Dataset Information

Sequence, Structure, and Context Preferences of Human RNA Binding Proteins.

ABSTRACT: RNA binding proteins (RBPs) orchestrate the production, processing, and function of mRNAs. Here, we present the affinity landscapes of 78 human RBPs using an unbiased assay that determines the sequence, structure, and context preferences of these proteins in vitro by deep sequencing of bound RNAs. These data enable construction of "RNA maps" of RBP activity without requiring crosslinking-based assays. We found an unexpectedly low diversity of RNA motifs, implying frequent convergence of binding specificity toward a relatively small set of RNA motifs, many with low compositional complexity. Offsetting this trend, however, we observed extensive preferences for contextual features distinct from short linear RNA motifs, including spaced "bipartite" motifs, biased flanking nucleotide composition, and bias away from or toward RNA structure. Our results emphasize the importance of contextual features in RNA recognition, which likely enable targeting of distinct subsets of transcripts by different RBPs that recognize the same linear motif.

SUBMITTER: Dominguez D

PROVIDER: S-EPMC6062212 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Sequence, Structure, and Context Preferences of Human RNA Binding Proteins.

Dominguez Daniel D Freese Peter P Alexis Maria S MS Su Amanda A Hochman Myles M Palden Tsultrim T Bazile Cassandra C Lambert Nicole J NJ Van Nostrand Eric L EL Pratt Gabriel A GA Yeo Gene W GW Graveley Brenton R BR Burge Christopher B CB

Molecular cell 20180607 5

RNA binding proteins (RBPs) orchestrate the production, processing, and function of mRNAs. Here, we present the affinity landscapes of 78 human RBPs using an unbiased assay that determines the sequence, structure, and context preferences of these proteins in vitro by deep sequencing of bound RNAs. These data enable construction of "RNA maps" of RBP activity without requiring crosslinking-based assays. We found an unexpectedly low diversity of RNA motifs, implying frequent convergence of binding ...[more]

PMID: 29883606

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Project description:BackgroundCharacterizing the binding preference of RNA-binding proteins (RBP) is essential for us to understand the interaction between an RBP and its RNA targets, and to decipher the mechanism of post-transcriptional regulation. Experimental methods have been used to generate protein-RNA binding data for a number of RBPs in vivo and in vitro. Utilizing the binding data, a couple of computational methods have been developed to detect the RNA sequence or structure preferences of the RBPs. However, the majority of RBPs have not yet been experimentally characterized and lack RNA binding data. For these poorly studied RBPs, the identification of their binding preferences cannot be performed by most existing computational methods because the experimental binding data are prerequisite to these methods.ResultsHere we propose a new method based on co-evolution to predict the sequence preferences for the poorly studied RBPs, waiving the requirement of their binding data. First, we demonstrate the co-evolutionary relationship between RBPs and their RNA partners. We then present a K-nearest neighbors (KNN) based algorithm to infer the sequence preference of an RBP using only the preference information from its homologous RBPs. By benchmarking against several in vitro and in vivo datasets, our proposed method outperforms the existing alternative which uses the closest neighbor's preference on all the datasets. Moreover, it shows comparable performance with two state-of-the-art methods that require the presence of the experimental binding data. Finally, we demonstrate the usage of this method to infer sequence preferences for novel proteins which have no binding preference information available.ConclusionFor a poorly studied RBP, the current methods used to determine its binding preference need experimental data, which is expensive and time consuming. Therefore, determining RBP's preference is not practical in many situations. This study provides an economic solution to infer the sequence preference of such protein based on the co-evolution. The source codes and related datasets are available at https://github.com/syang11/KNN .

Dataset Information

Sequence, Structure, and Context Preferences of Human RNA Binding Proteins.

Publications

Sequence, Structure, and Context Preferences of Human RNA Binding Proteins.

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