Abstract
Autonomous molecular machines capable of converting chemical energy into mechanical motion are foundational components for synthetic nanoscale systems. Inspired by biological motors, we report the construction of a tunable, RNA-fueled DNA origami engine that drives the cyclic movement of a 500 nm-diameter particle at the microscale. The engine operates via sequential RNA–DNA hybridization and enzymatic cleavage by RNase H, enabling reversible switching between folded and unfolded conformations without external intervention. By modulating RNA and enzyme concentrations and controlling temperature, we achieve tunable switching kinetics, with transition periods as short as ~10 s. Kinetic modeling reveals that the folding pathway is governed by both productive RNA binding and the enzymatic clearance of misfolded intermediates, while unfolding is primarily controlled by RNase H activity. Since the RNA fuel binds specifically to the DNA strands, each engine is addressable simply by changing the sequences. This work demonstrates a programmable, self-resetting molecular actuator and offers a blueprint for building more complex nanomechanical systems with forces and energies comparable to molecular motors.
Data availability
The data supporting the findings of the study are available in the article and its Supplementary Information. All data are available from the corresponding author upon request. Source data are provided with this paper. Source data is available for Figs. 2B–C, 3B–G, 4 and 5C in the associated source data file. Uncropped original gel images for Supplementary Figs. 3 and 4 are also included. Source data are provided with this paper.
Code availability
The code used for particle tracking is based on the publicly available Trackpy package (https://github.com/soft-matter/trackpy). The custom Python scripts developed for kinetic analysis, including double-exponential weighted fitting and model simulations, are available in a public GitHub repository (https://github.com/kw2556nyu/A_Tunable_Autonomous_RNA_Fueled_Micro_Engine_Analysis_Code.git) and archived with the permanent identifier https://doi.org/10.5281/zenodo.18173464. The analysis relies on standard open-source libraries (NumPy, SciPy, Matplotlib) as specified in the repository documentation.
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Acknowledgements
This research received primary support from the U.S. Department of Energy under Award DE-SC0007991 (P.M.C., N.S., R.S., K.W.), which covered project conception, planning, and data analysis. The experimental implementations were supported by DOE Award DE-SC0020971 (K.W.). Computational modeling efforts were funded by the National Science Foundation through the NSF-BSF Organization Far From Equilibrium Program (GRANT NO 2414721) and the ISS: GOALI initiative (NSF Grant No. 11832291) (B.G., W.C.). Early-stage design efforts were supported by the Center for Bio-Inspired Energy Sciences (CBES), an Energy Frontier Research Center sponsored by the U.S. DOE Office of Science, Basic Energy Sciences (Award DE-SC0000989) (G.Z.). Additional funding was provided by the Office of Naval Research (ONR Grant N000141912596) and NSF CCF-2106790 (R.S., N.C.S.), and P.M.C. acknowledges support from the Simons Foundation (Award No. 7200138).
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Project conceptualization was led by P.M.C., N.C.S., R.S., and K.W. Sample fabrication and preparation were performed by K.W. Experimental procedures and data analysis were conducted by K.W., W.C., G.Z., Q.H., B.G., P.M.C., and N.C.S.; Overall project oversight and coordination were carried out by P.M.C. and N.C.S. The initial manuscript draft was prepared by P.M.C. and K.W. The revision experiments were conducted by G.Z., M.P., and L.Z.
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Nature Communications thanks Khalid Salaita, Luona Zhang, and Ryota Iino for their contribution to the peer review of this work. A peer review file is available.
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Wang, K., Chen, W., Guo, B. et al. A tunable autonomous RNA-fueled micro-engine. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69521-x
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DOI: https://doi.org/10.1038/s41467-026-69521-x
