A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules

Function of protein phosphatase-1, Glc7, in Saccharomyces cerevisiae

Budding yeast, Saccharomyces cerevisiae, and its close relatives are unique among eukaryotes in having a single gene, GLC7, encoding protein phosphatase-1 (PP1). This enzyme with a highly conserved amino acid sequence controls many processes in all eukaryotic cells. Therefore, the study of Glc7 function offers a unique opportunity to gain a comprehensive understanding of this critical regulatory enzyme. This review summarizes our current knowledge of how Glc7 function modulates processes in the cytoplasm and nucleus. Additionally, global Glc7 regulation is described.

Calmodulin dissociation regulates Myo5 recruitment and function at endocytic sites

Myosins-I are conserved proteins that bear an N-terminal motor head followed by a Tail Homology 1 (TH1) lipid-binding domain. Some myosins-I have an additional C-terminal extension (C(ext)) that promotes Arp2/3 complex-dependent actin polymerization. The head and the tail are separated by a neck that binds calmodulin or calmodulin-related light chains. Myosins-I are known to participate in actin-dependent membrane remodelling. However, the molecular mechanisms controlling their recruitment and their biochemical activities in vivo are far from being understood. In this study, we provided evidence suggesting the existence of an inhibitory interaction between the TH1 domain of the yeast myosin-I Myo5 and its C(ext). The TH1 domain prevented binding of the Myo5 C(ext) to the yeast WIP homologue Vrp1, Myo5 C(ext)-induced actin polymerization and recruitment of the Myo5 C(ext) to endocytic sites. Our data also indicated that calmodulin dissociation from Myo5 weakened the interaction between the neck and TH1 domains and the C(ext). Concomitantly, calmodulin dissociation triggered Myo5 binding to Vrp1, extended the myosin-I lifespan at endocytic sites and activated Myo5-induced actin polymerization.

The use of phage display in neurobiology

Phage display has been extensively used to study protein-protein interactions, receptor- and antibody-binding sites, and immune responses, to modify protein properties, and to select antibodies against a wide range of different antigens. In the format most often used, a polypeptide is displayed on the surface of a filamentous phage by genetic fusion to one of the coat proteins, creating a chimeric coat protein, and coupling phenotype (the protein) to genotype (the gene within). As the gene encoding the chimeric coat protein is packaged within the phage, selection of the phage on the basis of the binding properties of the polypeptide displayed on the surface simultaneously results in the isolation of the gene encoding the polypeptide. This unit describes the background to the technique, and illustrates how it has been applied to a number of different problems, each of which has its neurobiological counterparts. Although this overview concentrates on the use of filamentous phage, which is the most popular platform, other systems are also described.

DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein-protein interactions

Protein-protein complexes play key roles in all cellular signal transduction processes. We have developed a fast and accurate computational approach to predict changes in the binding free energy upon alanine mutations in protein-protein interfaces. The approach is based on a knowledge-based scoring function, DrugScore(PPI), for which pair potentials were derived from 851 complex structures and adapted against 309 experimental alanine scanning results. Based on this approach, we developed the DrugScore(PPI) webserver. The input consists of a protein-protein complex structure; the output is a summary table and bar plot of binding free energy differences for wild-type residue-to-Ala mutations. The results of the analysis are mapped on the protein-protein complex structure and visualized using J mol. A single interface can be analyzed within a few minutes. Our approach has been successfully validated by application to an external test set of 22 alanine mutations in the interface of Ras/RalGDS. The DrugScore(PPI) webserver is primarily intended for identifying hotspot residues in protein-protein interfaces, which provides valuable information for guiding biological experiments and in the development of protein-protein interaction modulators. The DrugScore(PPI) Webserver, accessible at http://cpclab.uni-duesseldorf.de/dsppi, is free and open to all users with no login requirement.

