High-quality binary protein interaction map of the yeast interactome network

The monopolin complex crosslinks kinetochore components to regulate chromosome-microtubule attachments

The monopolin complex regulates different types of kinetochore-microtubule attachments in fungi, ensuring sister chromatid co-orientation in Saccharomyces cerevisiae meiosis I and inhibiting merotelic attachment in Schizosaccharomyces pombe mitosis. In addition, the monopolin complex maintains the integrity and silencing of ribosomal DNA (rDNA) repeats in the nucleolus. We show here that the S. cerevisiae Csm1/Lrs4 monopolin subcomplex has a distinctive V-shaped structure, with two pairs of protein-protein interaction domains positioned approximately 10 nm apart. Csm1 presents a conserved hydrophobic surface patch that binds two kinetochore proteins: Dsn1, a subunit of the outer-kinetochore MIND/Mis12 complex, and Mif2/CENP-C. Csm1 point-mutations that disrupt kinetochore-subunit binding also disrupt sister chromatid co-orientation in S. cerevisiae meiosis I. We further show that the same Csm1 point-mutations affect rDNA silencing, probably by disrupting binding to the rDNA-associated protein Tof2. We propose that Csm1/Lrs4 functions as a molecular clamp, crosslinking kinetochore components to enforce sister chromatid co-orientation in S. cerevisiae meiosis I and to suppress merotelic attachment in S. pombe mitosis, and crosslinking rDNA repeats to aid rDNA silencing.

Hos1 deacetylates Smc3 to close the cohesin acetylation cycle

Cohesion between sister chromatids is mediated by the chromosomal cohesin complex. In budding yeast, cohesin is loaded onto chromosomes during the G1 phase of the cell cycle. During S phase, the replication fork-associated acetyltransferase Eco1 acetylates the cohesin subunit Smc3 to promote the establishment of sister chromatid cohesion. At the time of anaphase, Smc3 loses its acetylation again, but the Smc3 deacetylase and the possible importance of Smc3 deacetylation are unknown. Here, we show that the class I histone deacetylase family member Hos1 is responsible for Smc3 deacetylation. Cohesin is protected from deacetylation while bound to chromosomes but is deacetylated as soon as it dissociates from chromosomes at anaphase onset. Nonacetylated Smc3 is required as a substrate for cohesion establishment in the following cell cycle. Our results complete the description of an Smc3 acetylation cycle and provide unexpected insight into the importance of de novo Smc3 acetylation for cohesion establishment.

Tah18 transfers electrons to Dre2 in cytosolic iron-sulfur protein biogenesis

Cytosolic and nuclear iron-sulfur (Fe-S) proteins play key roles in processes such as ribosome maturation, transcription and DNA repair-replication. For biosynthesis of their Fe-S clusters, a dedicated cytosolic Fe-S protein assembly (CIA) machinery is required. Here, we identify the essential flavoprotein Tah18 as a previously unrecognized CIA component and show by cell biological, biochemical and spectroscopic approaches that the complex of Tah18 and the CIA protein Dre2 is part of an electron transfer chain functioning in an early step of cytosolic Fe-S protein biogenesis. Electrons are transferred from NADPH via the FAD- and FMN-containing Tah18 to the Fe-S clusters of Dre2. This electron transfer chain is required for assembly of target but not scaffold Fe-S proteins, suggesting a need for reduction in the generation of stably inserted Fe-S clusters. The pathway is conserved in eukaryotes, as human Ndor1-Ciapin1 proteins can functionally replace yeast Tah18-Dre2.

Evolutionary analysis reveals low coverage as the major challenge for protein interaction network alignment

Local alignments of protein interaction networks have found little conservation among several species. While this could be a consequence of the incompleteness of interaction data-sets and presence of error, an intriguing prospect is that the process of network evolution is sufficient to erase any evidence of conservation. Here, we aim to test this hypothesis using models of network evolution and also investigate the role of error in the results of network alignment. We devised a distance metric based on summary statistics to assess the fit between experimental and simulated network alignments. Our results indicate that network evolution alone is unlikely to account for the poor quality alignments given by real data. Alignments of simulated networks undergoing evolution are considerably (4 to 5 times) larger than real alignments. We compare several error models in their ability to explain this discrepancy. Our estimates of false negative rates vary from 20 to 60% dependent on whether incomplete proteome sampling is taken into account or not. We also find that false positives appear to affect network alignments little compared to false negatives indicating that incompleteness, not spurious links, is the major challenge for interactome-level comparisons.

