Biophysics Program, Stanford University, Stanford, CA 94305, USA.
Biophysics Program, Stanford University, Stanford, CA 94305, USA; Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Physics, Stanford University, Stanford, CA 94305, USA.
Structure. 2019 Jan 2;27(1):140-151.e5. doi: 10.1016/j.str.2018.10.001. Epub 2018 Nov 8.
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.
RNA 与蛋白质复合物是许多细胞过程的基础，包括翻译、剪接以及基因表达的转录后调控。这些复合物的结构对于它们的功能至关重要，但通常难以确定其高分辨率结构。需要能够整合 RNA 与蛋白质复合物的低分辨率数据，同时对 RNA 成分在形成复合物时的大构象变化进行从头建模的计算方法。为了解决这一挑战，我们描述了 RNP-denovo，这是一种 Rosetta 方法，可以将 RNA 同时折叠并对接至蛋白质表面。在一组具有挑战性的、无法用先前策略解决的多样化 RNA 与蛋白质复合物基准集中，RNP-denovo 一致地采样到了具有优于核苷酸分辨率的天然样结构。我们重新研究了过去三个涉及剪接体、端粒酶和甲基转移酶-核糖体 RNA 复合物的盲建模挑战，其中先前的方法给出了较差的结果。当与之前使用的相同稀疏 FRET、交联和功能数据结合使用时，RNP-denovo 给出了准确性显著提高的模型。这些结果为从低分辨率数据对 RNA 与蛋白质复合物的整体折叠进行建模开辟了一条途径。
