An experimentally derived confidence score for binary protein-protein interactions

Prediction of virus-host protein-protein interactions mediated by short linear motifs

BACKGROUND

Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1).

RESULTS

We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions.

CONCLUSION

We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.

Proteome-Scale Human Interactomics

Cellular functions are mediated by complex interactome networks of physical, biochemical, and functional interactions between DNA sequences, RNA molecules, proteins, lipids, and small metabolites. A thorough understanding of cellular organization requires accurate and relatively complete models of interactome networks at proteome scale. The recent publication of four human protein-protein interaction (PPI) maps represents a technological breakthrough and an unprecedented resource for the scientific community, heralding a new era of proteome-scale human interactomics. Our knowledge gained from these and complementary studies provides fresh insights into the opportunities and challenges when analyzing systematically generated interactome data, defines a clear roadmap towards the generation of a first reference interactome, and reveals new perspectives on the organization of cellular life.

LUMIER: A Discovery Tool for Mammalian Protein Interaction Networks

Protein-protein interactions (PPIs) play an essential role in all biological processes. In vivo, PPIs occur dynamically and depend on extracellular cues. To discover novel protein-protein interactions in mammalian cells, we developed a high-throughput automated technology called LUMIER (LUminescence-based Mammalian IntERactome). In this approach, we co-express a Luciferase (LUC)-tagged fusion protein along with a Flag-tagged protein in an efficiently transfectable cell line such as HEK-293T cells. The interaction between the two proteins is determined by co-immunoprecipitation using an anti-Flag antibody, and the presence of the LUC-tagged interactor in the complex is subsequently detected via its luciferase activity. LUMIER can easily detect transmembrane protein partners, interactions that are signaling- or splice isoform-dependent, as well as those that may occur only in the presence of posttranslational modifications. Using various collections of Flag-tagged proteins, we have generated protein interaction networks for several TGF-β family receptors, Wnt pathway members, and have systematically analyzed the effect of neural-specific alternative splicing on protein interaction networks. The results have provided important insights into the physiological and functional relevance of some of the novel interactions found. LUMIER is highly scalable and can be used for both low- and high-throughput strategies. LUMIER is thus a valuable tool for the identification and characterization of dynamically regulated PPIs in mammalian systems. Here, we describe a manual version of LUMIER in a 96-well format that can be easily implemented in any laboratory.

Mapping Protein-Protein Interaction Using High-Throughput Yeast 2-Hybrid

A tremendous asset to the analysis of protein-protein interactions is the yeast-2-hybrid (Y2H) method. The Y2H assay is a heterologous system that is expanding network biology knowledge via in vivo investigations of binary protein-protein interactions. Traditionally, the Y2H protocol entails the mating or co-transformation of yeast in solid agar media followed by visual analysis for diploid selection. Having played a key role in identifying protein-protein interactions for nearly three decades in a wide range of biological systems, the Y2H system assays the interaction between two proteins of interest which results in a reconstituted and/or activation of transcription factor allowing a reporter gene to be transcribed. Overall, the Y2H method takes advantage of two factors: (1) the auxotrophic yeast requires expression of the reporter gene to grow in media purposefully designed to lack one or more essential amino acids, and (2) the DNA-binding (DB) domain of transcription factor GAL4 is unable to initiate transcription unless it is physically associated with an activating domain (AD), which, together, DBs and ADs are fused to proteins of interest that must interact with each other to reconstitute the transcription factor and activate the reporter gene. The applications of Y2H are broad, entailing fields such as drug discovery, clinical trials for human disease including cancer and neurodegenerative disease, and extend even into synthetic biology applications and cellular engineering. This chapter begins with an introduction to the fundamental mechanics of Y2H utilizing a genetically engineered strain of yeast and proceeds with an in-depth look at the different types of Y2H and turn our focus particularly to the GAL4-based Y2H system to map protein-protein interactions. We will then provide a step-by-step protocol for the Y2H experimentation preceded by a materials listing while simultaneously including key notes throughout the entire experimental process of biological-mechanistic and historical understandings of the steps.