Curating the innate immunity interactome

BACKGROUND

The innate immune response is the first line of defence against invading pathogens and is regulated by complex signalling and transcriptional networks. Systems biology approaches promise to shed new light on the regulation of innate immunity through the analysis and modelling of these networks. A key initial step in this process is the contextual cataloguing of the components of this system and the molecular interactions that comprise these networks. InnateDB (http://www.innatedb.com) is a molecular interaction and pathway database developed to facilitate systems-level analyses of innate immunity.

RESULTS

Here, we describe the InnateDB curation project, which is manually annotating the human and mouse innate immunity interactome in rich contextual detail, and present our novel curation software system, which has been developed to ensure interactions are curated in a highly accurate and data-standards compliant manner. To date, over 13,000 interactions (protein, DNA and RNA) have been curated from the biomedical literature. Here, we present data, illustrating how InnateDB curation of the innate immunity interactome has greatly enhanced network and pathway annotation available for systems-level analysis and discuss the challenges that face such curation efforts. Significantly, we provide several lines of evidence that analysis of the innate immunity interactome has the potential to identify novel signalling, transcriptional and post-transcriptional regulators of innate immunity. Additionally, these analyses also provide insight into the cross-talk between innate immunity pathways and other biological processes, such as adaptive immunity, cancer and diabetes, and intriguingly, suggests links to other pathways, which as yet, have not been implicated in the innate immune response.

CONCLUSIONS

In summary, curation of the InnateDB interactome provides a wealth of information to enable systems-level analysis of innate immunity.

Yeast proteomics and protein microarrays

Our understanding of biological processes as well as human diseases has improved greatly thanks to studies on model organisms such as yeast. The power of scientific approaches with yeast lies in its relatively simple genome, its facile classical and molecular genetics, as well as the evolutionary conservation of many basic biological mechanisms. However, even in this simple model organism, systems biology studies, especially proteomic studies had been an intimidating task. During the past decade, powerful high-throughput technologies in proteomic research have been developed for yeast including protein microarray technology. The protein microarray technology allows the interrogation of protein–protein, protein–DNA, protein–small molecule interaction networks as well as post-translational modification networks in a large-scale, high-throughput manner. With this technology, many groundbreaking findings have been established in studies with the budding yeast Saccharomyces cerevisiae, most of which could have been unachievable with traditional approaches. Discovery of these networks has profound impact on explicating biological processes with a proteomic point of view, which may lead to a better understanding of normal biological phenomena as well as various human diseases.

Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons.

A CUG codon adapted two-hybrid system for the pathogenic fungus Candida albicans

The genetics of the most common human pathogenic fungus Candida albicans has several unique characteristics. Most notably, C. albicans does not follow the universal genetic code, by translating the CUG codon into serine instead of leucine. Consequently, the use of Saccharomyces cerevisiae as a host for yeast two-hybrid experiments with C. albicans proteins is limited due to erroneous translation caused by the aberrant codon usage of C. albicans. To circumvent the need for heterologous expression and codon optimalization of C. albicans genes we constructed a two-hybrid system with C. albicans itself as the host with components that are compatible for use in this organism. The functionality of this two-hybrid system was shown by successful interaction assays with the protein pairs Kis1-Snf4 and Ino4-Ino2. We further confirmed interactions between components of the filamentation/mating MAP kinase pathway, including the unsuspected interaction between the MAP kinases Cek2 and Cek1. We conclude that this system can be used to enhance our knowledge of protein-protein interactions in C. albicans.

A genome-wide gene function prediction resource for Drosophila melanogaster

Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

Purification and characterization of HIV-human protein complexes

To fully understand how pathogens infect their host and hijack key biological processes, systematic mapping of intra-pathogenic and pathogen–host protein–protein interactions (PPIs) is crucial. Due to the relatively small size of viral genomes (usually around 10–100 proteins), generation of comprehensive host–virus PPI maps using different experimental platforms, including affinity tag purification-mass spectrometry (AP-MS) and yeast two-hybrid (Y2H) approaches, can be achieved. Global maps such as these provide unbiased insight into the molecular mechanisms of viral entry, replication and assembly. However, to date, only two-hybrid methodology has been used in a systematic fashion to characterize viral–host protein–protein interactions, although a deluge of data exists in databases that manually curate from the literature individual host–pathogen PPIs. We will summarize this work and also describe an AP-MS platform that can be used to characterize viral-human protein complexes and discuss its application for the HIV genome.

