Sequence and Structure Determinants for the Self-aggregation of Recombinant Polypeptides Modeled after Human Elastin
Bibliographic record
Abstract
Elastin is a polymeric structural protein that imparts the physical properties of extensibility and elastic recoil to tissues. The mechanism of assembly of the tropoelastin monomer into the elastin polymer probably involves extrinsic protein factors but is also related to an intrinsic capacity of elastin for ordered assembly through a process of hydrophobic self-aggregation or coacervation. Using a series of simple recombinant polypeptides based on elastin sequences and mimicking the unusual alternating domain structure of native elastin, we have investigated the influence of sequence motifs and domain structures on the propensity of these polypeptides for coacervation. The number of hydrophobic domains, their context in the alternating domain structure of elastin, and the specific nature of the hydrophobic domains included in the polypeptides all had major effects on self-aggregation. Surprisingly, in polypeptides with the same number of domains, propensity for coacervation was inversely related to the mean Kyte-Doolittle hydropathy of the polypeptide. Point mutations designed to increase the conformational flexibility of hydrophobic domains had the unexpected effect of suppressing coacervation and promoting formation of amyloid-like fibers. Such simple polypeptides provide a useful model system for understanding the relationship between sequence, structure, and mechanism of assembly of polymeric elastin.
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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".