Atomic accuracy in predicting and designing non-canonical RNA structure

By Rhiju Das, John Karanicolas, David Baker. 2010

363 citations recorded by the source corpus.

We present fragment assembly of RNA with full-atom refinement (FARFAR), a Rosetta framework for predicting and designing noncanonical motifs that define RNA tertiary structure. In a test set of thirty-two 6–20-nucleotide motifs, FARFAR recapitulated 50% of the experimental structures at near-atomic accuracy. Sequence redesign calculations recovered native bases at 65% of residues engaged in noncanonical interactions, and we experimentally validated mutations predicted to stabilize a signal recognition particle domain.

Subfields: Molecular Biology

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Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, background): “The first was a carbon-H-bond potential added to Rosetta(butnotincludedaspartof Score12 )tomodelRNA(Das,Karanicolas, & Baker, 2010).”
Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules (2011, positive): “Design of RNA using ROSETTA 3 has already been tested (Das et al. , 2010).”
RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures (2015, methodology): “…in the Rosetta framework (Leaver-Fay et al. 2011) were created for threading structural templates into the desired sequence refinement (FARFAR) (Das et al. 2010; Kladwang et al. 2011a), whose interface for job setup was previously cumbersome, especially to solve subpieces of large RNAs (e.g.,…”
De Novo RNA Tertiary Structure Prediction at Atomic Resolution Using Geometric Potentials from Deep Learning (2022, background): “Last, Puzzle 12 is a medium-size (108 nucleotides) ydaO riboswitch (PDB ID: 4qlm) with a novel structural topology that contains two binding pockets for cyclic-di-AMP 25 .”
RNA design rules from a massive open laboratory (2014, neutral): “S11 C and D ) (37).”
Blind prediction of noncanonical RNA structure at atomic accuracy (2017, background): “Perhaps the most striking models were recovered for multi-helix junctions and tertiary contacts, whichhave largely eludedRNAmodeling efforts seeking high resolution (6, 8).”
SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction (2015, methodology): “While we intend to improve SimRNA with respect to this type of problems, it may be useful to consider the use of independent methods such as RMDetect (45) or JAR3D (46 ) to predict local structured motifs before the folding, or to use lo-cal high-resolution resampling; e.g., with FARFAR (39 ) after ”
Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement (2021, methodology): “Comparison to FARFAR2 .”
An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling (2011, methodology): “The canonical base pairs at the edges of the motif were assumed to be known a priori (2) and were built using idealized geometry (12).”
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; Miao…”
Automated 3D structure composition for large RNAs (2012, methodology): “Automated methods make use of the coarse-grained and atomic-level molecular dynamics (15–17), internal coordinate space dynamics (18,19), fragment assembly (20,21) and comparative modelling using templates (22).”