Bibliographic record
Abstract
Many elastomeric proteins, which play important roles in a wide range of biological processes, exist as parallel/antiparallelly arranged dimers or multimers to perform their mechanobiological functions. For example, in striated muscle sarcomeres, the giant muscle protein titin exists as hexameric bundles to mediate the passive elasticity of muscles. However, it has not been possible to directly probe the mechanical properties of such parallelly arranged elastomeric proteins. And it remains unknown if the knowledge obtained from single-molecule force spectroscopy studies can be directly extrapolated to such parallelly/antiparallelly arranged systems. Here, we report the development of atomic force microscopy (AFM)-based two-molecule force spectroscopy to directly probe the mechanical properties of two elastomeric proteins that are arranged in parallel. We developed a twin-molecule approach to allow two parallelly arranged elastomeric proteins to be picked up and stretched simultaneously in an AFM experiment. Our results clearly revealed the mechanical features of such parallelly arranged elastomeric proteins during force-extension measurements and allowed for the determination of mechanical unfolding forces of proteins in such an experimental setting. Our study provides a general and robust experimental strategy to closely mimic the physiological condition of such parallel elastomeric protein multimers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".