Artificial Forisomes Are Ideal Models of Forisome Assembly and Activity That Allow the Development of Technical Devices
Bibliographic record
Abstract
Forisomes are protein polymers found in leguminous plants that have the remarkable ability to undergo reversible "muscle-like" contractions in the presence of divalent cations and in extreme pH environments. To gain insight into the molecular basis of forisome structure and assembly, we used confocal laser scanning microscopy to monitor the assembly of fluorescence-labeled artificial forisomes in real time, revealing two distinct assembly processes involving either fiber elongation or fiber alignment. We also used scanning and transmission electron microscopy and X-ray diffraction to investigate the ultrastructure of forisomes, finding that individual fibers are arranged into compact fibril bundles that disentangle with minimal residual order in the presence of calcium ions. To demonstrate the potential applications of artificial forisomes, we created hybrid protein bodies from forisome subunits fused to the B-domain of staphylococcal protein A. This allowed the functionalization of the artificial forisomes with antibodies that were then used to target forisomes to specific regions on a substrate, providing a straightforward approach to develop forisome-based technical devices with precise configurations. The functional contractile properties of forisomes are also better preserved when they are immobilized via affinity reagents rather than by direct contact to the substrate. Artificial forisomes produced in plants and yeast therefore provide an ideal model for the investigation of forisome structure and assembly and for the design and testing of tailored artificial forisomes for technical applications.
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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 | 0.001 |
| 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".