Diverse mechanisms of metaeffector activity in an intracellular bacterial pathogen, Legionella pneumophila

Pathogens deliver complex arsenals of translocated effector proteins to host cells during infection, but the extent to which these proteins are regulated once inside the eukaryotic cell remains poorly defined. Among all bacterial pathogens, Legionella pneumophila maintains the largest known set of translocated substrates, delivering over 300 proteins to the host cell via its Type IVB, Icm/Dot translocation system. Backed by a few notable examples of effector–effector regulation in L. pneumophila, we sought to define the extent of this phenomenon through a systematic analysis of effector–effector functional interaction. We used Saccharomyces cerevisiae, an established proxy for the eukaryotic host, to query > 108,000 pairwise genetic interactions between two compatible expression libraries of ~330 L. pneumophila‐translocated substrates. While capturing all known examples of effector–effector suppression, we identify fourteen novel translocated substrates that suppress the activity of other bacterial effectors and one pair with synergistic activities. In at least nine instances, this regulation is direct—a hallmark of an emerging class of proteins called metaeffectors, or “effectors of effectors”. Through detailed structural and functional analysis, we show that metaeffector activity derives from a diverse range of mechanisms, shapes evolution, and can be used to reveal important aspects of each cognate effector's function. Metaeffectors, along with other, indirect, forms of effector–effector modulation, may be a common feature of many intracellular pathogens—with unrealized potential to inform our understanding of how pathogens regulate their interactions with the host cell.

Harnessing molecular motors for nanoscale pulldown in live cells

Nanoscale pulldown (NanoSPD) miniaturizes the concept of affinity pulldown to detect protein–protein interactions in live cells. NanoSPD hijacks the myosin-based intracellular trafficking machinery to assess interactions under physiological buffer conditions and is microscopy-based, allowing for sensitive detection and quantification.

Protein–protein interactions (PPIs) regulate assembly of macromolecular complexes, yet remain challenging to study within the native cytoplasm where they normally exert their biological effect. Here we miniaturize the concept of affinity pulldown, a gold-standard in vitro PPI interrogation technique, to perform nanoscale pulldowns (NanoSPDs) within living cells. NanoSPD hijacks the normal process of intracellular trafficking by myosin motors to forcibly pull fluorescently tagged protein complexes along filopodial actin filaments. Using dual-color total internal reflection fluorescence microscopy, we demonstrate complex formation by showing that bait and prey molecules are simultaneously trafficked and actively concentrated into a nanoscopic volume at the tips of filopodia. The resulting molecular traffic jams at filopodial tips amplify fluorescence intensities and allow PPIs to be interrogated using standard epifluorescence microscopy. A rigorous quantification framework and software tool are provided to statistically evaluate NanoSPD data sets. We demonstrate the capabilities of NanoSPD for a range of nuclear and cytoplasmic PPIs implicated in human deafness, in addition to dissecting these interactions using domain mapping and mutagenesis experiments. The NanoSPD methodology is extensible for use with other fluorescent molecules, in addition to proteins, and the platform can be easily scaled for high-throughput applications.

Proteome-scale Binary Interactomics in Human Cells

Because proteins are the main mediators of most cellular processes they are also prime therapeutic targets. Identifying physical links among proteins and between drugs and their protein targets is essential in order to understand the mechanisms through which both proteins themselves and the molecules they are targeted with act. Thus, there is a strong need for sensitive methods that enable mapping out these biomolecular interactions. Here we present a robust and sensitive approach to screen proteome-scale collections of proteins for binding to proteins or small molecules using the well validated MAPPIT (Mammalian Protein-Protein Interaction Trap) and MASPIT (Mammalian Small Molecule-Protein Interaction Trap) assays. Using high-density reverse transfected cell microarrays, a close to proteome-wide collection of human ORF clones can be screened for interactors at high throughput. The versatility of the platform is demonstrated through several examples. With MAPPIT, we screened a 15k ORF library for binding partners of RNF41, an E3 ubiquitin protein ligase implicated in receptor sorting, identifying known and novel interacting proteins. The potential related to the fact that MAPPIT operates in living human cells is illustrated in a screen where the protein collection is scanned for interactions with the glucocorticoid receptor (GR) in its unliganded versus dexamethasone-induced activated state. Several proteins were identified the interaction of which is modulated upon ligand binding to the GR, including a number of previously reported GR interactors. Finally, the screening technology also enables detecting small molecule target proteins, which in many drug discovery programs represents an important hurdle. We show the efficiency of MASPIT-based target profiling through screening with tamoxifen, a first-line breast cancer drug, and reversine, an investigational drug with interesting dedifferentiation and antitumor activity. In both cases, cell microarray screens yielded known and new potential drug targets highlighting the utility of the technology beyond fundamental biology.

