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• W2952396359 abstract "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 RNP-denovo, a Rosetta method to simultaneously fold-and-dock RNA to a protein surface. On a benchmark set of diverse RNA-protein complexes not solvable with prior strategies, RNP-denovo consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges involving the spliceosome, telomerase, and a methyltransferase-ribosomal RNA complex in which previous methods gave poor results. When coupled with the same sparse FRET, crosslinking, and functional data used previously, RNP-denovo gave models with significantly improved accuracy. These results open a route to modeling global folds of RNA-protein complexes from low-resolution data." @default.
• W2952396359 created "2019-06-27" @default.
• W2952396359 creator A5071548009 @default.
• W2952396359 creator A5074365776 @default.
• W2952396359 date "2019-01-01" @default.
• W2952396359 modified "2023-10-16" @default.
• W2952396359 title "Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking" @default.
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• W2952396359 doi "https://doi.org/10.1016/j.str.2018.10.001" @default.
• W2952396359 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6318048" @default.
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