A consensus of core protein complex compositions for Saccharomyces cerevisiae

Analyses of biological processes would benefit from accurate definitions of protein complexes. High-throughput mass spectrometry data offer the possibility of systematically defining protein complexes; however, the predicted compositions vary substantially depending on the algorithm applied. We determine consensus compositions for 409 core protein complexes from Saccharomyces cerevisiae by merging previous predictions with a new approach. Various analyses indicate that the consensus is comprehensive and of high quality. For 85 out of 259 complexes not recorded in GO, literature search revealed strong support in the form of coprecipitation. New complexes were verified by an independent interaction assay and by gene expression profiling of strains with deleted subunits, often revealing which cellular processes are affected. The consensus complexes are available in various formats, including a merge with GO, resulting in 518 protein complex compositions. The utility is further demonstrated by comparison with binary interaction data to reveal interactions between core complexes.

RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS

Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects S = {A(1) : B(1), A(2) : B(2), …, A(N) : B(N)}, measures how well other pairs A : B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided.

Protein–protein interactions

In the present article, we describe the two standard high-throughput methods for identification of protein complexes: two-hybrid screens and TAP (tandem affinity purification) tagging. These methods have been used to characterize the interactome of Saccharomyces cerevisiae, showing that the majority of proteins are part of complexes, and that complexes typically consist of a core to which are bound ‘party’ and ‘dater’ proteins. Complexes typically are merely the sum of their parts. A particularly interesting type of complex is the metabolon, containing enzymes within the same metabolic pathway. There is reasonably good evidence that metabolons exist, but they have not been detected using high-thoughput assays, possibly because of their fragility.

The function of communities in protein interaction networks at multiple scales

BACKGROUND

If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network.

RESULTS

Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein.

CONCLUSIONS

We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

Network-based function prediction and interactomics: the case for metabolic enzymes

As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases.

Deletion of Swm2p selectively impairs trimethylation of snRNAs by trimethylguanosine synthase (Tgs1p)

The 5' cap trimethylation of small nuclear (snRNAs) and several nucleolar RNAs (snoRNAs) by trimethylguanosine synthase 1 (Tgs1p) is required for efficient pre-mRNA splicing. The previously uncharacterised protein Swm2p interacted with Tgs1p in yeast two-hybrid screens. In the present study we show that Swm2p interacts with the N-terminus of Tgs1p and its deletion impairs pre-mRNA splicing and pre-rRNA processing. The trimethylation of spliceosomal snRNAs and the U3 snoRNA, but not other snoRNAs, was abolished in the absence of Swm2p, indicating that Swm2p is required for a substrate-specific activity of Tgs1p.

Genome-wide approaches to systematically identify substrates of the ubiquitin-proteasome pathway

The ubiquitin-proteasome system handles the majority of controlled proteolysis in eukaryotes. Defects in the ubiquitin-proteasome system have been implicated in diseases ranging from cancers to neurodegenerative disorders. However, the precise role of ubiquitin-proteasome-mediated degradation in health and disease is far from clear. A major challenge is to link specific substrates directly to a particular degradation pathway. Here, we review genome-wide approaches that have been developed in recent years to comprehensively identify ubiquitylated substrates of a particular pathway. Components of the ubiquitin-proteasome system are attractive drug targets, as illustrated by the efficacy of some proteasome inhibitors in the treatment of multiple myeloma. Information that has emerged from these studies could reveal novel drug targets and strategies for treating human diseases.

Characterization of cell cycle specific protein interaction networks of the yeast 26S proteasome complex by the QTAX strategy

Ubiquitin-proteasome dependent protein degradation plays a fundamental role in the regulation of the eukaryotic cell cycle. Cell cycle transitions between different phases are tightly regulated to prevent uncontrolled cell proliferation, which is characteristic of cancer cells. To understand cell cycle phase specific regulation of the 26S proteasome and reveal the molecular mechanisms underlying the ubiquitin-proteasome degradation pathway during cell cycle progression, we have carried out comprehensive characterization of cell cycle phase specific proteasome interacting proteins (PIPs) by QTAX analysis of synchronized yeast cells. Our efforts have generated specific proteasome interaction networks for the G1, S, and M phases of the cell cycle and identified a total of 677 PIPs, 266 of which were not previously identified from unsynchronized cells. On the basis of the dynamic changes of their SILAC ratios across the three cell cycle phases, we have employed a profile vector-based clustering approach and identified 20 functionally significant groups of PIPs, 3 of which are enriched with cell cycle related functions. This work presents the first step toward understanding how dynamic proteasome interactions are involved in various cellular pathways during the cell cycle.

