Proline‐poor hydrophobic domains modulate the assembly and material properties of polymeric elastin
Bibliographic record
Abstract
Elastin is a self-assembling extracellular matrix protein that provides elasticity to tissues. For entropic elastomers such as elastin, conformational disorder of the monomer building block, even in the polymeric form, is essential for elastomeric recoil. The highly hydrophobic monomer employs a range of strategies for maintaining disorder and flexibility within hydrophobic domains, particularly involving a minimum compositional threshold of proline and glycine residues. However, the native sequence of hydrophobic elastin domain 30 is uncharacteristically proline-poor and, as an isolated polypeptide, is susceptible to formation of amyloid-like structures comprised of stacked β-sheet. Here we investigated the biophysical and mechanical properties of multiple sets of elastin-like polypeptides designed with different numbers of proline-poor domain 30 from human or rat tropoelastins. We compared the contributions of these proline-poor hydrophobic sequences to self-assembly through characterization of phase separation, and to the tensile properties of cross-linked, polymeric materials. We demonstrate that length of hydrophobic domains and propensity to form β-structure, both affecting polypeptide chain flexibility and cross-link density, play key roles in modulating elastin mechanical properties. This study advances the understanding of elastin sequence-structure-function relationships, and provides new insights that will directly support rational approaches to the design of biomaterials with defined suites of mechanical 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) | 0.000 | 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".