SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction

By Michal J. Boniecki, G. Lach, W. Dawson, Konrad Tomala, Pawel Lukasz, Tomasz Soltysinski, Kristian Rother, Janusz M. Bujnicki. 2015

403 citations recorded by the source corpus.

RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the 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. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools (2019, methodology): “Tools for SimRNA and Rosetta help to prepare input files, run modeling, cluster results, and extract models from trajectory files.”
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, neutral): “For example, the fraction of the test targets for which DRfold achieved a lower RMSD than the control methods was 80.0% (to 3dRNA), 82.5% (FARFAR2), 72.5% (RNAComposer), 75.0% (RNA-BRiQ), and 80.0% (SimRNA), respectively, as shown in Fig.”
FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, positive): “…dynamics simulation (Vfold3D [Zhao et al., 2017] and iFoldRNA [Krokhotin et al., 2015]), Monte Carlo (Rosetta FARFAR [Das and Baker, 2007] and SimRNA [Boniecki et al., 2015]), and motif assembly (RNAComposer [Popenda et al., 2012]; 3dRNA [Jian et al., 2017]; MC-Fold [Parisien and Major, 2008]).”
Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement (2021, neutral): “Examples are FARNA 7 , MC-fold/MC-Sym 8 , Rosetta-SWM 9 , ifoldRNA 10,11 , Vfold 12,13 , 3dRNA 14,15 , and SimRNA 16 .”
RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme (2017, methodology): “Remodeling of uncertain regions and modeling of RNA molecules that lacked suitable templates relied mostly on template-free folding using the coarse-grained method SimRNA (Boniecki et al. 2016).”
An RNA-centric historical narrative around the Protein Data Bank (2021, neutral): “(1) FRODO developed by Alwyn Jones was a pioneering tool in computer molecular graphics (151); (2) NUCLIN-NUCLSQ (159), an inclusive refinement program dedicated to nucleic acids and based on Hendrickson–Konnert PROLSQ (253, 254); (3) MIDAS (255, 256); (4) MC-Sym (257); (5) MANIP (258); (6) Chimera ”
RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints (2023, methodology): “16 The initial models are then subjected to a conformational space search with SimRNA 17,18 and its extensions that allow modeling of RNA interactions with small molecules and proteins.”