A post-decoding re-ranking algorithm for predicting interacting residues in proteins with hidden Markov models incorporating long-distance information

Protein-protein interactions play a central role in the biological processes of cells. Accurate prediction of the interacting residues in protein-protein interactions enhances understanding of the interaction mechanisms and enables in silico mutagenesis, which can help facilitate drug design and deepen our understanding of the inner workings of cells. Correlations have been found among interacting residues as a result of selection pressure to retain the interaction during evolution. In previous work, incorporation of such correlations in the interaction profile hidden Markov models with a special decoding algorithm (ETB-Viterbi) has led to improvement in prediction accuracy. In this work, we first demonstrated the sub-optimality of the ETB-Viterbi algorithm, and then reformulated the optimality of decoding paths to include correlations between interacting residues. To identify optimal decoding paths, we propose a post-decoding re-ranking algorithm based on a genetic algorithm with simulated annealing and show that the new method gains an increase of near 14% in prediction accuracy over the ETB-Viterbi algorithm.

The RNF146 and tankyrase pathway maintains the junctional Crumbs complex through regulation of angiomotin

The Crumbs complex is an important determinant of epithelial apical-basal polarity that functions in regulation of tight junctions, resistance to epithelial-to-mesenchymal transitions and as a tumour suppressor. Although the functional role of the Crumbs complex is being elucidated, its regulation is poorly understood. Here, we show that suppression of RNF146, an E3 ubiquitin ligase that recognizes ADP-ribosylated substrates, and tankyrase, a poly(ADP-ribose) polymerase, disrupts the junctional Crumbs complex and disturbs the function of tight junctions. We show that RNF146 binds a number of polarity-associated proteins, in particular members of the angiomotin (AMOT) family. Accordingly, AMOT proteins are ADP-ribosylated by TNKS2, which drives ubiquitylation by RNF146 and subsequent degradation. Ablation of RNF146 or tankyrase, as well as overexpression of AMOT, led to the relocation of PALS1 (a Crumbs complex component) from the apical membrane to internal puncta, a phenotype that is rescued by AMOTL2 knockdown. We thus reveal a new function of RNF146 and tankyrase in stabilizing the Crumbs complex through downregulation of AMOT proteins at the apical membrane.

Mapping transcription factor interactome networks using HaloTag protein arrays

Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein-protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor-protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.

An extra dimension in protein tagging by quantifying universal proteotypic peptides using targeted proteomics

The use of protein tagging to facilitate detailed characterization of target proteins has not only revolutionized cell biology, but also enabled biochemical analysis through efficient recovery of the protein complexes wherein the tagged proteins reside. The endogenous use of these tags for detailed protein characterization is widespread in lower organisms that allow for efficient homologous recombination. With the recent advances in genome engineering, tagging of endogenous proteins is now within reach for most experimental systems, including mammalian cell lines cultures. In this work, we describe the selection of peptides with ideal mass spectrometry characteristics for use in quantification of tagged proteins using targeted proteomics. We mined the proteome of the hyperthermophile Pyrococcus furiosus to obtain two peptides that are unique in the proteomes of all known model organisms (proteotypic) and allow sensitive quantification of target proteins in a complex background. By combining these 'Proteotypic peptides for Quantification by SRM' (PQS peptides) with epitope tags, we demonstrate their use in co-immunoprecipitation experiments upon transfection of protein pairs, or after introduction of these tags in the endogenous proteins through genome engineering. Endogenous protein tagging for absolute quantification provides a powerful extra dimension to protein analysis, allowing the detailed characterization of endogenous proteins.

