Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA.
Center for Systems Biology, University of Texas at Dallas, Richardson, TX, 75080, USA.
Nat Commun. 2018 Jun 28;9(1):2511. doi: 10.1038/s41467-018-04729-0.
RNA-protein interactions permeate biology. Transcription, translation, and splicing all hinge on the recognition of structured RNA elements by RNA-binding proteins. Models of RNA-protein interactions are generally limited to short linear motifs and structures because of the vast sequence sampling required to access longer elements. Here, we develop an integrated approach that calculates global pairwise interaction scores from in vitro selection and high-throughput sequencing. We examine four RNA-binding proteins of phage, viral, and human origin. Our approach reveals regulatory motifs, discriminates between regulated and non-regulated RNAs within their native genomic context, and correctly predicts the consequence of mutational events on binding activity. We design binding elements that improve binding activity in cells and infer mutational pathways that reveal permissive versus disruptive evolutionary trajectories between regulated motifs. These coupling landscapes are broadly applicable for the discovery and characterization of protein-RNA recognition at single nucleotide resolution.
RNA 与蛋白质的相互作用贯穿生物学。转录、翻译和剪接都依赖于 RNA 结合蛋白对结构 RNA 元件的识别。由于需要进行大量的序列采样才能获取更长的元件，因此 RNA 与蛋白质相互作用的模型通常仅限于短线性基序和结构。在这里，我们开发了一种综合方法，该方法可从体外选择和高通量测序中计算全局成对相互作用评分。我们研究了噬菌体、病毒和人类来源的四个 RNA 结合蛋白。我们的方法揭示了调控基序，区分了它们在天然基因组背景下的调节和非调节 RNA，并正确预测了突变事件对结合活性的影响。我们设计了结合元件，可提高细胞中的结合活性，并推断出突变途径，揭示调节基序之间允许与破坏的进化轨迹。这些耦合图谱可广泛应用于单核苷酸分辨率下蛋白质-RNA 识别的发现和表征。
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