Tyrosine Templating in the Self-Assembly and Crystallization of Silk Fibroin
Bibliographic record
Abstract
Native silk fibers exhibit strength and toughness that rival those of the best synthetic fibers. Despite significant research, further insight is still needed to understand the mechanisms by which silkworms are capable of spinning such tough fibers. Here we propose that π-π and π-OH group interactions of tyrosine side chains provide templating effects, such that the crystal-forming domains are in registration, thereby fostering the self-assembly of the spinning dope. Intrinsic fluorescence measurements, in conjunction with circular dichroism, showed that during self-assembly of regenerated silk solutions, the tyrosine residues were localized in a more hydrophobic local environment, suggesting preferential assembly. In situ Fourier transform infrared spectroscopy indicated that cross-linking of the tyrosine residues resulted in the development of extended β-sheet structure. Additionally, control of cross-link density directly influenced the degree of crystallinity upon drying. Molecular dynamics simulations were performed on silk mimetic peptides in order to more thoroughly understand the role of tyrosines. The results indicated that tyrosine residues tended to transiently colocate in solution due to π-π interactions and hydrogen bonds with adjacent residues and with the peptide backbone. These more stable tyrosine interactions resulted in reduced lateral chain fluctuations and increased incidence of coordinated intrachain association, while introduction of a dityrosine bond directly promoted the formation of β-sheet structures. In total, the experimental and modeling data support a critical role for tyrosine-tyrosine interactions as a key early feature in the self-assembly of regenerated silk protein chains and therefore are important in the robust and unusual mechanical properties ultimately achieved in the process.
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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".