Trapping mammalian protein complexes in viral particles

Cell lysis is an inevitable step in classical mass spectrometry–based strategies to analyse protein complexes. Complementary lysis conditions, in situ cross-linking strategies and proximal labelling techniques are currently used to reduce lysis effects on the protein complex. We have developed Virotrap, a viral particle sorting approach that obviates the need for cell homogenization and preserves the protein complexes during purification. By fusing a bait protein to the HIV-1 GAG protein, we show that interaction partners become trapped within virus-like particles (VLPs) that bud from mammalian cells. Using an efficient VLP enrichment protocol, Virotrap allows the detection of known binary interactions and MS-based identification of novel protein partners as well. In addition, we show the identification of stimulus-dependent interactions and demonstrate trapping of protein partners for small molecules. Virotrap constitutes an elegant complementary approach to the arsenal of methods to study protein complexes.

A large portion of the proteome carries out its cellular function as part of macromolecular complexes. Here the authors describe Virotrap, a novel lysis-free approach for the isolation and identification of biologically relevant protein-protein and small molecule-protein interactions.

The interactome of Streptococcus pneumoniae and its bacteriophages show highly specific patterns of interactions among bacteria and their phages

Although an abundance of bacteriophages exists, little is known about interactions between their proteins and those of their bacterial hosts. Here, we experimentally determined the phage-host interactomes of the phages Dp-1 and Cp-1 and their underlying protein interaction network in the host Streptococcus pneumoniae. We compared our results to the interaction patterns of E. coli phages lambda and T7. Dp-1 and Cp-1 target highly connected host proteins, occupy central network positions, and reach many protein clusters through the interactions of their targets. In turn, lambda and T7 targets cluster to conserved and essential proteins in E. coli, while such patterns were largely absent in S. pneumoniae. Furthermore, targets in E. coli were mutually strongly intertwined, while targets of Dp-1 and Cp-1 were strongly connected through essential and orthologous proteins in their immediate network vicinity. In both phage-host systems, the impact of phages on their protein targets appears to extend from their network neighbors, since proteins that interact with phage targets were located in central network positions, have a strong topologically disruptive effect and touch complexes with high functional heterogeneity. Such observations suggest that the phages, biological impact is accomplished through a surprisingly limited topological reach of their targets.

Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions

Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.

Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon

Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.

Bacterial Interactomes: Interacting Protein Partners Share Similar Function and Are Validated in Independent Assays More Frequently Than Previously Reported

Numerous affinity purification-mass spectrometry (AP-MS) and yeast two-hybrid screens have each defined thousands of pairwise protein-protein interactions (PPIs), most of which are between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here, we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial yeast two-hybrid and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli Compared with the nine published interactomes, our two networks are smaller, are much less highly connected, and have significantly lower false discovery rates. In addition, our interactomes are much more enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays than the pairs reported in prior studies. Our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.

A combined binary interaction and phenotypic map of C. elegans cell polarity proteins

The establishment of cell polarity is an essential process for the development of multicellular organisms and the functioning of cells and tissues. Here, we combine large-scale protein interaction mapping with systematic phenotypic profiling to study the network of physical interactions that underlies polarity establishment and maintenance in the nematode Caenorhabditis elegans. Using a fragment-based yeast two-hybrid strategy, we identified 439 interactions between 296 proteins, as well as the protein regions that mediate these interactions. Phenotypic profiling of the network resulted in the identification of 100 physically interacting protein pairs for which RNAi-mediated depletion caused a defect in the same polarity-related process. We demonstrate the predictive capabilities of the network by showing that the physical interaction between the RhoGAP PAC-1 and PAR-6 is required for radial polarization of the C. elegans embryo. Our network represents a valuable resource of candidate interactions that can be used to further our insight into cell polarization.

A Computational Network Biology Approach to Uncover Novel Genes Related to Alzheimer's Disease

Recent advances in the fields of genetics and genomics have enabled the identification of numerous Alzheimer's disease (AD) candidate genes, although for many of them the role in AD pathophysiology has not been uncovered yet. Concomitantly, network biology studies have shown a strong link between protein network connectivity and disease. In this chapter I describe a computational approach that, by combining local and global network analysis strategies, allows the formulation of novel hypotheses on the molecular mechanisms involved in AD and prioritizes candidate genes for further functional studies.

