Sampling native-like structures of RNA-protein complexes through Rosetta folding and docking
43 citations recorded by the source corpus.
RNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe a Rosetta method called RNP-denovo to simultaneously fold and dock RNA to a protein surface. On a benchmark set of structurally diverse RNA-protein complexes that are not solvable with prior strategies, this fold-and-dock method consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges in which previous methods gave poor results: human telomerase, an RNA methyltransferase with a ribosomal RNA domain, and the spliceosome. When coupled with the same sparse FRET, cross-linking, and functional data used in previous work, RNP-denovo gave models with significantly improved accuracy. These results open a route to computationally modeling global folds of RNA-protein complexes from low-resolution data.
Subfields: Molecular Biology
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In Molecular Biology
- K. Kappel
- Rhiju Das
- Daniel Herschlag
- Joseph D. Puglisi
- W.J. Greenleaf
- Jeffrey J. Gray
- David Baker
- Brian D. Weitzner
Citations in the corpus
- FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, unknown): “…blind tests of the method based on three independently solved structures of riboswitch aptamers based on cryoelectron microscopy (cryo-EM) maps (Kappel et al., 2019), an adenoviral ncRNA presented as RNA-Puzzle 24 (Hood et al., 2019), and two T-box riboswitch structures presented as…”
- Blind tests of RNA–protein binding affinity prediction (2019, background): “This limitation could be addressed by computationally predicting structures of RNA–protein complexes de novo, although existing structure prediction methods are not likely to be accurate enough for this approach (41).”
- De novo computational RNA modeling into cryoEM maps of large ribonucleoprotein complexes (2018, methodology): “This score function is available within Rosetta as rna_hires_with_protein.wts .”