of artificial spider silk nanofibrils reveal insights into β sheet crystallization
Bibliographic record
Abstract
Spider silk is renowned for its exceptional mechanical properties, including its strength, toughness, and lightweight nature, making it a promising biomaterial. These properties largely arise from β sheet crystalline regions composed of poly(A) sequences, formed via liquid-liquid phase separation (LLPS). However, its nanoscale dimensions and disordered segments complicate structural characterization. In this study, we investigate an artificial spider silk protein, replacing poly(A) motifs with amyloidogenic peptides (GDVIEV) that promote β sheet formation. This replacement facilitates LLPS and the formation of nanofibrils with periodic structures resembling the β sheet conformation of natural spider silk. Using cryoelectron microscopy, we identify three different fibril polymorphs. The GDVIEV peptides predominantly adopt β sheet structures in the nanofibril core, stabilized by hydrogen bonds and hydrophobic interactions. These findings enhance our understanding of the self-assembly mechanisms and structural organization of spider silk, providing valuable insights for the development of biomimetic silk materials and the design of artificial proteins.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".