Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made-particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.

/pdf/string-v9-1-protein-protein-interaction-networks-with-42hnvufk2o.pdf

STRING v9.1: protein-protein interaction networks, with increased coverage and integration

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Interactome networks and human disease

Precursor messenger RNA (pre-mRNA) splicing is a critical step in the posttranscriptional regulation of gene expression, providing significant expansion of the functional proteome of eukaryotic organisms with limited gene numbers. Split eukaryotic genes contain intervening sequences or introns disrupting protein-coding exons, and intron removal occurs by repeated assembly of a large and highly dynamic ribonucleoprotein complex termed the spliceosome, which is composed of five small nuclear ribonucleoprotein particles, U1, U2, U4/U6, and U5. Biochemical studies over the past 10 years have allowed the isolation as well as compositional, functional, and structural analysis of splicing complexes at distinct stages along the spliceosome cycle. The average human gene contains eight exons and seven introns, producing an average of three or more alternatively spliced mRNA isoforms. Recent high-throughput sequencing studies indicate that 100% of human genes produce at least two alternative mRNA isoforms. Mechanisms of alternative splicing include RNA-protein interactions of splicing factors with regulatory sites termed silencers or enhancers, RNA-RNA base-pairing interactions, or chromatin-based effects that can change or determine splicing patterns. Disease-causing mutations can often occur in splice sites near intron borders or in exonic or intronic RNA regulatory silencer or enhancer elements, as well as in genes that encode splicing factors. Together, these studies provide mechanistic insights into how spliceosome assembly, dynamics, and catalysis occur; how alternative splicing is regulated and evolves; and how splicing can be disrupted by cis- and trans-acting mutations leading to disease states. These findings make the spliceosome an attractive new target for small-molecule, antisense, and genome-editing therapeutic interventions.

/pdf/mechanisms-and-regulation-of-alternative-pre-mrna-splicing-30gwnxjpdd.pdf

Mechanisms and Regulation of Alternative Pre-mRNA Splicing

One of the most amazing findings in molecular biology was the discovery that eukaryotic genes are discontinuous, with coding DNA being interrupted by stretches of non-coding sequence. The subsequent realization that the intervening regions are removed from pre-mRNA transcripts via the activity of a common set of small nuclear RNAs (snRNAs), which assemble together with associated proteins into a complex known as the spliceosome, was equally surprising. How do cells coordinate the assembly of this molecular machine? And how does the spliceosome accurately recognize exons and introns to carry out the splicing reaction? Insights into these questions have been gained by studying the life cycle of spliceosomal snRNAs from their transcription, nuclear export and re-import to their dynamic assembly into the spliceosome. This assembly process can also affect the regulation of alternative splicing and has implications for human disease.

/pdf/a-day-in-the-life-of-the-spliceosome-1cdz9j2725.pdf

A day in the life of the spliceosome

Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

/pdf/the-consensuspathdb-interaction-database-2013-update-v69jmcrxvy.pdf

The ConsensusPathDB interaction database: 2013 update

Dynamic Protein-Protein Interaction Wiring of the Human Spliceosome

Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes.

https://www.embopress.org/doi/pdf/10.15252/msb.20145968

Phospho‐tyrosine dependent protein–protein interaction network

https://publications.mpi-cbg.de/Elefsinioti_2011_4614.pdf

Large-scale de novo prediction of physical protein-protein association

The yeast two-hybrid (Y2H) system is currently one of the most important techniques for protein-protein interaction (PPI) discovery. Here, we describe a stringent three-step Y2H matrix interaction approach that is suitable for systematic PPI screening on a proteome scale. We start with the identification and elimination of autoactivating strains that would lead to false-positive signals and prevent the identification of interactions. Nonautoactivating strains are used for the primary PPI screen that is carried out in quadruplicate with arrayed preys. Interacting pairs of baits and preys are identified in a pairwise retest step. Only PPI pairs that pass the retest step are regarded as potentially biologically relevant interactions and are considered for further analysis.

/pdf/a-stringent-yeast-two-hybrid-matrix-screening-approach-for-4wse2uuv6h.pdf

Anna Hegele

Papers

Dynamic Protein-Protein Interaction Wiring of the Human Spliceosome

Phospho‐tyrosine dependent protein–protein interaction network

Large-scale de novo prediction of physical protein-protein association

A stringent yeast two-hybrid matrix screening approach for protein-protein interaction discovery.