ModeRNA: a tool for comparative modeling of RNA 3D structure

By Magdalena Rother, Kristian Rother, Tomasz Puton, Janusz M. Bujnicki. 2011

274 citations recorded by the source corpus.

RNA is a large group of functionally important biomacromolecules. In striking analogy to proteins, the function of RNA depends on its structure and dynamics, which in turn is encoded in the linear sequence. However, while there are numerous methods for computational prediction of protein three-dimensional (3D) structure from sequence, with comparative modeling being the most reliable approach, there are very few such methods for RNA. Here, we present ModeRNA, a software tool for comparative modeling of RNA 3D structures. As an input, ModeRNA requires a 3D structure of a template RNA molecule, and a sequence alignment between the target to be modeled and the template. It must be emphasized that a good alignment is required for successful modeling, and for large and complex RNA molecules the development of a good alignment usually requires manual adjustments of the input data based on previous expertise of the respective RNA family. ModeRNA can model post-transcriptional modifications, a functionally important feature analogous to post-translational modifications in proteins. ModeRNA can also model DNA structures or use them as templates. It is equipped with many functions for merging fragments of different nucleic acid structures into a single model and analyzing their geometry. Windows and UNIX implementations of ModeRNA with comprehensive documentation and a tutorial are freely available.

Subfields: Molecular Biology

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Citations in the corpus

SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction (2015, methodology): “Previously, we have developed ModeRNA, a method for RNA 3D structure prediction that builds models using information from structures of homologous molecules used as templates (6,7).”
RNA tertiary structure modeling with BRiQ potential in CASP15 (2023, methodology): “…to prediction of protein structures, a variety of approaches have been developed for predicting the RNA 3D structure, 7 including homology modeling, 8,9 fragment assembly, 10 – 16 and de novo prediction with coarse grained models 17 – 20 as well as deep learning methods, 21 – 26 following the…”
An RNA-centric historical narrative around the Protein Data Bank (2021, unknown): “(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-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers (2020, background): “…Figure S1a. www.rnajournal.org 983 Puzzle 15: The hammerhead ribozyme Although the hammerhead ribozyme was the first of the small nucleolytic ribozymes to be described (Uhlenbeck 1987), the initial structures were trapped in an inactive conformation (Pley et al. 1994; Scott et al. 1995).”
ClaRNA: a classifier of contacts in RNA 3D structures based on a comparative analysis of various classification schemes (2014, methodology): “We also used the ModeRNA stacking detection algorithm (12).”
RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools (2019, methodology): “But it overcomes ModeRNA’s limitation in processing only one chain at the time (Figure 3A).”
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “1H, the Euclidean distance between the P, C4 ’ , and glycosidic N atoms are calculated, where the distance values for the inter-P atoms, inter-C4 ’ atoms, and inter-N atoms are discretized into 56, 44, and 32 bins in the ranges of [2, 30Å], [2, 24 Å], and [2, 18 Å], respectively.”
RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. (2012, methodology): “Briefly, initial models were constructed by template-based modeling and fragment assembly, with constraints on secondary structure, using the comparative RNA modeling tool ModeRNA (Rother et al. 2011b).”
An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling (2011, methodology): “, in enumerative coarse-grained or stochastic all-atom search methods from Dill and coworkers (18, 19), pioneering peptide-modeling work from the 1980s by the Scheraga lab (17), and earlier computational explorations by Levinthal in 1968 (6).”
Recent advances in RNA structurome (2022, methodology): “In recent years, various tools, such as ModeRNA (Rother et al., 2011), RNAbuilder (Flores and Altman, 2010), and Rosetta (Watkins et al., 2019), have been developed to build RNA homology models.”
RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures (2015, methodology): “ModeRNA: a tool for comparative modeling of RNA 3D structure.”
Automated 3D structure composition for large RNAs (2012, methodology): “Knowledge-based, comparative modelling (22) requires access to 3D structural templates and un-equivocal sequence alignment.”
Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement (2021, background): “There are ten types of coordinate transformation matrices to describe nine types of edges: (1) sequenceneighboring base pairs along 5′ to 3′ and 3′ to 5′ directions in the loop regions, (2) sequence-neighboring base pairs along 5′ to 3′ and 3′ to 5′ directions in the Watson–Crick pairing regions, (3”
RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints (2023, methodology): “9,10 When local 3D structural motifs are predicted, we extract candidate conformations from the RNA Bricks database.”
RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme (2017, methodology): “The structure of the YbxF protein was predicted by fold-recognition using the GeneSilico metaserver (Kurowski and Bujnicki 2003), followed by template-based modeling using the L7Ae protein structure (PDB ID: 2FC3) as a template, with MODELLER (Sali and Blundell 1993).”