Automated 3D structure composition for large RNAs

By Mariusz Popenda, Marta Szachniuk, Maciej Antczak, Katarzyna J Purzycka, Piotr Lukasiak, Natalia Bartol, J. Błażewicz, Ryszard W. Adamiak. 2012

696 citations recorded by the source corpus.

Understanding the numerous functions that RNAs play in living cells depends critically on knowledge of their three-dimensional structure. Due to the difficulties in experimentally assessing structures of large RNAs, there is currently great demand for new high-resolution structure prediction methods. We present the novel method for the fully automated prediction of RNA 3D structures from a user-defined secondary structure. The concept is founded on the machine translation system. The translation engine operates on the RNA FRABASE database tailored to the dictionary relating the RNA secondary structure and tertiary structure elements. The translation algorithm is very fast. Initial 3D structure is composed in a range of seconds on a single processor. The method assures the prediction of large RNA 3D structures of high quality. Our approach needs neither structural templates nor RNA sequence alignment, required for comparative methods. This enables the building of unresolved yet native and artificial RNA structures. The method is implemented in a publicly available, user-friendly server RNAComposer. It works in an interactive mode and a batch mode. The batch mode is designed for large-scale modelling and accepts atomic distance restraints. Presently, the server is set to build RNA structures of up to 500 residues.

Subfields: Molecular Biology

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Citations in the corpus

Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, neutral): “In contrast, other approaches, including SimRNA 12 , FARFAR2 10 and RNAComposer 34 , exhibited significantly longer running times, probably due to the large-scale sampling processes employed by these methods (Fig.”
iFoldRNA v2: folding RNA with constraints (2015, methodology): “As a benchmark we used two other servers RNAComposer (Popenda et al. , 2012) and 3dRNA (Zhao et al. , 2012).”
MCQ4Structures to compute similarity of molecule structures (2013, background): “They support a choice between candidate 3D models and they are applied to evaluate the generated structures (Popenda et al. 2012).”
FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, positive): “, 2015]), and motif assembly (RNAComposer [Popenda et al., 2012]; 3dRNA [Jian et al.”
Recent advances in RNA structurome (2022, methodology): “Several approaches have been developed to perform all-atom assembling using known fragments, such as FARNA/FARFAR/FARFAR2 (Das and Baker, 2007; Das et al., 2010; Watkins et al., 2020), RNAComposer (Popenda et al., 2012), and 3dRNA (Zhang et al., 2020c; Zhao et al., 2012).”
3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction (2019, methodology): “Therefore, many computational methods have been proposed for predicting the 3D structure of non-coding RNA, e.g., FARNA [2] / FARFAR [3], MC-Sym [4], NAST [5], iFoldRNA [6,7], ASSEMBLE [8], Vfold [9,10], RNAComposer [11,12], 3dRNA [13–15] and F-RAG [16].”
RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme (2017, methodology): “Structuremodeling was performed using discrete molecular dynamics (DMD) simulations (Dokholyan et al. 1998; Proctor et al. 2011).”
RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures (2015, methodology): “Since RNAComposer automatically conducts two energy minimization steps prior to returning final RNA 3D structure this model did not require any further refinement.”
Using Rosetta for RNA homology modeling (2019, methodology): “Before describing this Rosetta-focused methodology, we note that other groups have also developed excellent tools outside Rosetta for RNA homology modeling, and we advise the reader to try multiple tools and check for consensus (Piatkowski et al., 2016; Popenda et al., 2012; Xu & Chen, 2018).”
An RNA-centric historical narrative around the Protein Data Bank (2021, positive): “(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): “According to the scaffold analysis (Porter et al. 2017), the secondary structure of 5-hydroxytryptophan riboswitch is similar to the guanine riboswitch (Batey et al. 2004), di-GMP riboswitch (Kulshina et al. 2009), and hammerhead ribozyme (Anderson et al. 2013), which have been reported.”
RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints (2023, methodology): “12 For models of RNA-ligand complexes, the initial structures are generated by docking using rDock 13 with a dock_solv desolvation protocol, with the docking volume set using the template ligand, with 100 generated conformations combined with scoring with the AnnapuRNA scoring function.”