Identifying communities from multiplex biological networks

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi).

Fundamentals of protein interaction network mapping

Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.

An extracellular biochemical screen reveals that FLRTs and Unc5s mediate neuronal subtype recognition in the retina

In the inner plexiform layer (IPL) of the mouse retina, ~70 neuronal subtypes organize their neurites into an intricate laminar structure that underlies visual processing. To find recognition proteins involved in lamination, we utilized microarray data from 13 subtypes to identify differentially-expressed extracellular proteins and performed a high-throughput biochemical screen. We identified ~50 previously-unknown receptor-ligand pairs, including new interactions among members of the FLRT and Unc5 families. These proteins show laminar-restricted IPL localization and induce attraction and/or repulsion of retinal neurites in culture, placing them in an ideal position to mediate laminar targeting. Consistent with a repulsive role in arbor lamination, we observed complementary expression patterns for one interaction pair, FLRT2-Unc5C, in vivo. Starburst amacrine cells and their synaptic partners, ON-OFF direction-selective ganglion cells, express FLRT2 and are repelled by Unc5C. These data suggest a single molecular mechanism may have been co-opted by synaptic partners to ensure joint laminar restriction.

Studying Cellular Signal Transduction with OMIC Technologies

In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.

Characterization of known protein complexes using k-connectivity and other topological measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms

BACKGROUND

In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary.

METHODS

Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs).

RESULTS

The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome.

CONCLUSION

In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.

DULIP: A Dual Luminescence-Based Co-Immunoprecipitation Assay for Interactome Mapping in Mammalian Cells

Mapping of protein-protein interactions (PPIs) is critical for understanding protein function and complex biological processes. Here, we present DULIP, a dual luminescence-based co-immunoprecipitation assay, for systematic PPI mapping in mammalian cells. DULIP is a second-generation luminescence-based PPI screening method for the systematic and quantitative analysis of co-immunoprecipitations using two different luciferase tags. Benchmarking studies with positive and negative PPI reference sets revealed that DULIP allows the detection of interactions with high sensitivity and specificity. Furthermore, the analysis of a PPI reference set with known binding affinities demonstrated that both low- and high-affinity interactions can be detected with DULIP assays. Finally, using the well-characterized interaction between Syntaxin-1 and Munc18, we found that DULIP is capable of detecting the effects of point mutations on interaction strength. Taken together, our studies demonstrate that DULIP is a sensitive and reliable method of great utility for systematic interactome research. It can be applied for interaction screening and validation of PPIs in mammalian cells. Moreover, DULIP permits the specific analysis of mutation-dependent binding patterns.

Mapping the Protein-Protein Interactome Networks Using Yeast Two-Hybrid Screens

The yeast two-hybrid system (Y2H) is a powerful method to identify binary protein-protein interactions in vivo. Here we describe Y2H screening strategies that use defined libraries of open reading frames (ORFs) and cDNA libraries. The array-based Y2H system is well suited for interactome studies of small genomes with an existing ORFeome clones preferentially in a recombination based cloning system. For large genomes, pooled library screening followed by Y2H pairwise retests may be more efficient in terms of time and resources, but multiple sampling is necessary to ensure comprehensive screening. While the Y2H false positives can be efficiently reduced by using built-in controls, retesting, and evaluation of background activation; implementing the multiple variants of the Y2H vector systems is essential to reduce the false negatives and ensure comprehensive coverage of an interactome.

Widespread macromolecular interaction perturbations in human genetic disorders

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

Unveiling Contacts within Macromolecular Assemblies by Solving Minimum Weight Connectivity Inference (MWC) Problems

