Molecular interaction networks in the analyses of sequence variation and proteomics data

Protein-protein interaction networks are typically generated in standard cell lines or model organisms as it is prohibitively difficult to record large interaction datasets from specific tissues or disease models at a reasonable pace. Although the interaction data are of high confidence, they thus do not reflect in vivo relationships as such. A wealth of physiologically relevant protein information, obtained under different conditions and from different systems, is available including information on genetic variation, protein levels, and PTMs. However, these data are difficult to assess comprehensively because the relationships between the entities remain elusive from the measurements. Here, we exemplarily highlight recent studies that gained deeper insight from genetic variation, protein, and PTM measurements using interaction information pointing toward the importance and potential of interaction networks for the interpretation of sequencing and proteomics data

Cluster-based assessment of protein-protein interaction confidence

Background: Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity. Results: We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring. Conclusions: On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at http://intscore.molgen.mpg.d

Identification and characterization of BATF3 as a context-specific coactivator of the glucocorticoid receptor

The ability of the glucocorticoid receptor (GR) to regulate the transcriptional output of genes relies on its interactions with transcriptional coregulators. However, which coregulators are required for GR-dependent activation is context-dependent and can be influenced by the sequence of the DNA bound by GR and by the nature of the GR isoform responsible for the regulation of a gene. Here, we screened for GR-interacting proteins for which the interaction signal differed between two GR isoforms GRalpha and GRgamma. These isoforms diverge by a single amino acid insertion in a domain, the lever arm, which adopts DNA sequence-specific conformations. We identify Basic Leucine Zipper ATF-Like Transcription Factor 3 (BATF3), an AP-1 family transcription factor, as a GR coregulator whose interaction with GR is modulated by the lever arm. Further, a combination of experiments uncovered that BATF3 acts as a gene-specific coactivator of GR whose coactivator potency is influenced by the sequence of the GR binding site. Together, our findings suggest that GR isoform and the sequence of GR binding site influence the interaction of GR with BATF3, which might direct the assembly of gene-specific regulatory complexes to fine-tune the expression of individual GR target genes

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

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

Protein interaction perturbation profiling at amino-acid resolution

The identification of genomic variants in healthy and diseased individuals continues to rapidly outpace our ability to functionally annotate these variants. Techniques that both systematically assay the functional consequences of nucleotide-resolution variation and can scale to hundreds of genes are urgently required. We designed a sensitive yeast two-hybrid-based 'off switch' for positive selection of interaction-disruptive variants from complex genetic libraries. Combined with massively parallel programmed mutagenesis and a sequencing readout, this method enables systematic profiling of protein-interaction determinants at amino-acid resolution. We defined >1,000 interaction-disrupting amino acid mutations across eight subunits of the BBSome, the major human cilia protein complex associated with the pleiotropic genetic disorder Bardet–Biedl syndrome. These high-resolution interaction-perturbation profiles provide a framework for interpreting patient-derived mutations across the entire protein complex and thus highlight how the impact of disease variation on interactome networks can be systematically assessed

A noncanonical PWI domain in the N-terminal helicase-associated region of the spliceosomal Brr2 protein

The spliceosomal RNA helicase Brr2 is required for the assembly of a catalytically active spliceosome on a messenger RNA precursor. Brr2 exhibits an unusual organization with tandem helicase units, each comprising dual RecA-like domains and a Sec63 homology unit, preceded by a more than 400-residue N-terminal helicase-associated region. Whereas recent crystal structures have provided insights into the molecular architecture and regulation of the Brr2 helicase region, little is known about the structural organization and function of its N-terminal part. Here, a near-atomic resolution crystal structure of a PWI-like domain that resides in the N-terminal region of Chaetomium thermophilum Brr2 is presented. CD spectroscopic studies suggested that this domain is conserved in the yeast and human Brr2 orthologues. Although canonical PWI domains act as low-specificity nucleic acid-binding domains, no significant affinity of the unusual PWI domain of Brr2 for a broad spectrum of DNAs and RNAs was detected in band-shift assays. Consistently, the C. thermophilum Brr2 PWI-like domain, in the conformation seen in the present crystal structure, lacks an expanded positively charged surface patch as observed in at least one canonical, nucleic acid-binding PWI domain. Instead, in a comprehensive yeast two-hybrid screen against human spliceosomal proteins, fragments of the N-terminal region of human Brr2 were found to interact with several other spliceosomal proteins. At least one of these interactions, with the Prp19 complex protein SPF27, depended on the presence of the PWI-like domain. The results suggest that the N-terminal region of Brr2 serves as a versatile protein-protein interaction platform in the spliceosome and that some interactions require or are reinforced by the PWI-like domain

The ConsensusPathDB interaction database: 2013 update

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

A Y2H-seq approach defines the human protein methyltransferase interactome

To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation