Cooperative evolution in protein complexes of yeast from comparative analyses of its interaction network

A comparative analysis among Saccharomyces cerevisiae and the other four yeasts Candida glabrata, Kluyveromyces lactis, Debaryomyces hansenii, and Yarrowia lipolytica is presented. The broad evolutionary range spanned by the organisms allows to quantitatively demonstrate novel evolutionary effects in protein complexes. The evolution rates within cliques of interlinked proteins are found to bear strong multipoint correlations, witnessing a cooperative coevolution of complex subunits. The coevolution is found to be largely independent of the tendency of the subunits to have similar abundances.     

Identification of protein complexes from multi-relationship protein interaction networks

Background

Protein complexes play an important role in biological processes. Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to the prediction of protein complexes. However, the precision of protein complex prediction still needs to be improved due to the incompletion and noise in PPI networks.

Results

There exist complex and diverse relationships among proteins after integrating multiple sources of biological information. Considering that the influences of different types of interactions are not the same weight for protein complex prediction, we construct a multi-relationship protein interaction network (MPIN) by integrating PPI network topology with gene ontology annotation information. Then, we design a novel algorithm named MINE (identifying protein complexes based on Multi-relationship protein Interaction NEtwork) to predict protein complexes with high cohesion and low coupling from MPIN.

Conclusions

The experiments on yeast data show that MINE outperforms the current methods in terms of both accuracy and statistical significance.

     

Functional maps of protein complexes from quantitative genetic interaction data

Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.     

Iterative cluster analysis of protein interaction data

MOTIVATION: Generation of fast tools of hierarchical clustering to be applied when distances among elements of a set are constrained, causing frequent distance ties, as happens in protein interaction data. RESULTS: We present in this work the program UVCLUSTER, that iteratively explores distance datasets using hierarchical clustering. Once the user selects a group of proteins, UVCLUSTER converts the set of primary distances among them (i.e. the minimum number of steps, or interactions, required to connect two proteins) into secondary distances that measure the strength of the connection between each pair of proteins when the interactions for all the proteins in the group are considered. We show that this novel strategy has advantages over conventional clustering methods to explore protein-protein interaction data. UVCLUSTER easily incorporates the information of the largest available interaction datasets to generate comprehensive primary distance tables. The versatility, simplicity of use and high speed of UVCLUSTER on standard personal computers suggest that it can be a benchmark analytical tool for interactome data analysis. AVAILABILITY: The program is available upon request from the authors, free for academic users. Additional information available at http://www.uv.es/genomica/UVCLUSTER.     

Quantitative analysis of protein interaction network dynamics in yeast

Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.     

Identification of continuous interaction sites in PLA2-based protein complexes by peptide arrays

Crotoxin (CA.CB) is a β-neurotoxin from Crotalus durissus terrificus snake venom that is responsible for main envenomation effects upon biting by this snake. It is a heterodimer of an acidic protein (CA) devoid of any biological activity per se and a basic, enzymatically active, PLA₂ counterpart (CB). Both lethal and enzymatic activities of crotoxin have been shown to be inhibited by CNF, a protein from the blood of C. d. terrificus snakes. CNF replaces CA in the CA.CB complex, forming a stable, non-toxic complex CNF.CB. The molecular sites involved in the tight interfacial protein–protein interactions in these PLA₂-based complexes have not been clearly determined. To help address this question, we used the peptide arrays approach to map possible interfacial interaction sites in CA.CB and CNF.CB. Amino acid stretches putatively involved in these interactions were firstly identified in the primary structure of CB. Further analysis of the interfacial availability of these stretches in the presumed biologically active structure of CB, suggested two interaction main sites, located at the amino-terminus and β-wing regions. Peptide segments at the carboxyl-terminus of CB were also suggested to play a secondary role in the binding of both CA and CNF.     

The application of yeast hybrid systems in protein interaction analysis

Yeast hybrid systems have been widely used due to their convenience and low cost. Based on these systems, many methods have been developed to analyze protein–protein, protein–DNA and protein–RNA interactions. In this paper, we are reviewing these different yeast hybrid systems. According to the number of hybrid proteins, yeast hybrid systems can be divided into three categories, yeast one-hybrid, yeast two-hybrid and yeast three-hybrid systems. Alternatively, yeast hybrid systems can be categorized according to the subcellular localization of the protein interaction process in the cell into nuclear protein–protein interactions, cytosol protein–protein interactions and membrane protein–protein interactions. Throughout the review, we focus on the progress and limitations of each yeast hybrid system over the recent years.     