Purification of bacteriophage M13 by anion exchange chromatography

M13 is a non-lytic filamentous bacteriophage (phage). It has been used widely in phage display technology for displaying foreign peptides, and also for studying macromolecule structures and interactions. Traditionally, this phage has been purified by cesium chloride (CsCl) density gradient ultracentrifugation which is highly laborious and time consuming. In the present study, a simple, rapid and efficient method for the purification of M13 based on anion exchange chromatography was established. A pre-packed SepFast Super Q column connected to a fast protein liquid chromatography (FPLC) system was employed to capture released phages in clarified Escherichia coli fermented broth. An average yield of 74% was obtained from a packed bed mode elution using citrate buffer (pH 4), containing 1.5 M NaCl at 1 ml/min flow rate. The purification process was shortened substantially to less than 2 h from 18 h in the conventional ultracentrifugation method. SDS-PAGE revealed that the purity of particles was comparable to that of CsCl gradient density ultracentrifugation method. Plaque forming assay showed that the purified phages were still infectious.

MOTIPS: automated motif analysis for predicting targets of modular protein domains

Background

Many protein interactions, especially those involved in signaling, involve short linear motifs consisting of 5-10 amino acid residues that interact with modular protein domains such as the SH3 binding domains and the kinase catalytic domains. One straightforward way of identifying these interactions is by scanning for matches to the motif against all the sequences in a target proteome. However, predicting domain targets by motif sequence alone without considering other genomic and structural information has been shown to be lacking in accuracy.

Results

We developed an efficient search algorithm to scan the target proteome for potential domain targets and to increase the accuracy of each hit by integrating a variety of pre-computed features, such as conservation, surface propensity, and disorder. The integration is performed using naïve Bayes and a training set of validated experiments.

Conclusions

By integrating a variety of biologically relevant features to predict domain targets, we demonstrated a notably improved prediction of modular protein domain targets. Combined with emerging high-resolution data of domain specificities, we believe that our approach can assist in the reconstruction of many signaling pathways.

Comparative transcriptomic and proteomic profiling of industrial wine yeast strains

The geno- and phenotypic diversity of commercial Saccharomyces cerevisiae wine yeast strains provides an opportunity to apply the system-wide approaches that are reasonably well established for laboratory strains to generate insight into the functioning of complex cellular networks in industrial environments. We have previously analyzed the transcriptomes of five industrial wine yeast strains at three time points during alcoholic fermentation. Here, we extend the comparative approach to include an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis of two of the previously analyzed wine yeast strains at the same three time points during fermentation in synthetic wine must. The data show that differences in the transcriptomes of the two strains at a given time point rather accurately reflect differences in the corresponding proteomes independently of the gene ontology (GO) category, providing strong support for the biological relevance of comparative transcriptomic data sets in yeast. In line with previous observations, the alignment proves to be less accurate when assessing intrastrain changes at different time points. In this case, differences between the transcriptome and proteome appear to be strongly dependent on the GO category of the corresponding genes. The data in particular suggest that metabolic enzymes and the corresponding genes appear to be strongly correlated over time and between strains, suggesting a strong transcriptional control of such enzymes. The data also allow the generation of hypotheses regarding the molecular origin of significant differences in phenotypic traits between the two strains.

Predicting protein-protein interactions in unbalanced data using the primary structure of proteins

BACKGROUND

Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks.

RESULTS

This study presents a method for PPI prediction based only on sequence information, which contributes in three aspects. First, we propose a probability-based mechanism for transforming protein sequences into feature vectors. Second, the proposed predictor is designed with an efficient classification algorithm, where the efficiency is essential for handling highly unbalanced datasets. Third, the proposed PPI predictor is assessed with several unbalanced datasets with different positive-to-negative ratios (from 1:1 to 1:15). This analysis provides solid evidence that the degree of dataset imbalance is important to PPI predictors.

CONCLUSIONS

Dealing with data imbalance is a key issue in PPI prediction since there are far fewer interacting protein pairs than non-interacting ones. This article provides a comprehensive study on this issue and develops a practical tool that achieves both good prediction performance and efficiency using only protein sequence information.

SAROTUP: scanner and reporter of target-unrelated peptides

As epitope mimics, mimotopes have been widely utilized in the study of epitope prediction and the development of new diagnostics, therapeutics, and vaccines. Screening the random peptide libraries constructed with phage display or any other surface display technologies provides an efficient and convenient approach to acquire mimotopes. However, target-unrelated peptides creep into mimotopes from time to time through binding to contaminants or other components of the screening system. In this study, we present SAROTUP, a free web tool for scanning, reporting and excluding possible target-unrelated peptides from real mimotopes. Preliminary tests show that SAROTUP is efficient and capable of improving the accuracy of mimotope-based epitope mapping. It is also helpful for the development of mimotope-based diagnostics, therapeutics, and vaccines.

