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

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 .

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

“…The Bujnicki group used a hybrid modeling strategy (Piatkowski et al 2016) based on the approach tested in the previous editions of the RNA-Puzzles experiment (Cruz et al 2012;Miao et al 2015). If the target sequence exhibited detectable similarity to an RNA with known experimentally determined structure (as happened in the case of Puzzles 4 and 8), the Bujnicki group generated models of the whole molecule or its parts using a template-based (comparative) modeling method ModeRNA (Rother et al 2011b) or its server version (Rother et al 2011a).…”

Evolution of Subgenomic RNA Shapes Dengue Virus Adaptation and Epidemiological Fitness

“…The modeling of DENV-2 fNR structures was optimized through comparative RNA modeling, using ZIKV fNR crystal structure as template (5TPY). This was performed using ModeRNA software (Piatkowski et al, 2016). The local geometry in preliminary models were refined through energy minimization using the AMBER force field in the Molecular Modelling toolkit (Hinsen, 2000).…”

FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds

“…The new FARFAR2 protocol is designed to instead take input information in as simple a manner as possible into a single Rosetta executable, rna_denovo . Analogous to other RNA modeling packages (Krokhotin et al, 2015; Piatkowski et al, 2016; Popenda et al, 2012), the rna_denovo executable now accepts the RNA sequence, the RNA secondary structure in community-standard dot-parentheses notation, and, if available, the names of PDB-formatted files holding template structures of any known sub-motifs or sub-domains.…”

“…Therefore, computational methods to predict 3D ncRNA structure could be of substantial value. Current computational methods for 3D ncRNA structure prediction include coarse-grained MD simulation (Vfold3D (Zhao et al, 2017); iFoldRNA (Krokhotin et al, 2015)), coarse-grained Monte Carlo simulation (Rosetta FARFAR (Das and Baker, 2007) and SimRNA (Piatkowski et al, 2016)), and motif assembly (RNAComposer (Popenda et al, 2012); 3dRNA (Jian et al, 2017); MC-Fold (Parisien and Major, 2008)).…”