Literature DB >> 27665602

RNA 3D Structure Modeling by Combination of Template-Based Method ModeRNA, Template-Free Folding with SimRNA, and Refinement with QRNAS.

Pawel Piatkowski1, Joanna M Kasprzak2,3, Deepak Kumar4, Marcin Magnus1, Grzegorz Chojnowski1, Janusz M Bujnicki2,3.   

Abstract

RNA encompasses an essential part of all known forms of life. The functions of many RNA molecules are dependent on their ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that either utilize information derived from known structures of other RNA molecules (by way of template-based modeling) or attempt to simulate the physical process of RNA structure formation (by way of template-free modeling). All computational methods suffer from various limitations that make theoretical models less reliable than high-resolution experimentally determined structures. This chapter provides a protocol for computational modeling of RNA 3D structure that overcomes major limitations by combining two complementary approaches: template-based modeling that is capable of predicting global architectures based on similarity to other molecules but often fails to predict local unique features, and template-free modeling that can predict the local folding, but is limited to modeling the structure of relatively small molecules. Here, we combine the use of a template-based method ModeRNA with a template-free method SimRNA. ModeRNA requires a sequence alignment of the target RNA sequence to be modeled with a template of the known structure; it generates a model that predicts the structure of a conserved core and provides a starting point for modeling of variable regions. SimRNA can be used to fold small RNAs (<80 nt) without any additional structural information, and to refold parts of models for larger RNAs that have a correctly modeled core. ModeRNA can be either downloaded, compiled and run locally or run through a web interface at http://genesilico.pl/modernaserver/ . SimRNA is currently available to download for local use as a precompiled software package at http://genesilico.pl/software/stand-alone/simrna and as a web server at http://genesilico.pl/SimRNAweb . For model optimization we use QRNAS, available at http://genesilico.pl/qrnas .

Keywords:  Comparative modeling; De novo modeling; Free modeling; Homology modeling; Monte Carlo simulation; RNA structure; Statistical potential

Mesh:

Substances:

Year:  2016        PMID: 27665602     DOI: 10.1007/978-1-4939-6433-8_14

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  6 in total

1.  Using Rosetta for RNA homology modeling.

Authors:  Andrew M Watkins; Ramya Rangan; Rhiju Das
Journal:  Methods Enzymol       Date:  2019-06-11       Impact factor: 1.600

2.  FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds.

Authors:  Andrew Martin Watkins; Ramya Rangan; Rhiju Das
Journal:  Structure       Date:  2020-06-11       Impact factor: 5.006

3.  Methods for Identifying Microbial Natural Product Compounds that Target Kinetoplastid RNA Structural Motifs by Homology and De Novo Modeled 18S rRNA.

Authors:  Harrison Ndung'u Mwangi; Edward Kirwa Muge; Peter Waiganjo Wagacha; Albert Ndakala; Francis Jackim Mulaa
Journal:  Int J Mol Sci       Date:  2021-04-26       Impact factor: 5.923

4.  Constrained peptides mimic a viral suppressor of RNA silencing.

Authors:  Arne Kuepper; Niall M McLoughlin; Saskia Neubacher; Alejandro Yeste-Vázquez; Estel Collado Camps; Chandran Nithin; Sunandan Mukherjee; Lucas Bethge; Janusz M Bujnicki; Roland Brock; Stefan Heinrichs; Tom N Grossmann
Journal:  Nucleic Acids Res       Date:  2021-12-16       Impact factor: 16.971

5.  RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme.

Authors:  Zhichao Miao; Ryszard W Adamiak; Maciej Antczak; Robert T Batey; Alexander J Becka; Marcin Biesiada; Michał J Boniecki; Janusz M Bujnicki; Shi-Jie Chen; Clarence Yu Cheng; Fang-Chieh Chou; Adrian R Ferré-D'Amaré; Rhiju Das; Wayne K Dawson; Feng Ding; Nikolay V Dokholyan; Stanisław Dunin-Horkawicz; Caleb Geniesse; Kalli Kappel; Wipapat Kladwang; Andrey Krokhotin; Grzegorz E Łach; François Major; Thomas H Mann; Marcin Magnus; Katarzyna Pachulska-Wieczorek; Dinshaw J Patel; Joseph A Piccirilli; Mariusz Popenda; Katarzyna J Purzycka; Aiming Ren; Greggory M Rice; John Santalucia; Joanna Sarzynska; Marta Szachniuk; Arpit Tandon; Jeremiah J Trausch; Siqi Tian; Jian Wang; Kevin M Weeks; Benfeard Williams; Yi Xiao; Xiaojun Xu; Dong Zhang; Tomasz Zok; Eric Westhof
Journal:  RNA       Date:  2017-01-30       Impact factor: 4.942

6.  QRNAS: software tool for refinement of nucleic acid structures.

Authors:  Juliusz Stasiewicz; Sunandan Mukherjee; Chandran Nithin; Janusz M Bujnicki
Journal:  BMC Struct Biol       Date:  2019-03-21
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.