The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data

By Marc-André Parisien, François Major. 2008

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In Molecular Biology

Citations in the corpus

3D RNA and Functional Interactions from Evolutionary Couplings. (2015, neutral): “Thus, despite rapid strides in 3D structure prediction accuracy for small (< 40 nt) RNAs (Cao and Chen, 2011; Das and Baker, 2007; Das et al., 2010; Frellsen et al., 2009; Parisien and Major, 2008), predicting the structure of large (> 70 nt) RNAs remains challenging (Laing and Schlick, 2010; Mia”
RNA tertiary structure modeling with BRiQ potential in CASP15 (2023, methodology): “…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 success of AlphaFold2.”
FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, neutral): “…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]).”
ModeRNA: a tool for comparative modeling of RNA 3D structure (2011, methodology): “MC-Fold/MC-Sym is another method that assembles RNA structures from a library of fragments (30).”
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 .”
ViennaRNA Package 2.0 (2011, neutral): “[18,21,61,73–79].”
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 ”
GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function (2015, methodology): “However, there exist methods such as SHEVEK (Pang et al., 2005), MC-Fold (Parisien and Major, 2008) or RNAwolf (zu Siederdissen et al., 2011) that predict other types of contacts in RNA structures, including non-canonical base pairs.”
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].”
Knowledge-based machine learning methods for macromolecular 3D structure prediction (2016, methodology): “PDB ID Length MC-Sym (h) TreeFolder (s) 1l8v 152 48 1919 2gis 94 32 564 1vc7 74 46 400 Overlay examples: Figure 4-1-11 shows three overlay examples of 1q9a, 2a43 and 1xjr with length of 27 nt, 26 nt and 49 nt, respectively.”
RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers (2020, methodology): “In RNA-Puzzles Round I (Cruz et al. 2012), the Major group tested MC-Fold/MCSym (Parisien and Major 2008), while the Das group applied their Rosetta SWA (Das 2013) algorithm.”
New metrics for comparing and assessing discrepancies between RNA 3D structures and models. (2009, methodology): “For each MC-Sym generated structure, the RMSD and INF values when compared with the crystal structure are plotted.”