Consider a set of oligomers listing the subunits involved in subcomplexes of a macromolecular assembly, obtained e.g. using native mass spectrometry or affinity purification. Given these oligomers, connectivity inference (CI) consists of finding the most plausible contacts between these subunits, and minimum connectivity inference (MCI) is the variant consisting of finding a set of contacts of smallest cardinality. MCI problems avoid speculating on the total number of contacts but yield a subset of all contacts and do not allow exploiting a priori information on the likelihood of individual contacts. In this context, we present two novel algorithms, MILP-W and MILP-WB. The former solves the minimum weight connectivity inference (MWCI), an optimization problem whose criterion mixes the number of contacts and their likelihood. The latter uses the former in a bootstrap fashion to improve the sensitivity and the specificity of solution sets.Experiments on three systems (yeast exosome, yeast proteasome lid, human eIF3), for which reference contacts are known (crystal structure, cryo electron microscopy, cross-linking), show that our algorithms predict contacts with high specificity and sensitivity, yielding a very significant improvement over previous work, typically a twofold increase in sensitivity.The software accompanying this paper is made available and should prove of ubiquitous interest whenever connectivity inference from oligomers is faced.

Phospho-tyrosine dependent protein-protein interaction network

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.

A visual review of the interactome of LRRK2: Using deep-curated molecular interaction data to represent biology

Molecular interaction databases are essential resources that enable access to a wealth of information on associations between proteins and other biomolecules. Network graphs generated from these data provide an understanding of the relationships between different proteins in the cell, and network analysis has become a widespread tool supporting –omics analysis. Meaningfully representing this information remains far from trivial and different databases strive to provide users with detailed records capturing the experimental details behind each piece of interaction evidence. A targeted curation approach is necessary to transfer published data generated by primarily low-throughput techniques into interaction databases. In this review we present an example highlighting the value of both targeted curation and the subsequent effective visualization of detailed features of manually curated interaction information. We have curated interactions involving LRRK2, a protein of largely unknown function linked to familial forms of Parkinson's disease, and hosted the data in the IntAct database. This LRRK2-specific dataset was then used to produce different visualization examples highlighting different aspects of the data: the level of confidence in the interaction based on orthogonal evidence, those interactions found under close-to-native conditions, and the enzyme–substrate relationships in different in vitro enzymatic assays. Finally, pathway annotation taken from the Reactome database was overlaid on top of interaction networks to bring biological functional context to interaction maps.

Extracting high confidence protein interactions from affinity purification data: at the crossroads

Deriving protein-protein interactions from data generated by affinity-purification and mass spectrometry (AP-MS) techniques requires application of scoring methods to measure the reliability of detected putative interactions. Choosing the appropriate scoring method has become a major challenge. Here we apply six popular scoring methods to the same AP-MS dataset and compare their performance. The comparison was carried out for six distinct datasets from human, fly and yeast, which focus on different biological processes and differ in their coverage of the proteome. Results show that the performance of a given scoring method may vary substantially depending on the dataset. Disturbingly, we find that the high confidence (HC) PPI networks built by applying the six scoring methods to the same raw AP-MS dataset display very poor overlap, with only 1.7-4.1% of the HC interactions present in all the networks built, respectively, from the proteome-wide human, fly or yeast datasets. Various properties of the shared versus unique interactions in each network, including biases in protein abundance, suggest that current scoring methods are able to eliminate only the most obvious contaminants, but still fail to reliably single out specific interactions from the large body of spurious associations detected in the AP-MS experiments.

BIOLOGICAL SIGNIFICANCE

The fast progress in AP-MS techniques has prompted the development of a multitude of scoring methods, which are relied upon to remove contaminants and non-specific binders. Choosing the appropriate scoring scheme for a given AP-MS dataset has become a major challenge. The comparative analysis of 6 of the most popular scoring methods, presented here, reveals that overall these methods do not perform as expected. Evidence is provided that this is due to 3 closely related issues: the high 'noise' levels of the raw AP-MS data, the limited capacity of current scoring methods to deal with such high noise levels, and the biases introduced using Gold Standard datasets to benchmark the scoring functions and threshold the networks. For the field to move forward, all three issues will have to be addressed. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study

The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative–molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

Systematic identification of molecular links between core and candidate genes in breast cancer

Despite the remarkable progress achieved in the identification of specific genes involved in breast cancer (BC), our understanding of their complex functioning is still limited. In this manuscript, we systematically explore the existence of direct physical interactions between the products of BC core and associated genes. Our aim is to generate a protein interaction network of BC-associated gene products and suggest potential molecular mechanisms to unveil their role in the disease. In total, we report 599 novel high-confidence interactions among 44 BC core, 54 BC candidate/associated and 96 newly identified proteins. Our findings indicate that this network-based approach is indeed a robust inference tool to pinpoint new potential players and gain insight into the underlying mechanisms of those proteins with previously unknown roles in BC. To illustrate the power of our approach, we provide initial validation of two BC-associated proteins on the alteration of DNA damage response as a result of specific re-wiring interactions. Overall, our BC-related network may serve as a framework to integrate clinical and molecular data and foster novel global therapeutic strategies.

