Refining the pool of RNA-binding domains advances the classification and prediction of RNA-binding proteins.

From transcription to decay, RNA-binding proteins (RBPs) influence RNA metabolism. Using the RBP2GO database that combines proteome-wide RBP screens from 13 species, we investigated the RNA-binding features of 176 896 proteins. By compiling published lists of RNA-binding domains (RBDs) and RNA-related protein family (Rfam) IDs with lists from the InterPro database, we analyzed the distribution of the RBDs and Rfam IDs in RBPs and non-RBPs to select RBDs and Rfam IDs that were enriched in RBPs. We also explored proteins for their content in intrinsically disordered regions (IDRs) and low complexity regions (LCRs). We found a strong positive correlation between IDRs and RBDs and a co-occurrence of specific LCRs. Our bioinformatic analysis indicated that RBDs/Rfam IDs were strong indicators of the RNA-binding potential of proteins and helped predicting new RBP candidates, especially in less investigated species. By further analyzing RBPs without RBD, we predicted new RBDs that were validated by RNA-bound peptides. Finally, we created the RBP2GO composite score by combining the RBP2GO score with new quality factors linked to RBDs and Rfam IDs. Based on the RBP2GO composite score, we compiled a list of 2018 high-confidence human RBPs. The knowledge collected here was integrated into the RBP2GO database at https://RBP2GO-2-Beta.dkfz.de.

Proteome-Wide Identification of RNA-Dependent Proteins in Lung Cancer Cells.

Following the concept of RNA dependence and exploiting its application in the R-DeeP screening approach, we have identified RNA-dependent proteins in A549 lung adenocarcinoma cells. RNA-dependent proteins are defined as proteins whose interactome depends on RNA and thus entails RNA-binding proteins (RBPs) as well as proteins in ribonucleoprotein complexes (RNPs) without direct RNA interaction. With this proteome-wide technique based on sucrose density gradient ultracentrifugation and fractionation followed by quantitative mass spectrometry and bioinformatic analysis, we have identified 1189 RNA-dependent proteins including 170 proteins which had never been linked to RNA before. R-DeeP provides quantitative information on the fraction of a protein being RNA-dependent as well as it allows the reconstruction of protein complexes based on co-segregation. The RNA dependence of three newly identified RNA-dependent proteins, DOCK5, ELMO2, also known as CED12A, and ABRAXAS1, also known as CCDC98, was validated using western blot analysis, and the direct RNA interaction was verified by iCLIP2 for the migration-related protein DOCK5 and the mitosis-related protein ABRAXAS1. The R-DeeP 2.0 database provides proteome-wide and cell line-specific information from A549 and HeLa S3 cells on proteins and their RNA dependence to contribute to understanding the functional role of RNA and RNA-binding proteins in cancer cells.

RBP2GO: a comprehensive pan-species database on RNA-binding proteins, their interactions and functions.

RNA-protein complexes have emerged as central players in numerous key cellular processes with significant relevance in health and disease. To further deepen our knowledge of RNA-binding proteins (RBPs), multiple proteome-wide strategies have been developed to identify RBPs in different species leading to a large number of studies contributing experimentally identified as well as predicted RBP candidate catalogs. However, the rapid evolution of the field led to an accumulation of isolated datasets, hampering the access and comparison of their valuable content. Moreover, tools to link RBPs to cellular pathways and functions were lacking. Here, to facilitate the efficient screening of the RBP resources, we provide RBP2GO (https://RBP2GO.DKFZ.de), a comprehensive database of all currently available proteome-wide datasets for RBPs across 13 species from 53 studies including 105 datasets identifying altogether 22 552 RBP candidates. These are combined with the information on RBP interaction partners and on the related biological processes, molecular functions and cellular compartments. RBP2GO offers a user-friendly web interface with an RBP scoring system and powerful advanced search tools allowing forward and reverse searches connecting functions and RBPs to stimulate new research directions.

Identification, quantification and bioinformatic analysis of RNA-dependent proteins by RNase treatment and density gradient ultracentrifugation using R-DeeP.

Analysis of RNA-protein complexes is central to understanding the molecular circuitry governing cellular processes. In recent years, several proteome-wide studies have been dedicated to the identification of RNA-binding proteins. Here, we describe in detail R-DeeP, an approach built on RNA dependence, defined as the ability of a protein to engage in protein complexes only in the presence of RNA, involving direct or indirect interaction with RNA. This approach provides-for the first time, to our knowledge-quantitative information on the fraction of a protein associated with RNA-protein complexes. R-DeeP is independent of any potentially biased purification procedures. It is based on cellular lysate fractionation by density gradient ultracentrifugation and subsequent analysis by proteome-wide mass spectrometry (MS) or individual western blotting. The comparison of lysates with and without previous RNase treatment enables the identification of differences in the apparent molecular weight and, hence, the size of the complexes. In combination with information from databases of protein-protein complexes, R-DeeP facilitates the computational reconstruction of protein complexes from proteins migrating in the same fraction. In addition, we developed a pipeline for the statistical analysis of the MS dataset to automatically identify RNA-dependent proteins (proteins whose interactome depends on RNA). With this protocol, the individual analysis of proteins of interest by western blotting can be completed within 1-2 weeks. For proteome-wide studies, additional time is needed for the integration of the proteomic and statistical analyses. In the future, R-DeeP can be extended to other fractionation techniques, such as chromatography.