Recombinant Silk Fiber Properties Correlate to Prefibrillar Self‐Assembly
Bibliographic record
Abstract
Spider silks are desirable materials with mechanical properties superior to most synthetic materials coupled with biodegradability and biocompatibility. In order to replicate natural silk properties using recombinant spider silk proteins (spidroins) and wet-spinning methods, the focus to date has typically been on modifying protein sequence, protein size, and spinning conditions. Here, an alternative approach is demonstrated. Namely, using the same ≈57 kDa recombinant aciniform silk protein with a consistent wet-spinning protocol, fiber mechanical properties are shown to significantly differ as a function of the solvent used to dissolve the protein at high concentration (the "spinning dope" solution). A fluorinated acid/alcohol/water dope leads to drastic improvement in fibrillar extensibility and, correspondingly, toughness compared to fibers produced using a previously developed fluorinated alcohol/water dope. To understand the underlying cause for these mechanical differences, morphology and structure of the two classes of silk fiber are compared, with features tracing back to dope-state protein structuring and preassembly. Specifically, distinct classes of spidroin nanoparticles appear to form in each dope prior to fiber spinning and these preassembled states are, in turn, linked to fiber morphology, structure, and mechanical properties. Tailoring of dope-state spidroin nanoparticle assembly, thus, appears a promising strategy to modulate fibrillar silk properties.
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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) | 0.001 | 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".