MAPPIT, a mammalian two-hybrid method for in-cell detection of protein-protein interactions

MAPPIT (MAmmalian Protein-Protein Interaction Trap) is a two-hybrid technology that facilitates the detection and analysis of interactions between proteins in living mammalian cells. The system is based on type 1 cytokine receptor signaling. The bait protein of interest is fused to a chimeric signaling-deficient cytokine receptor, the signaling competence of which is restored upon recruitment of a prey protein that is coupled to a functional cytokine receptor domain. MAPPIT exhibits an excellent signal-to-noise ratio, detects a wide variety of protein-protein interactions (PPIs) including transient and indirect interactions, and has been shown to be highly complementary to other two-hybrid methods with respect to the interactions it can detect. Variants of the method were developed to allow large-scale PPI screening, mapping of protein interaction interfaces, PPI inhibitor screening and drug profiling. This chapter describes a basic 4-day MAPPIT protocol for the analysis of interaction between two designated proteins.

Intrinsic disorder mediates hepatitis C virus core-host cell protein interactions

Viral proteins bind to numerous cellular and viral proteins throughout the infection cycle. However, the mechanisms by which viral proteins interact with such large numbers of factors remain unknown. Cellular proteins that interact with multiple, distinct partners often do so through short sequences known as molecular recognition features (MoRFs) embedded within intrinsically disordered regions (IDRs). In this study, we report the first evidence that MoRFs in viral proteins play a similar role in targeting the host cell. Using a combination of evolutionary modeling, protein-protein interaction analyses and forward genetic screening, we systematically investigated two computationally predicted MoRFs within the N-terminal IDR of the hepatitis C virus (HCV) Core protein. Sequence analysis of the MoRFs showed their conservation across all HCV genotypes and the canine and equine Hepaciviruses. Phylogenetic modeling indicated that the Core MoRFs are under stronger purifying selection than the surrounding sequence, suggesting that these modules have a biological function. Using the yeast two-hybrid assay, we identified three cellular binding partners for each HCV Core MoRF, including two previously characterized cellular targets of HCV Core (DDX3X and NPM1). Random and site-directed mutagenesis demonstrated that the predicted MoRF regions were required for binding to the cellular proteins, but that different residues within each MoRF were critical for binding to different partners. This study demonstrated that viruses may use intrinsic disorder to target multiple cellular proteins with the same amino acid sequence and provides a framework for characterizing the binding partners of other disordered regions in viral and cellular proteomes.

A field-proven yeast two-hybrid protocol used to identify coronavirus-host protein-protein interactions

Over the last 2 decades, yeast two-hybrid became an invaluable technique to decipher protein-protein interaction networks. In the field of virology, it has proven instrumental to identify virus-host interactions that are involved in viral embezzlement of cellular functions and inhibition of immune mechanisms. Here, we present a yeast two-hybrid protocol that has been used in our laboratory since 2006 to search for cellular partners of more than 300 viral proteins. Our aim was to develop a robust and straightforward pipeline, which minimizes false-positive interactions with a decent coverage of target cDNA libraries, and only requires a minimum of equipment. We also discuss reasons that motivated our technical choices and compromises that had to be made. This protocol has been used to screen most non-structural proteins of murine hepatitis virus (MHV), a member of betacoronavirus genus, against a mouse brain cDNA library. Typical results were obtained and are presented in this report.

A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, "Clone-seq", leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.