Efficient extraction of protein-protein interactions from full-text articles

Proteins and their interactions govern virtually all cellular processes, such as regulation, signaling, metabolism, and structure. Most experimental findings pertaining to such interactions are discussed in research papers, which, in turn, get curated by protein interaction databases. Authors, editors, and publishers benefit from efforts to alleviate the tasks of searching for relevant papers, evidence for physical interactions, and proper identifiers for each protein involved. The BioCreative II.5 community challenge addressed these tasks in a competition-style assessment to evaluate and compare different methodologies, to make aware of the increasing accuracy of automated methods, and to guide future implementations. In this paper, we present our approaches for protein-named entity recognition, including normalization, and for extraction of protein-protein interactions from full text. Our overall goal is to identify efficient individual components, and we compare various compositions to handle a single full-text article in between 10 seconds and 2 minutes. We propose strategies to transfer document-level annotations to the sentence-level, which allows for the creation of a more fine-grained training corpus; we use this corpus to automatically derive around 5,000 patterns. We rank sentences by relevance to the task of finding novel interactions with physical evidence, using a sentence classifier built from this training corpus. Heuristics for paraphrasing sentences help to further remove unnecessary information that might interfere with patterns, such as additional adjectives, clauses, or bracketed expressions. In BioCreative II.5, we achieved an f-score of 22 percent for finding protein interactions, and 43 percent for mapping proteins to UniProt IDs; disregarding species, f-scores are 30 percent and 55 percent, respectively. On average, our best-performing setup required around 2 minutes per full text. All data and pattern sets as well as Java classes that extend- - third-party software are available as supplementary information (see Appendix).

Topology-free querying of protein interaction networks

In the network querying problem, one is given a protein complex or pathway of species A and a protein-protein interaction network of species B; the goal is to identify subnetworks of B that are similar to the query in terms of sequence, topology, or both. Existing approaches mostly depend on knowledge of the interaction topology of the query in the network of species A; however, in practice, this topology is often not known. To address this problem, we develop a topology-free querying algorithm, which we call Torque. Given a query, represented as a set of proteins, Torque seeks a matching set of proteins that are sequence-similar to the query proteins and span a connected region of the network, while allowing both insertions and deletions. The algorithm uses alternatively dynamic programming and integer linear programming for the search task. We test Torque with queries from yeast, fly, and human, where we compare it to the QNet topology-based approach, and with queries from less studied species, where only topology-free algorithms apply. Torque detects many more matches than QNet, while giving results that are highly functionally coherent.

Link communities reveal multiscale complexity in networks

Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society. One crucial step when studying the structure and dynamics of networks is to identify communities: groups of related nodes that correspond to functional subunits such as protein complexes or social spheres. Communities in networks often overlap such that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure. However, the fact that many real networks have communities with pervasive overlap, where each and every node belongs to more than one group, has the consequence that a global hierarchy of nodes cannot capture the relationships between overlapping groups. Here we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks, including major biological networks such as protein-protein interaction and metabolic networks, and show that a large social network contains hierarchically organized community structures spanning inner-city to regional scales while maintaining pervasive overlap. Our results imply that link communities are fundamental building blocks that reveal overlap and hierarchical organization in networks to be two aspects of the same phenomenon.

Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks

The idea of "date" and "party" hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.

Structural characterization of the Get4/Get5 complex and its interaction with Get3

The recently elucidated Get proteins are responsible for the targeted delivery of the majority of tail-anchored (TA) proteins to the endoplasmic reticulum. Get4 and Get5 have been identified in the early steps of the pathway mediating TA substrate delivery to the cytoplasmic targeting factor Get3. Here we report a crystal structure of Get4 and an N-terminal fragment of Get5 from Saccharomyces cerevisae. We show Get4 and Get5 (Get4/5) form an intimate complex that exists as a dimer (two copies of Get4/5) mediated by the C-terminus of Get5. We further demonstrate that Get3 specifically binds to a conserved surface on Get4 in a nucleotide dependent manner. This work provides further evidence for a model in which Get4/5 operates upstream of Get3 and mediates the specific delivery of a TA substrate.

