Evidence map›Paper›PMID 24189588›Full record

ArticleMethods (San Diego, Calif.)2014

Computational and experimental characterization of RNA cubic nanoscaffolds.

Kirill A Afonin, Wojciech Kasprzak, Eckart Bindewald, Praneet S Puppala, Alex R Diehl, Kenneth T Hall, Tae Jin Kim, Michael T Zimmermann, Robert L Jernigan, Luc Jaeger and 1 more

Abstract read
In one paragraph

Article in Methods (San Diego, Calif.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

45 citing papers in PubMed.

  1. Article
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  9. Unleashing the potential of catalytic RNAs to combat mis-spliced transcripts.Frontiers in bioengineering and biotechnology · 2023
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Kirill A AfoninCenter for Cancer Research Nanobiology Program, National Cancer Institute, Frederick, MD 21702, USA.
Wojciech KasprzakBasic Science Program, SAIC-Frederick, Inc., Center for Cancer Research Nanobiology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
Eckart BindewaldBasic Science Program, SAIC-Frederick, Inc., Center for Cancer Research Nanobiology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
Praneet S PuppalaCenter for Cancer Research Nanobiology Program, National Cancer Institute, Frederick, MD 21702, USA.
Alex R DiehlCenter for Cancer Research Nanobiology Program, National Cancer Institute, Frederick, MD 21702, USA.
Kenneth T HallCenter for Cancer Research Nanobiology Program, National Cancer Institute, Frederick, MD 21702, USA.
Tae Jin KimCenter for Cancer Research Nanobiology Program, National Cancer Institute, Frederick, MD 21702, USA.
Michael T ZimmermannDepartment of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA 50011, USA.
Robert L JerniganDepartment of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA 50011, USA.
Luc JaegerDepartment of Chemistry and Biochemistry, Biomolecular Science and Engineering Program, University of California, Santa Barbara, CA 93106-9510, USA. Electronic address: jaeger@chem.ucsb.edu.
Bruce A ShapiroCenter for Cancer Research Nanobiology Program, National Cancer Institute, Frederick, MD 21702, USA. Electronic address: shapirbr@mail.nih.gov.

Funding

Computational RNA NanodesignZIABC011061 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI SHAPIRO, BRUCE · 2009 to 2021
$12.3M
Exploring the RNA syntax for engineering therapeutic RNA-based nano-factoriesR01GM079604 · NIGMS · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI JAEGER, LUC · 2007 to 2011
$1.7M
CCR NIH HHS HHSN261200800001CIntramural NIH HHS ZIA BC011061NCI NIH HHS HHSN261200800001ENIGMS NIH HHS R01 GM079604NIGMS NIH HHS R01GM-079604
6 · The paper itself

Abstract

The fast-developing field of RNA nanotechnology requires the adoption and development of novel and faster computational approaches to modeling and characterization of RNA-based nano-objects. We report the first application of Elastic Network Modeling (ENM), a structure-based dynamics model, to RNA nanotechnology. With the use of an Anisotropic Network Model (ANM), a type of ENM, we characterize the dynamic behavior of non-compact, multi-stranded RNA-based nanocubes that can be used as nano-scale scaffolds carrying different functionalities. Modeling the nanocubes with our tool NanoTiler and exploring the dynamic characteristics of the models with ANM suggested relatively minor but important structural modifications that enhanced the assembly properties and thermodynamic stabilities. In silico and in vitro, we compared nanocubes having different numbers of base pairs per side, showing with both methods that the 10 bp-long helix design leads to more efficient assembly, as predicted computationally. We also explored the impact of different numbers of single-stranded nucleotide stretches at each of the cube corners and showed that cube flexibility simulations help explain the differences in the experimental assembly yields, as well as the measured nanomolecule sizes and melting temperatures. This original work paves the way for detailed computational analysis of the dynamic behavior of artificially designed multi-stranded RNA nanoparticles.

Indexed as

AnisotropyComputer SimulationCryoelectron MicroscopyLightModels, ChemicalModels, MolecularNanostructuresNucleic Acid ConformationRNAScattering, RadiationRNAAnisotropic network modelNanostructure designNative PAGERNA architectonicsRNA nanostructure characterizationRNA nanostructure dynamicsRNA nanotechnologyTGGE

Identifiers

PMID24189588
PMCPMC4007386

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.