RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

By Zhichao Miao, Ryszard W. Adamiak, Marc-Frédérick Blanchet, Michal J. Boniecki, Janusz M. Bujnicki, Shi-Jie Chen, C. Cheng, Grzegorz Chojnowski, Fang-Chieh Chou, P. Cordero and 30 more. 2015

197 citations recorded by the source corpus.

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5–3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson–Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

Subfields: Molecular Biology

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Citations in the corpus

Assessment of three-dimensional RNA structure prediction in CASP15 (2023, neutral): “Since then, several additional publications have reported the results of the RNAPuzzles assessments.(9,10,11) In 2021, it became clear that accelerations in RNA structure determination(12) would allow enough targets for a single CASP season.”
3D RNA and Functional Interactions from Evolutionary Couplings. (2015, unknown): “…7 - 10 Å, comparable to the state of the art for RNA structure predictions that use tertiary contacts derived from biochemical probing (6.8 – 11.7 Å) (Miao et al., 2015) and a dramatic improvement over pure in silico structure prediction, where the average RMSD is 20 Å for medium size RNAs (50 –…”
RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme (2017, background): “RNA-Puzzles is a CASP-like (Moult et al. 2016) collective blind experiment for three-dimensional (3D) RNA structure Corresponding authors: z.miao@ibmc-cnrs.unistra.fr, e.westhof@ ibmc-cnrs.unistra.fr Article is online at http://www.rnajournal.org/cgi/doi/10.1261/rna.”
FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, methodology): “…RNA-Puzzles, FARFAR modeling has involved problem-specific expert intuition, such as guesses of ligand binding sites based on inspection of alignments, as well as algorithmic extensions created ‘on-the-fly’ to explore novel ideas inspired by the targets (Cruz et al., 2012; Miao et al., 2015, 2017).”
RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers (2020, methodology): “One success achieved in Round IV is that all the bestpredictedmodels have aRMSD<7Å. Thehigh atomic clash of otherwise accurate models has been pointed out since the Round II prediction (Miao et al. 2015) and new structure relaxation approaches have been developed.”
Using Rosetta for RNA homology modeling (2019, methodology): “…experiments offer reliable RNA secondary structures (Tian & Das, 2016; Cheng, Kladwang, Yesselman, & Das, 2017); it is worth noting here that SHAPE and DMS-guided approaches are simpler but have been less reliable (Kladwang, VanLang, Cordero, & Das, 2011; Tian & Das, 2016; Miao et al., 2017, 2015).”
De Novo RNA Tertiary Structure Prediction at Atomic Resolution Using Geometric Potentials from Deep Learning (2022, methodology): “…method for RNA, as a control we also list the distance/orientation parameters taken from the predicted models by two state-of-the-art modeling methods: SimRNA 4 and Rosetta FARFAR2 5 , which have been among the most accurate automatic modeling approaches in previous RNA-Puzzles experiments 6,7,13 .”
SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction (2015, methodology): “These structures are generally very difficult to model, which is why the RNA Puzzles challenge is so important for the community of researchers working on RNA 3D structure prediction (13,40).”