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Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures

DNA nanotechnology enables the programmed synthesis of intricate nanometer-scale structures for diverse applications in materials and biological science. Precise control over the 3D solution shape and mechanical flexibility of target designs is important to achieve desired functionality. Because exp...

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Autores principales: Kim, Do-Nyun, Kilchherr, Fabian, Dietz, Hendrik, Bathe, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3326316/
https://www.ncbi.nlm.nih.gov/pubmed/22156372
http://dx.doi.org/10.1093/nar/gkr1173
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author Kim, Do-Nyun
Kilchherr, Fabian
Dietz, Hendrik
Bathe, Mark
author_facet Kim, Do-Nyun
Kilchherr, Fabian
Dietz, Hendrik
Bathe, Mark
author_sort Kim, Do-Nyun
collection PubMed
description DNA nanotechnology enables the programmed synthesis of intricate nanometer-scale structures for diverse applications in materials and biological science. Precise control over the 3D solution shape and mechanical flexibility of target designs is important to achieve desired functionality. Because experimental validation of designed nanostructures is time-consuming and cost-intensive, predictive physical models of nanostructure shape and flexibility have the capacity to enhance dramatically the design process. Here, we significantly extend and experimentally validate a computational modeling framework for DNA origami previously presented as CanDo [Castro,C.E., Kilchherr,F., Kim,D.-N., Shiao,E.L., Wauer,T., Wortmann,P., Bathe,M., Dietz,H. (2011) A primer to scaffolded DNA origami. Nat. Meth., 8, 221–229.]. 3D solution shape and flexibility are predicted from basepair connectivity maps now accounting for nicks in the DNA double helix, entropic elasticity of single-stranded DNA, and distant crossovers required to model wireframe structures, in addition to previous modeling (Castro,C.E., et al.) that accounted only for the canonical twist, bend and stretch stiffness of double-helical DNA domains. Systematic experimental validation of nanostructure flexibility mediated by internal crossover density probed using a 32-helix DNA bundle demonstrates for the first time that our model not only predicts the 3D solution shape of complex DNA nanostructures but also their mechanical flexibility. Thus, our model represents an important advance in the quantitative understanding of DNA-based nanostructure shape and flexibility, and we anticipate that this model will increase significantly the number and variety of synthetic nanostructures designed using nucleic acids.
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spelling pubmed-33263162012-04-16 Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures Kim, Do-Nyun Kilchherr, Fabian Dietz, Hendrik Bathe, Mark Nucleic Acids Res Computational Biology DNA nanotechnology enables the programmed synthesis of intricate nanometer-scale structures for diverse applications in materials and biological science. Precise control over the 3D solution shape and mechanical flexibility of target designs is important to achieve desired functionality. Because experimental validation of designed nanostructures is time-consuming and cost-intensive, predictive physical models of nanostructure shape and flexibility have the capacity to enhance dramatically the design process. Here, we significantly extend and experimentally validate a computational modeling framework for DNA origami previously presented as CanDo [Castro,C.E., Kilchherr,F., Kim,D.-N., Shiao,E.L., Wauer,T., Wortmann,P., Bathe,M., Dietz,H. (2011) A primer to scaffolded DNA origami. Nat. Meth., 8, 221–229.]. 3D solution shape and flexibility are predicted from basepair connectivity maps now accounting for nicks in the DNA double helix, entropic elasticity of single-stranded DNA, and distant crossovers required to model wireframe structures, in addition to previous modeling (Castro,C.E., et al.) that accounted only for the canonical twist, bend and stretch stiffness of double-helical DNA domains. Systematic experimental validation of nanostructure flexibility mediated by internal crossover density probed using a 32-helix DNA bundle demonstrates for the first time that our model not only predicts the 3D solution shape of complex DNA nanostructures but also their mechanical flexibility. Thus, our model represents an important advance in the quantitative understanding of DNA-based nanostructure shape and flexibility, and we anticipate that this model will increase significantly the number and variety of synthetic nanostructures designed using nucleic acids. Oxford University Press 2012-04 2011-12-10 /pmc/articles/PMC3326316/ /pubmed/22156372 http://dx.doi.org/10.1093/nar/gkr1173 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Kim, Do-Nyun
Kilchherr, Fabian
Dietz, Hendrik
Bathe, Mark
Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures
title Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures
title_full Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures
title_fullStr Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures
title_full_unstemmed Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures
title_short Quantitative prediction of 3D solution shape and flexibility of nucleic acid nanostructures
title_sort quantitative prediction of 3d solution shape and flexibility of nucleic acid nanostructures
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3326316/
https://www.ncbi.nlm.nih.gov/pubmed/22156372
http://dx.doi.org/10.1093/nar/gkr1173
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