Sequence and domain arrangements influence mechanical properties of elastin‐like polymeric elastomers
Bibliographic record
Abstract
Elastin is the polymeric, extracellular matrix protein that provides properties of extensibility and elastic recoil to large arteries, lung parenchyma, and other tissues. Elastin assembles by crosslinking through lysine residues of its monomeric precursor, tropoelastin. Tropoelastin, as well as polypeptides based on tropoelastin sequences, undergo a process of self-assembly that aligns lysine residues for crosslinking. As a result, both the full-length monomer as well as elastin-like polypeptides (ELPs) can be made into biomaterials whose properties resemble those of native polymeric elastin. Using both full-length human tropoelastin (hTE) as well as ELPs, we and others have previously reported on the influence of sequence and domain arrangements on self-assembly properties. Here we investigate the role of domain sequence and organization on the tensile mechanical properties of crosslinked biomaterials fabricated from ELP variants. In general, substitutions in ELPs involving similiar domain types (hydrophobic or crosslinking) had little effect on mechanical properties. However, modifications altering either the structure or the characteristic sequence style of these domains had significant effects on such properties. In addition, using a series of deletion and replacement constructs for full-length hTE, we provide new insights into the role of conserved domains of tropoelastin in determining 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 | 0.001 |
| 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".