An integrated view of molecular coevolution in protein-protein interactions

Protein-protein interactions effectively mediate molecular function. They are the result of specific interactions between protein interfaces and are maintained by the action of evolutionary pressure on the regions of the interacting proteins that contribute to binding. For the most part, selection restricts amino acid replacements, accounting for the conservation of binding interfaces. However, in some cases, change in one protein will be mitigated by compensatory change in its binding partner, maintaining function in the face of evolutionary change. There have been several attempts to use correlations in sequence evolution to predict interactions of proteins. Most commonly, these approaches use the entire sequence to identify correlations and so infer probable binding. However, other factors such as shared evolutionary history and similarities in the rates of evolution confound these whole-sequence-based approaches. Here, we discuss recent work on this topic and argue that both site-specific coevolutionary change and whole-sequence evolution contribute to evolutionary signals in sets of interacting proteins. We discuss the relative effects of both types of selection and how they might be identified. This permits an integrated view of protein-protein interactions, their evolution, and coevolution.

Trm112p is a 15-kDa zinc finger protein essential for the activity of two tRNA and one protein methyltransferases in yeast

The degenerate base at position 34 of the tRNA anticodon is the target of numerous modification enzymes. In Saccharomyces cerevisiae, five tRNAs exhibit a complex modification of uridine 34 (mcm(5)U(34) and mcm(5)s(2)U(34)), the formation of which requires at least 25 different proteins. The addition of the last methyl group is catalyzed by the methyltransferase Trm9p. Trm9p interacts with Trm112p, a 15-kDa protein with a zinc finger domain. Trm112p is essential for the activity of Trm11p, another tRNA methyltransferase, and for Mtq2p, an enzyme that methylates the translation termination factor eRF1/Sup45. Here, we report that Trm112p is required in vivo for the formation of mcm(5)U(34) and mcm(5)s(2)U(34). When produced in Escherichia coli, Trm112p forms a complex with Trm9p, which renders the latter soluble. This recombinant complex catalyzes the formation of mcm(5)U(34) on tRNA in vitro but not mcm(5)s(2)U(34). An mtq2-0 trm9-0 strain exhibits a synthetic growth defect, thus revealing the existence of an unexpected link between tRNA anticodon modification and termination of translation. Trm112p is associated with other partners involved in ribosome biogenesis and chromatin remodeling, suggesting that it has additional roles in the cell.

Databases of protein-protein interactions and complexes

In the current understanding, translation of genomic sequences into proteins is the most important path for realization of genome information. In exercising their intended function, proteins work together through various forms of direct (physical) or indirect interaction mechanisms. For a variety of basic functions, many proteins form a large complex representing a molecular machine or a macromolecular super-structural building block. After several high-throughput techniques for detection of protein-protein interactions had matured, protein interaction data became available in a large scale and curated databases for protein-protein interactions (PPIs) are a new necessity for efficient research. Here, their scope, annotation quality, and retrieval tools are reviewed. In addition, attention is paid to portals that provide unified access to a variety of such databases with added annotation value.

A survey of yeast genomic assays for drug and target discovery

Over the past decade, the development and application of chemical genomic assays using the model organism Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of known drugs and novel small molecules in vivo. These assays identify drug target candidates, genes involved in buffering drug target pathways and also help to define the general cellular response to small molecules. In this review, we examine current yeast chemical genomic assays and summarize the potential applications of each approach.

A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes

Using radiation hybrid genotyping data, 99% of all possible gene pairs across the mammalian genome were tested for interactions based on co-retention frequencies higher (attraction) or lower (repulsion) than chance. Gene interaction networks constructed from six independent data sets overlapped strongly. Combining the data sets resulted in a network of more than seven million interactions, almost all attractive. This network overlapped with protein-protein interaction networks on multiple measures and also confirmed the relationship between essentiality and centrality. In contrast to other biological networks, the radiation hybrid network did not show a scale-free distribution of connectivity but was Gaussian-like, suggesting a closer approach to saturation. The radiation hybrid (RH) network constitutes a platform for understanding the systems biology of the mammalian cell.

Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

Background

Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives.

Results

Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs) and 779 protein-protein interactions. A number of proteins were identified having interactions with more than one of the protein kinases. The fully reconstructed interaction network includes all the information available in separate databases for all the proteins included in the network (nodes) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner.

Conclusions

The reported fully annotated interaction model serves as a platform for integrated systems biology studies of nutrient sensing and regulation in S. cerevisiae. Furthermore, we propose this annotated reconstruction as a first step towards generation of an extensive annotated protein-protein interaction network of signal transduction and metabolic regulation in this yeast.

