‘Mechanical Engineering’ of Elastomeric Proteins: Toward Designing New Protein Building Blocks for Biomaterials
Bibliographic record
Abstract
Abstract Elastomeric proteins are subject to stretching force under biological settings and play important roles in regulating the mechanical properties of a wide range of biological machinery. Elastomeric proteins also underlie the superb mechanical properties of many protein‐based biomaterials. The developments of single molecule force spectroscopy have enabled the direct characterization of the mechanical properties of elastomeric proteins at the single molecule level and led to the new burgeoning field of research: single protein mechanics and engineering. Combined single molecule atomic force microscopy and protein engineering efforts are well under way to understand molecular determinants for the mechanical stability of elastomeric proteins and to develop methodologies to tune the mechanical properties of proteins in a rational and systematic fashion, which will lead to the ‘mechanical engineering’ of elastomeric proteins. Here the current status of these experimental efforts is discussed and the successes and challenges in constructing novel proteins with tailored nanomechanical proteins highlighted. The prospect of employing such engineered artificial elastomeric proteins as building blocks for the construction of biomaterials for applications ranging from material sciences to biomedical engineering are also discussed. magnified image
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".