The bar-hinge motor: a synthetic protein design exploiting conformational switching to achieve directional motility
Bibliographic record
Abstract
One challenge to synthetic biology is to design functional machines from natural building blocks, from individual amino acids up to larger motifs such as the coiled coil. Here we investigate a novel bipedal motor concept, the Bar-Hinge Motor (BHM), a peptide-based motor capable of executing directed motion via externally controlled conformational switching between a straight bar and a V -shaped hinged form. Incorporating ligand-regulated binding to a DNA track and periodic control of ligand supply makes the BHM an example of a 'clocked walker'. Here, we employ a coarse-grained computational model for the BHM to assess the feasibility of a proposed experimental realization, with conformational switching regulated through the photoisomerization of peptide-bound azobenzene molecules. The results of numerical simulations using the model show that the incorporation of this conformational switch is necessary for the BHM to execute directional, rather than random, motion on a one-dimensional track. The power-stroke-driven directed motion is seen in the model even under conditions that underestimate the level of control we expect to be able to produce in the experimental realisation, demonstrating that this type of design should be an excellent vehicle for exploring the physics behind protein motion. By investigating its force-dependent dynamics, we show that the BHM is capable of directional motion against an applied load, even in the more relaxed conformational switching regimes. Thus, BHM appears to be an excellent candidate for a motor design incorporating a power stroke, enabling us to explore the ability of switchable coiled-coil designs to deliver power strokes within synthetic biology.
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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) | 0.001 | 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".