When peptides fly: advances in Drosophila proteomics

In the past decade, improvements in genome annotation, protein fractionation methods and mass spectrometry instrumentation resulted in rapid growth of Drosophila proteomics. This review presents the current status of proteomics research in the fly. Areas that have seen major advances in recent years include efforts to map and catalog the Drosophila proteome and high-throughput as well as targeted studies to analyze protein-protein interactions and post-translational modifications. Stable isotope labeling of flies and other applications of quantitative proteomics have opened up new possibilities for functional analyses. It is clear that proteomics is becoming an indispensable tool in Drosophila systems biology research that adds a unique dimension to studying gene function.

Mutational analyses of trimethylguanosine synthase (Tgs1) and Mud2: proteins implicated in pre-mRNA splicing

Yeast and human Tgs1 are orthologous RNA cap (guanine-N2) methyltransferases that convert m(7)G caps into the 2,2,7-trimethylguanosine (TMG) caps characteristic of spliceosomal snRNAs. TMG caps are dispensable for vegetative yeast growth, but are essential in the absence of Mud2, the putative yeast homolog of human splicing factor U2AF. Here we exploited the synthetic lethal interactions of tgs1Delta and mud2Delta mutations to identify essential structural features of the Tgs1 and Mud2 proteins. Thirty-two new mutations were introduced into human Tgs1 and surveyed for their effects on function in vivo in yeast and on the two sequential guanine-N2 methylation reactions in vitro. The structure-function data highlight a strictly essential pi-cation interaction between Trp766 and the m(7)G base and a network of important enzymic contacts to the cap triphosphate via Lys646, Tyr771, Arg807, and Lys836. Mud2 is a 527-amino acid polypeptide composed of a hydrophilic N-terminal domain and a C-terminal RRM domain. We found that the RRM domain is necessary but not sufficient for Mud2 function in complementing growth of tgs1Delta mud2Delta and mud1Delta mud2Delta strains. Other changes in Mud2 elicited distinct phenotypes in tgs1Delta versus mud1Delta backgrounds. mud2Delta also caused a severe growth defect in cells lacking the Tgs1-binding protein encoded by the nonessential gene YNR004w (now renamed SWM2, synthetic with mud2Delta). Mud2 mutational effects in the swm2Delta background paralleled those for mud1Delta. The requirements for Mud2 function are apparently more stringent when yeast cells lack TMG caps than when they lack Mud1 or Swm2.

Analysis of hot region organization in hub proteins

Protein interaction maps constructed from binary interactions reveal that some proteins are highly connected to others (acting as hub proteins), whereas some others have a few interactions (at the edges of the map). This paper addresses hub proteins from a structural point: interfaces. It investigates how hot spots are organized in hub proteins (hot regions). We annotate interfaces as the ones between two date-hubs (DD), two party hubs (PP), and two non-hubs (NN). We investigate the physico-chemical properties of these three types of interfaces focusing on the accessible surface area distribution, hot region organization, and amino acid composition differences. Results reveal that there are significant differences between DD and PP interfaces. More of the hot spots are organized into the hot regions in DD interfaces compared to PP ones. A high fraction of the interfaces are covered by hot regions in DD interfaces. There are more distinct hot regions in DDs. Since the same (or overlapping) DD interfaces should be used repeatedly, different hot regions can be used to bind to different partners. Further, these hot region characteristics can be used to predict whether a given hub interface is involved in a DD or a PP interface type with 80% accuracy.

Tti1 and Tel2 are critical factors in mammalian target of rapamycin complex assembly

Mammalian target of rapamycin (mTOR) is a member of the phosphatidylinositol 3-kinase-related kinase (PIKK) family and is a major regulator of translation, cell growth, and autophagy. mTOR exists in two distinct complexes, mTORC1 and mTORC2, that differ in their subunit composition. In this study, we identified KIAA0406 as a novel mTOR-interacting protein. Because it has sequence homology with Schizosaccharomyces pombe Tti1, we named it mammalian Tti1. Tti1 constitutively interacts with mTOR in both mTORC1 and mTORC2. Knockdown of Tti1 suppresses phosphorylation of both mTORC1 substrates (S6K1 and 4E-BP1) and an mTORC2 substrate (Akt) and also induces autophagy. S. pombe Tti1 binds to Tel2, a protein whose mammalian homolog was recently reported to regulate the stability of PIKKs. We confirmed that Tti1 binds to Tel2 also in mammalian cells, and Tti1 interacts with and stabilizes all six members of the PIKK family of proteins (mTOR, ATM, ATR, DNA-PKcs, SMG-1, and TRRAP). Furthermore, using immunoprecipitation and size-exclusion chromatography analyses, we found that knockdown of either Tti1 or Tel2 causes disassembly of mTORC1 and mTORC2. These results indicate that Tti1 and Tel2 are important not only for mTOR stability but also for assembly of the mTOR complexes to maintain their activities.

