David Lyon

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

The posttranslational modification of proteins by arginine methylation is functionally important, yet the breadth of this modification is not well characterized. Using high-resolution mass spectrometry, we identified 8030 arginine methylation sites within 3300 human proteins in human embryonic kidney 293 cells, indicating that the occurrence of this modification is comparable to phosphorylation and ubiquitylation. A site-level conservation analysis revealed that arginine methylation sites are less evolutionarily conserved compared to arginines that were not identified as modified by methylation. Through quantitative proteomics and RNA interference to examine arginine methylation stoichiometry, we unexpectedly found that the protein arginine methyltransferase (PRMT) family of arginine methyltransferases catalyzed methylation independently of arginine sequence context. In contrast to the frequency of somatic mutations at arginine methylation sites throughout the proteome, we observed that somatic mutations were common at arginine methylation sites in proteins involved in mRNA splicing. Furthermore, in HeLa and U2OS cells, we found that distinct arginine methyltransferases differentially regulated the functions of the pre-mRNA splicing factor SRSF2 (serine/arginine-rich splicing factor 2) and the RNA transport ribonucleoprotein HNRNPUL1 (heterogeneous nuclear ribonucleoprotein U-like 1). Knocking down PRMT5 impaired the RNA binding function of SRSF2, whereas knocking down PRMT4 [also known as coactivator-associated arginine methyltransferase 1 (CARM1)] or PRMT1 increased the RNA binding function of HNRNPUL1. High-content single-cell imaging additionally revealed that knocking down CARM1 promoted the nuclear accumulation of SRSF2, independent of cell cycle phase. Collectively, the presented human arginine methylome provides a missing piece in the global and integrative view of cellular physiology and protein regulation.