From pull-down data to protein interaction networks and complexes with biological relevance

Motivation: Recent improvements in high-throughput Mass Spectrometry(MS) technology have expedited genome-wide discovery of protein–proteininteractions by providing a capability of detecting proteincomplexes in a physiological setting. Computational inferenceof protein interaction networks and protein complexes from MSdata are challenging. Advances are required in developing robustand seamlessly integrated procedures for assessment of protein–proteininteraction affinities, mathematical representation of proteininteraction networks, discovery of protein complexes and evaluationof their biological relevance. Results: A multi-step but easy-to-follow framework for identifyingprotein complexes from MS pull-down data is introduced. It assessesinteraction affinity between two proteins based on similarityof their co-purification patterns derived from MS data. It constructsa protein interaction network by adopting a knowledge-guidedthreshold selection method. Based on the network, it identifiesprotein complexes and infers their core components using a graph-theoreticalapproach. It deploys a statistical evaluation procedure to assessbiological relevance of each found complex. On Saccharomycescerevisiae pull-down data, the framework outperformed othermore complicated schemes by at least 10% in F₁-measure and identified610 protein complexes with high-functional homogeneity basedon the enrichment in Gene Ontology (GO) annotation. Manual examinationof the complexes brought forward the hypotheses on cause offalse identifications. Namely, co-purification of differentprotein complexes as mediated by a common non-protein molecule,such as DNA, might be a source of false positives. Protein identificationbias in pull-down technology, such as the hydrophilic bias couldresult in false negatives. Contact: samatovan{at}ornl.gov Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Jonathan Wren Present address: Department of Biomedical Informatics, VanderbiltUniversity, Nashville, TN 37232. The authors wish it to be known that, in their opinion, thefirst two authors should be regarded as joint First Authors.     

Phosphorylating and dephosphorylating protein complexes in bacterial chemotaxis.

During optimal motility conditions, a 1:1 stoichiometry of CheA(L) (654 amino acids) to CheA(S) (557 amino acids) was determined. It was also found that CheZ binding to CheA(S) was inhibited by CheA(L)-CheA(S)-CheW interaction. This suggests that CheA(S) has different functions in the phosphorylating complex (CheA(L)-CheA(S)-CheW) and in the dephosphorylating complex (CheA(S)-CheZ).     

Identification of the yeast nuclear gene for the mitochondrial homologue of bacterial ribosomal protein L16.

An open reading frame encoding a member of the L16 family of ribosomal proteins is adjacent to the URA7 gene on the left arm of chromosome II in Saccharomyces cerevisiae. The predicted L16-like polypeptide is basic (pl 11.12), contains 232 amino acids (26.52 kDa) and has 36% amino acid sequence identity to E. coli L16. Immunoblot analysis with polyclonal antibodies to the L16-like polypeptide showed specific cross-reaction with a 22,000 Mr mitochondrial polypeptide that co-sediments with the large subunit of the mitochondrial ribosome in sucrose density gradients. The levels of the L16 mRNA and protein varied in response to carbon source. In [rho degree] cells lacking mitochondrial rRNA, the L16 mRNA accumulated at normal levels, but the protein was barely detectable, indicating RNA-dependent accumulation of the L16 protein. Gene disruption experiments demonstrated that the yeast mitochondrial L16 is an essential ribosomal protein in vivo.     

Up-to-date catalogues of yeast protein complexes

Gold standard datasets on protein complexes are key to inferring and validating protein–protein interactions. Despite much progress in characterizing protein complexes in the yeast Saccharomyces cerevisiae, numerous researchers still use as reference the manually curated complexes catalogued by the Munich Information Center of Protein Sequences database. Although this catalogue has served the community extremely well, it no longer reflects the current state of knowledge. Here, we report two catalogues of yeast protein complexes as results of systematic curation efforts. The first one, denoted as CYC2008, is a comprehensive catalogue of 408 manually curated heteromeric protein complexes reliably backed by small-scale experiments reported in the current literature. This catalogue represents an up-to-date reference set for biologists interested in discovering protein interactions and protein complexes. The second catalogue, denoted as YHTP2008, comprises 400 high-throughput complexes annotated with current literature evidence. Among them, 262 correspond, at least partially, to CYC2008 complexes. Evidence for interacting subunits is collected for 68 complexes that have only partial or no overlap with CYC2008 complexes, whereas no literature evidence was found for 100 complexes. Some of these partially supported and as yet unsupported complexes may be interesting candidates for experimental follow up. Both catalogues are freely available at: http://wodaklab.org/cyc2008/.     

