Abstract
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP–RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions. PrismNet results for 168 RBPs support its utility for both understanding CLIP-seq results and largely extending such interaction data to accurately analyze additional cell types. Further, PrismNet employs an “attention” strategy to computationally identify exact RBP-binding nucleotides, and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants (riboSNitches), which can link genetic diseases with dysregulated RBP bindings. Our rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP–RNA interactions, with clear utility for understanding and treating human diseases.
Highlights
RNA binding proteins (RBPs) play essential roles in regulating the transcription, metabolism, and translation of cellular RNAs.[1,2,3,4] Determining RBP binding profiles in different conditions and elucidating their detailed regulatory mechanisms are critical for understanding their functions
To characterize relationships between RNA structure and RBP binding globally, we generated a comprehensive resource of RNA secondary structures determined by icSHAPE in seven cell types: K562, HepG2, HEK293, HEK 293T, HeLa, H9, and mouse embryonic stem cell (mES) cells (Fig. 1a; Supplementary information, Fig. S1a)
RNA structure-disruptive variants (RiboSNitches) are associated with dynamic RBP binding and disease To further investigate the relationship between mutations in the predicted high attention regions (HARs) and human disease, we focused on riboSNitches, a special class of SNPs or SNVs in which different alleles exhibit distinct foci RNA structures[81,82] (Supplementary information, Fig. S7a)
Summary
RNA binding proteins (RBPs) play essential roles in regulating the transcription, metabolism, and translation of cellular RNAs.[1,2,3,4] Determining RBP binding profiles in different conditions and elucidating their detailed regulatory mechanisms are critical for understanding their functions. Given the sheer number of RBPs that account for close to 10% of the human proteome,[5,6] establishing links between RBPs and their targets has been an enormous challenge. To address this question, many highthroughput technologies have been developed to profile and predict RBP binding. Many highthroughput technologies have been developed to profile and predict RBP binding Assays such as systematic evolution of ligands by exponential selection (SELEX), RNAcompete, and RNA. Bind-n-Seq can characterize the sequence preferences of RBPs in vitro,[7,8,9] and methods like RNA immunoprecipitation (RIP) and UV crosslinking followed by immunoprecipitation (CLIP) and sequencing can identify RBP binding sites in vivo.[10,11,12,13]
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