Literature DB >> 27125734

Coarse-grained modeling of RNA 3D structure.

Wayne K Dawson1, Maciej Maciejczyk2, Elzbieta J Jankowska3, Janusz M Bujnicki4.   

Abstract

Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result.
Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Keywords:  Bioinformatics; Modeling; Molecular dynamics; Monte Carlo dynamics; RNA; Simulation; Structure

Mesh:

Substances:

Year:  2016        PMID: 27125734     DOI: 10.1016/j.ymeth.2016.04.026

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  19 in total

1.  Inverse folding with RNA-As-Graphs produces a large pool of candidate sequences with target topologies.

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2.  IsRNA1: De Novo Prediction and Blind Screening of RNA 3D Structures.

Authors:  Dong Zhang; Jun Li; Shi-Jie Chen
Journal:  J Chem Theory Comput       Date:  2021-02-09       Impact factor: 6.006

Review 3.  RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview.

Authors:  Jiří Šponer; Giovanni Bussi; Miroslav Krepl; Pavel Banáš; Sandro Bottaro; Richard A Cunha; Alejandro Gil-Ley; Giovanni Pinamonti; Simón Poblete; Petr Jurečka; Nils G Walter; Michal Otyepka
Journal:  Chem Rev       Date:  2018-01-03       Impact factor: 60.622

4.  A coarse-grained model for assisting the investigation of structure and dynamics of large nucleic acids by ion mobility spectrometry-mass spectrometry.

Authors:  S Vangaveti; R J D'Esposito; J L Lippens; D Fabris; S V Ranganathan
Journal:  Phys Chem Chem Phys       Date:  2017-06-14       Impact factor: 3.676

5.  Biomolecular modeling thrives in the age of technology.

Authors:  Tamar Schlick; Stephanie Portillo-Ledesma
Journal:  Nat Comput Sci       Date:  2021-05-20

6.  Thermodynamics of unfolding mechanisms of mouse mammary tumor virus pseudoknot from a coarse-grained loop-entropy model.

Authors:  Ke Tang; Jorjethe Roca; Rong Chen; Anjum Ansari; Jie Liang
Journal:  J Biol Phys       Date:  2022-04-20       Impact factor: 1.560

7.  Identification of novel RNA design candidates by clustering the extended RNA-As-Graphs library.

Authors:  Swati Jain; Qiyao Zhu; Amiel S P Paz; Tamar Schlick
Journal:  Biochim Biophys Acta Gen Subj       Date:  2020-01-16       Impact factor: 3.770

8.  F-RAG: Generating Atomic Coordinates from RNA Graphs by Fragment Assembly.

Authors:  Swati Jain; Tamar Schlick
Journal:  J Mol Biol       Date:  2017-10-05       Impact factor: 5.469

9.  A Fiedler Vector Scoring Approach for Novel RNA Motif Selection.

Authors:  Qiyao Zhu; Tamar Schlick
Journal:  J Phys Chem B       Date:  2021-01-20       Impact factor: 2.991

10.  Electron Transport in a Dioxygenase-Ferredoxin Complex: Long Range Charge Coupling between the Rieske and Non-Heme Iron Center.

Authors:  Wayne K Dawson; Ryota Jono; Tohru Terada; Kentaro Shimizu
Journal:  PLoS One       Date:  2016-09-22       Impact factor: 3.240

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