Global approaches to study protein-protein interactions among viruses and hosts

While high-throughput protein-protein interaction screens were first published approximately 10 years ago, systematic attempts to map interactions among viruses and hosts started only a few years ago. HIV-human interactions dominate host-pathogen interaction databases (with approximately 2000 interactions) despite the fact that probably none of these interactions have been identified in systematic interaction screens. Recently, combinations of protein interaction data with RNAi and other functional genomics data allowed researchers to model more complex interaction networks. The rapid progress in this area promises a flood of new data in the near future, with clinical applications as soon as structural and functional genomics catches up with next-generation sequencing of human variation and structure-based drug design.

Comprehensive reanalysis of transcription factor knockout expression data in Saccharomyces cerevisiae reveals many new targets

Transcription factor (TF) perturbation experiments give valuable insights into gene regulation. Genome-scale evidence from microarray measurements may be used to identify regulatory interactions between TFs and targets. Recently, Hu and colleagues published a comprehensive study covering 269 TF knockout mutants for the yeast Saccharomyces cerevisiae. However, the information that can be extracted from this valuable dataset is limited by the method employed to process the microarray data. Here, we present a reanalysis of the original data using improved statistical techniques freely available from the BioConductor project. We identify over 100,000 differentially expressed genes-nine times the total reported by Hu et al. We validate the biological significance of these genes by assessing their functions, the occurrence of upstream TF-binding sites, and the prevalence of protein-protein interactions. The reanalysed dataset outperforms the original across all measures, indicating that we have uncovered a vastly expanded list of relevant targets. In summary, this work presents a high-quality reanalysis that maximizes the information contained in the Hu et al. compendium. The dataset is available from ArrayExpress (accession: E-MTAB-109) and it will be invaluable to any scientist interested in the yeast transcriptional regulatory system.

Strategies for protein synthetic biology

Proteins are the most versatile among the various biological building blocks and a mature field of protein engineering has lead to many industrial and biomedical applications. But the strength of proteins—their versatility, dynamics and interactions—also complicates and hinders systems engineering. Therefore, the design of more sophisticated, multi-component protein systems appears to lag behind, in particular, when compared to the engineering of gene regulatory networks. Yet, synthetic biologists have started to tinker with the information flow through natural signaling networks or integrated protein switches. A successful strategy common to most of these experiments is their focus on modular interactions between protein domains or domains and peptide motifs. Such modular interaction swapping has rewired signaling in yeast, put mammalian cell morphology under the control of light, or increased the flux through a synthetic metabolic pathway. Based on this experience, we outline an engineering framework for the connection of reusable protein interaction devices into self-sufficient circuits. Such a framework should help to ‘refacture’ protein complexity into well-defined exchangeable devices for predictive engineering. We review the foundations and initial success stories of protein synthetic biology and discuss the challenges and promises on the way from protein- to protein systems design.

The C-terminus of Utp4, mutated in childhood cirrhosis, is essential for ribosome biogenesis

The small subunit (SSU) processome is a large ribonucleoprotein that is required for maturation of the 18S rRNA of the ribosome. Recently, a missense mutation in the C-terminus of an SSU processome protein, Utp4/Cirhin, was reported to cause North American Indian childhood cirrhosis (NAIC). In this study, we use Saccharomyces cerevisiae as a model to investigate the role of the NAIC mutation in ribosome biogenesis. While we find that the homologous NAIC mutation does not cause growth defects or aberrant ribosome biogenesis in yeast, we show that an intact C-terminus of Utp4 is required for cell growth and maturation of the 18S and 25S rRNAs. A protein–protein interaction map of the seven-protein t-Utp subcomplex of which Utp4 is a member shows that Utp8 interacts with the C-terminus of Utp4 and that this interaction is essential for assembly of the SSU processome and for the function of Utp4 in ribosome biogenesis. Furthermore, these results allow us to propose that NAIC may be caused by dysfunctional pre-ribosome assembly due to the loss of an interaction between the C-terminus of Utp4/Cirhin and another SSU processome protein.

