An artificial motor protein that walks along a DNA track
Bibliographic record
Abstract
Summary Paragraph Molecular motors are fundamental to life 1–6 because of their ability to convert chemical energy into mechanical work, an ability that is conferred by the chemical and structural complexity of their constituent proteins. Scientists have long sought to create artificial protein motors that may reveal insights into how biological motors function. While artificial molecular motors based on small molecules 7 and DNA 8,9 have been developed, creating an artificial motor protein has remained an elusive goal in synthetic biology 10 . Here we demonstrate the realization of an artificial protein motor called Tumbleweed (TW) that walks directionally along a DNA track under external control. TW consists of three legs, each with a ligand-gated DNA-binding domain that enables selective interaction with specific sites along a DNA track 11 . Using single-molecule fluorescence assays and a programmable microfluidic device, we show that TW steps directionally along a designed DNA track in response to a defined sequence of ligand inputs. We built our TW molecular walker using a modular approach, combining existing proteins with known properties to achieve emergent motor function, similar to how Nature evolves new proteins. Our design strategy thus offers a a platform for engineering advanced and dynamic protein functionality. Our demonstration of TW walking represents a step toward developing fully autonomous protein motors and opens new avenues for uncovering and leveraging the principles by which biological motors transduce chemical energy into motion.
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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 | 0.001 | 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".