Identification of a putative yeast homolog of the mammalian beta chains of the clathrin-associated protein complexes.

The clathrin-associated protein complexes are heterotetrameric structures believed to interact with clathrin and with membrane components of mammalian coated pits and coated vesicles. I have identified a yeast homolog of the mammalian beta-type large chains, suggesting the existence in yeast cells of clathrin-associated protein complexes. A sequence comparison between the putative yeast beta-type chain and its mammalian counterparts shows that their amino-terminal domains are related over their entire length and that their carboxyl-terminal domains diverge completely. This observation is consistent with our earlier proposal (T. Kurchhausen et al., Proc. Natl. Acad. Sci. USA 86:2612-2616, 1989) for the bifunctional-domain organization of the large chains, in which the invariant amino-terminal region interacts with conserved proteins of the coat while the variable carboxyl-terminal domain interacts with different membrane components of coated pits and coated vesicles.     

Bridging structural biology and genomics: assessing protein interaction data with known complexes

Currently, there is a major effort to map protein-protein interactions on a genome-wide scale. The utility of the resulting interaction networks will depend on the reliability of the experimental methods and the coverage of the approaches. Known macromolecular complexes provide a defined and objective set of protein interactions with which to compare biochemical and genetic data for validation. Here, we show that a significant fraction of the protein-protein interactions in genome-wide datasets, as well as many of the individual interactions reported in the literature, are inconsistent with the known 3D structures of three recent complexes (RNA polymerase II, Arp2/3 and the proteasome). Furthermore, comparison among genome-wide datasets, and between them and a larger (but less well resolved) group of 174 complexes, also shows marked inconsistencies. Finally, individual interaction datasets, being inherently noisy, are best used when integrated together, and we show how simple Bayesian approaches can combine them, significantly decreasing error rate.     

Confirmation of human protein interaction data by human expression data

Background  

With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome.     

Mapping of a yeast G protein betagamma signaling interaction.

The mating pathway of Saccharomyces cerevisiae is widely used as a model system for G protein-coupled receptor-mediated signal transduction. Following receptor activation by the binding of mating pheromones, G protein betagamma subunits transmit the signal to a MAP kinase cascade, which involves interaction of Gbeta (Ste4p) with the MAP kinase scaffold protein Ste5p. Here, we identify residues in Ste4p required for the interaction with Ste5p. These residues define a new signaling interface close to the Ste20p binding site within the Gbetagamma coiled-coil. Ste4p mutants defective in the Ste5p interaction interact efficiently with Gpa1p (Galpha) and Ste18p (Ggamma) but cannot function in signal transduction because cells expressing these mutants are sterile. Ste4 L65S is temperature-sensitive for its interaction with Ste5p, and also for signaling. We have identified a Ste5p mutant (L196A) that displays a synthetic interaction defect with Ste4 L65S, providing strong evidence that Ste4p and Ste5p interact directly in vivo through an interface that involves hydrophobic residues. The correlation between disruption of the Ste4p-Ste5p interaction and sterility confirms the importance of this interaction in signal transduction. Identification of the Gbetagamma coiled-coil in Ste5p binding may set a precedent for Gbetagamma-effector interactions in more complex organisms.     

A comprehensive set of protein complexes in yeast: mining large scale protein-protein interaction screens

MOTIVATION: The analysis of protein-protein interactions allows for detailed exploration of the cellular machinery. The biochemical purification of protein complexes followed by identification of components by mass spectrometry is currently the method, which delivers the most reliable information--albeit that the data sets are still difficult to interpret. Consolidating individual experiments into protein complexes, especially for high-throughput screens, is complicated by many contaminants, the occurrence of proteins in otherwise dissimilar purifications due to functional re-use and technical limitations in the detection. A non-redundant collection of protein complexes from experimental data would be useful for biological interpretation, but manual assembly is tedious and often inconsistent. RESULTS: Here, we introduce a measure to define similarity within collections of purifications and generate a set of minimally redundant, comprehensive complexes using unsupervised clustering. AVAILABILITY: Programs and results are freely available from http://www.bork.embl-heidelberg.de/Docu/purclust/