From Experimental Approaches to Computational Techniques: A Review on the Prediction of Protein-Protein Interactions

A crucial step towards understanding the properties of cellular systems in organisms is to map their network of protein-protein interactions (PPIs) on a proteomic-wide scale completely and as accurately as possible. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of disease and provide a basis for the development of novel therapeutic approaches. The development of large-scale high-throughput experiments has resulted in the production of a large volume of data which has aided in the uncovering of PPIs. However, these data are often erroneous and limited in interactome coverage. Therefore, additional experimental and computational methods are required to accelerate the discovery of PPIs. This paper provides a review on the prediction of PPIs addressing key prediction principles and highlighting the common experimental and computational techniques currently employed to infer PPI networks along with relevant studies in the area.

Functional modules in the Arabidopsis core cell cycle binary protein-protein interaction network

As in other eukaryotes, cell division in plants is highly conserved and regulated by cyclin-dependent kinases (CDKs) that are themselves predominantly regulated at the posttranscriptional level by their association with proteins such as cyclins. Although over the last years the knowledge of the plant cell cycle has considerably increased, little is known on the assembly and regulation of the different CDK complexes. To map protein-protein interactions between core cell cycle proteins of Arabidopsis thaliana, a binary protein-protein interactome network was generated using two complementary high-throughput interaction assays, yeast two-hybrid and bimolecular fluorescence complementation. Pairwise interactions among 58 core cell cycle proteins were tested, resulting in 357 interactions, of which 293 have not been reported before. Integration of the binary interaction results with cell cycle phase-dependent expression information and localization data allowed the construction of a dynamic interaction network. The obtained interaction map constitutes a framework for further in-depth analysis of the cell cycle machinery.

GMF is a cofilin homolog that binds Arp2/3 complex to stimulate filament debranching and inhibit actin nucleation

Cell locomotion and endocytosis are powered by the rapid polymerization and turnover of branched actin filament networks nucleated by Arp2/3 complex. Although a large number of cellular factors have been identified that stimulate Arp2/3 complex-mediated actin nucleation, only a small number of studies so far have addressed which factors promote actin network debranching. Here, we investigated the function of a conserved homolog of ADF/cofilin, glia maturation factor (GMF). We found that S. cerevisiae GMF (also called Aim7) localizes in vivo to cortical actin patches and displays synthetic genetic interactions with ADF/cofilin. However, GMF lacks detectable actin binding or severing activity and instead binds tightly to Arp2/3 complex. Using in vitro evanescent wave microscopy, we demonstrated that GMF potently stimulates debranching of actin filaments produced by Arp2/3 complex. Further, GMF inhibits nucleation of new daughter filaments. Together, these data suggest that GMF binds Arp2/3 complex to both "prune" daughter filaments at the branch points and inhibit new actin assembly. These activities and its genetic interaction with ADF/cofilin support a role for GMF in promoting the remodeling and/or disassembly of branched networks. Therefore, ADF/cofilin and GMF, members of the same superfamily, appear to have evolved to interact with actin and actin-related proteins, respectively, and to make mechanistically distinct contributions to the remodeling of cortical actin structures.

From protein sequences to 3D-structures and beyond: the example of the UniProt knowledgebase

With the dramatic increase in the volume of experimental results in every domain of life sciences, assembling pertinent data and combining information from different fields has become a challenge. Information is dispersed over numerous specialized databases and is presented in many different formats. Rapid access to experiment-based information about well-characterized proteins helps predict the function of uncharacterized proteins identified by large-scale sequencing. In this context, universal knowledgebases play essential roles in providing access to data from complementary types of experiments and serving as hubs with cross-references to many specialized databases. This review outlines how the value of experimental data is optimized by combining high-quality protein sequences with complementary experimental results, including information derived from protein 3D-structures, using as an example the UniProt knowledgebase (UniProtKB) and the tools and links provided on its website ( http://www.uniprot.org/ ). It also evokes precautions that are necessary for successful predictions and extrapolations.

An atlas of combinatorial transcriptional regulation in mouse and man

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

Building and analyzing protein interactome networks by cross-species comparisons

Background

A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species.

Results

The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen) and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced.

Conclusions

Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website http://www.interologfinder.org provides research biologists intuitive access to this data.

Building macromolecular assemblies by information-driven docking: introducing the HADDOCK multibody docking server

Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results.