Virus-host interactomics: new insights and opportunities for antiviral drug discovery

The current therapeutic arsenal against viral infections remains limited, with often poor efficacy and incomplete coverage, and appears inadequate to face the emergence of drug resistance. Our understanding of viral biology and pathophysiology and our ability to develop a more effective antiviral arsenal would greatly benefit from a more comprehensive picture of the events that lead to viral replication and associated symptoms. Towards this goal, the construction of virus-host interactomes is instrumental, mainly relying on the assumption that a viral infection at the cellular level can be viewed as a number of perturbations introduced into the host protein network when viral proteins make new connections and disrupt existing ones. Here, we review advances in interactomic approaches for viral infections, focusing on high-throughput screening (HTS) technologies and on the generation of high-quality datasets. We show how these are already beginning to offer intriguing perspectives in terms of virus-host cell biology and the control of cellular functions, and we conclude by offering a summary of the current situation regarding the potential development of host-oriented antiviral therapeutics.

A proteome-scale map of the human interactome network

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

In silico prediction of physical protein interactions and characterization of interactome orphans

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼ 10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).

A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways

Chaperones are abundant cellular proteins that promote the folding and function of their substrate proteins (clients). In vivo, chaperones also associate with a large and diverse set of cofactors (cochaperones) that regulate their specificity and function. However, how these cochaperones regulate protein folding and whether they have chaperone-independent biological functions is largely unknown. We combined mass spectrometry and quantitative high-throughput LUMIER assays to systematically characterize the chaperone-cochaperone-client interaction network in human cells. We uncover hundreds of chaperone clients, delineate their participation in specific cochaperone complexes, and establish a surprisingly distinct network of protein-protein interactions for cochaperones. As a salient example of the power of such analysis, we establish that NUDC family cochaperones specifically associate with structurally related but evolutionarily distinct β-propeller folds. We provide a framework for deciphering the proteostasis network and its regulation in development and disease and expand the use of chaperones as sensors for drug-target engagement.

Systematic screening reveals a role for BRCA1 in the response to transcription-associated DNA damage

BRCA1 is a breast and ovarian tumor suppressor. Given its numerous incompletely understood functions and the possibility that more exist, we performed complementary systematic screens in search of new BRCA1 protein-interacting partners. New BRCA1 functions and/or a better understanding of existing ones were sought. Among the new interacting proteins identified, genetic interactions were detected between BRCA1 and four of the interactors: TONSL, SETX, TCEANC, and TCEA2. Genetic interactions were also detected between BRCA1 and certain interactors of TONSL, including both members of the FACT complex. From these results, a new BRCA1 function in the response to transcription-associated DNA damage was detected. Specifically, new roles for BRCA1 in the restart of transcription after UV damage and in preventing or repairing damage caused by stabilized R loops were identified. These roles are likely carried out together with some of the newly identified interactors. This new function may be important in BRCA1 tumor suppression, since the expression of several interactors, including some of the above-noted transcription proteins, is repeatedly aberrant in both breast and ovarian cancers.

AMPylation of Rho GTPases subverts multiple host signaling processes

Rho GTPases are frequent targets of virulence factors as they are keystone signaling molecules. Herein, we demonstrate that AMPylation of Rho GTPases by VopS is a multifaceted virulence mechanism that counters several host immunity strategies. Activation of NFκB, Erk, and JNK kinase signaling pathways were inhibited in a VopS-dependent manner during infection with Vibrio parahaemolyticus. Phosphorylation and degradation of IKBα were inhibited in the presence of VopS as was nuclear translocation of the NFκB subunit p65. AMPylation also prevented the generation of superoxide by the phagocytic NADPH oxidase complex, potentially by inhibiting the interaction of Rac and p67. Furthermore, the interaction of GTPases with the E3 ubiquitin ligases cIAP1 and XIAP was hindered, leading to decreased degradation of Rac and RhoA during infection. Finally, we screened for novel Rac1 interactions using a nucleic acid programmable protein array and discovered that Rac1 binds to the protein C1QA, a protein known to promote immune signaling in the cytosol. Interestingly, this interaction was disrupted by AMPylation. We conclude that AMPylation of Rho Family GTPases by VopS results in diverse inhibitory consequences during infection beyond the most obvious phenotype, the collapse of the actin cytoskeleton.

Enhancing the functional content of eukaryotic protein interaction networks

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.