Blind prediction of noncanonical RNA structure at atomic accuracy

By Andrew M. Watkins, C. Geniesse, Wipapat Kladwang, P. Zakrevsky, L. Jaeger, Rhiju Das. 2017

48 citations recorded by the source corpus.

We report a new algorithm and a battery of blind challenges for the prediction of complex RNA structures at atomic accuracy. Prediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the method’s general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-Puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

Subfields: Molecular Biology, Physical and Theoretical Chemistry

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Citations in the corpus

Using Rosetta for RNA homology modeling (2019, methodology): “1D) (Watkins et al., 2018).”
RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools (2019, methodology): “The theoretical models were generated by the best existing RNA 3D structure prediction programs (21,42–46).”
FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, unknown): “Fully automated methods that more accurately solve subproblems at the expense of more computation, such as stepwise Monte Carlo (Watkins et al., 2018), may bridge these gaps if pipelined with fragment assembly.”
Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement (2021, methodology): “The resulting re ﬁ nement technique can consistently improve model structures for the majority of near-native structural models as demonstrated by re ﬁ ning Rosetta-SWM motif 34 , RNA puzzles 17 – 19 , and FARFAR2 24 models.”
Structure and ligand binding of the glutamine-II riboswitch (2019, methodology): “The structure was therefore determined by molecular replacement using PHASER (18) and a search model derived by Rosetta homology modeling using the stepwise Monte-Carlo algorithm (24) (see ‘Materials and Methods section’).”