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- W3204356843 abstract "Molecular dynamics simulations are often used to provide feedback in the design workflow of DNA nanostructures. However, even with coarse-grained models, the convergence of distributions from unbiased simulation is slow, limiting applications to equilibrium structural properties. Given the increasing interest in dynamic, reconfigurable, and deformable devices, methods that enable efficient quantification of large ranges of motion, conformational transitions, and mechanical deformation are critically needed. Metadynamics is an automated biasing technique that enables the rapid acquisition of molecular conformational distributions by flattening free energy landscapes. Here we leveraged this approach to sample the free energy landscapes of DNA nanostructures whose unbiased dynamics are nonergodic, including bistable Holliday junctions and part of a bistable DNA origami structure. Taking a DNA origami-compliant joint as a case study, we further demonstrate that metadynamics can predict the mechanical response of a full DNA origami device to an applied force, showing good agreement with experiments. Our results exemplify the efficient computation of free energy landscapes and force response in DNA nanodevices, which could be applied for rapid feedback in iterative design workflows and generally facilitate the integration of simulation and experiments. Metadynamics will be particularly useful to guide the design of dynamic devices for nanorobotics, biosensing, or nanomanufacturing applications." @default.
- W3204356843 created "2021-10-11" @default.
- W3204356843 creator A5015141581 @default.
- W3204356843 creator A5015516927 @default.
- W3204356843 creator A5070744700 @default.
- W3204356843 creator A5090710614 @default.
- W3204356843 date "2022-05-17" @default.
- W3204356843 modified "2023-10-18" @default.
- W3204356843 title "Probing the Mechanical Properties of DNA Nanostructures with Metadynamics" @default.
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- W3204356843 doi "https://doi.org/10.1021/acsnano.1c08999" @default.
- W3204356843 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35580231" @default.
- W3204356843 hasPublicationYear "2022" @default.
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