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RNA Nanostructures

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Cover of 'RNA Nanostructures'

Table of Contents

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    Book Overview
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    Chapter 1 A New Method to Predict Ion Effects in RNA Folding
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    Chapter 2 Computational Generation of RNA Nanorings
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    Chapter 3 Protocols for Molecular Dynamics Simulations of RNA Nanostructures
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    Chapter 4 Rolling Circle Transcription for the Self-Assembly of Multimeric RNAi Structures and Its Applications in Nanomedicine
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    Chapter 5 Computational Prediction of the Immunomodulatory Potential of RNA Sequences
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    Chapter 6 Cotranscriptional Production of Chemically Modified RNA Nanoparticles
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    Chapter 7 Supported Fluid Lipid Bilayer as a Scaffold to Direct Assembly of RNA Nanostructures
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    Chapter 8 Evaluation of Thermal Stability of RNA Nanoparticles by Temperature Gradient Gel Electrophoresis (TGGE) in Native Condition
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    Chapter 9 Design and Crystallography of Self-Assembling RNA Nanostructures
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    Chapter 10 X-Aptamer Selection and Validation
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    Chapter 11 Design and Preparation of Aptamer–siRNA Chimeras (AsiCs) for Targeted Cancer Therapy
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    Chapter 12 Cellular Delivery of siRNAs Using Bolaamphiphiles
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    Chapter 13 Preparation and Optimization of Lipid-Like Nanoparticles for mRNA Delivery
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    Chapter 14 Chitosan Nanoparticles for miRNA Delivery
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    Chapter 15 Synthesis of PLGA–Lipid Hybrid Nanoparticles for siRNA Delivery Using the Emulsion Method PLGA-PEG–Lipid Nanoparticles for siRNA Delivery
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    Chapter 16 Oxime Ether Lipids as Transfection Agents: Assembly and Complexation with siRNA
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    Chapter 17 Polycationic Probe-Guided Nanopore Single-Molecule Counter for Selective miRNA Detection
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    Chapter 18 Intracellular Reassociation of RNA–DNA Hybrids that Activates RNAi in HIV-Infected Cells
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    Chapter 19 Construction and In Vivo Testing of Prokaryotic Riboregulators
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    Chapter 20 Preparation of a Conditional RNA Switch
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    Chapter 21 Rational Engineering of a Modular Group I Ribozyme to Control Its Activity by Self-Dimerization
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    Chapter 22 CRISPR-Cas RNA Scaffolds for Transcriptional Programming in Yeast
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    Chapter 23 Using Planar Phi29 pRNA Three-Way Junction to Control Size and Shape of RNA Nanoparticles for Biodistribution Profiling in Mice
Attention for Chapter 1: A New Method to Predict Ion Effects in RNA Folding
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Chapter title
A New Method to Predict Ion Effects in RNA Folding
Chapter number 1
Book title
RNA Nanostructures
Published in
Methods in molecular biology, July 2017
DOI 10.1007/978-1-4939-7138-1_1
Pubmed ID
Book ISBNs
978-1-4939-7137-4, 978-1-4939-7138-1
Authors

Li-Zhen Sun, Shi-Jie Chen

Abstract

The strong interaction between metal ions in solution and highly charged RNA molecules is critical for RNA structure formation and stabilization. Metal ions binding to RNA can induce RNA structural changes that are important for RNA cellular functions. Therefore, quantitative modeling of the ion effects is essential for RNA structure prediction and RNA-based molecular design. Recently, inspired by the increasing experimental evidence that supports the importance of ion correlation and fluctuation in ion-RNA interactions, we developed a new computational model, Monte Carlo Tightly Bound Ion (MCTBI) model. The validity of the model is shown by the improved accuracy in the predictions for ion binding properties and ion-dependent free energies for RNA structures. In this chapter, using homodimeric tetraloop-receptor docking as an illustrative example, we showcase the MCTBI method for the computational prediction of the ion effects in RNA folding.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 25%
Professor > Associate Professor 1 13%
Student > Ph. D. Student 1 13%
Unknown 4 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 25%
Chemical Engineering 1 13%
Agricultural and Biological Sciences 1 13%
Unknown 4 50%