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.

Computational approaches for detecting protein complexes from protein interaction networks: a survey

Background

Most proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid) have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions.

Results

Given the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field.

Conclusions

Experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological evidences can be better incorporated into the detection process to maximize the exploitation of the increasing wealth of biological knowledge available.

Predicting the protein-protein interactions using primary structures with predicted protein surface

BACKGROUND

Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.

RESULTS

This study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures.

CONCLUSION

This work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an F-measure of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information.

Exploring and profiling protein function with peptide arrays

Development of array technologies started in the late 1980s and was first extensively applied to DNA arrays especially in the genomic field. Today this technique has become a powerful tool for high-throughput approaches in biology and chemistry. Progresses were mainly driven by the human genome project and were associated with the development of several new technologies, which led to the onset of additional "omic" topics like proteomics, glycomics, antibodyomics or lipidomics. The main characteristics of the array technology are (i) spatially addressable immobilization of a huge number of different capture molecules; (ii) probing the array in a simultaneous and highly parallel manner with a biological sample; (iii) tendency towards miniaturization of the arrays; and (iv) software-supported read-out and data analysis. We review some general concepts about peptide arrays on planar supports and point out technical aspects concerning the generation of peptide microarrays. Finally, we discuss recent applications by describing relevant literature.

Physical mechanisms of signal integration by WASP family proteins

The proteins of the Wiskott-Aldrich syndrome protein (WASP) family are activators of the ubiquitous actin nucleation factor, the Arp2/3 complex. WASP family proteins contain a C-terminal VCA domain that binds and activates the Arp2/3 complex in response to numerous inputs, including Rho family GTPases, phosphoinositide lipids, SH3 domain-containing proteins, kinases, and phosphatases. In the archetypal members of the family, WASP and N-WASP, these signals are integrated through two levels of regulation, an allosteric autoinhibitory interaction, in which the VCA is sequestered from the Arp2/3 complex, and dimerization/oligomerization, in which multi-VCA complexes are better activators of the Arp2/3 complex than monomers. Here, we review the structural, biochemical, and biophysical details of these mechanisms and illustrate how they work together to control WASP activity in response to multiple inputs. These regulatory principles, derived from studies of WASP and N-WASP, are likely to apply broadly across the family.

Protein complex prediction based on simultaneous protein interaction network

MOTIVATION

The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions.

RESULTS

In this article, a method of refining PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network (SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions.

AVAILABILITY

http://code.google.com/p/simultaneous-pin/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Partially-supervised protein subclass discovery with simultaneous annotation of functional residues

BACKGROUND

The study of functional subfamilies of protein domain families and the identification of the residues which determine substrate specificity is an important question in the analysis of protein domains. One way to address this question is the use of clustering methods for protein sequence data and approaches to predict functional residues based on such clusterings. The locations of putative functional residues in known protein structures provide insights into how different substrate specificities are reflected on the protein structure level.

RESULTS

We have developed an extension of the context-specific independence mixture model clustering framework which allows for the integration of experimental data. As these are usually known only for a few proteins, our algorithm implements a partially-supervised learning approach. We discover domain subfamilies and predict functional residues for four protein domain families: phosphatases, pyridoxal dependent decarboxylases, WW and SH3 domains to demonstrate the usefulness of our approach.

CONCLUSION

The partially-supervised clustering revealed biologically meaningful subfamilies even for highly heterogeneous domains and the predicted functional residues provide insights into the basis of the different substrate specificities.

Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins

A genome-scale specificity and interaction map for yeast SH3 domain-containing proteins reveal how family members show selective binding to target proteins and predicts the dynamic localization of new candidate endocytosis proteins.

SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast, Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes.

Significant diversity exists in protein structure and function, yet certain structural domains are used repeatedly across species to execute similar functions. The SH3 domain is one such common structural domain. It is found in signaling proteins and mediates protein–protein interactions by binding to short peptide sequences generally composed of proline. To investigate both the generality and selectivity of peptide binding by SH3 domains, we examined peptide specificity for almost all SH3 domains encoded within the proteome of the budding yeast, Saccharomyces cerevisiae, using a range of experimental methods. We found that although most of the intrinsic binding specificity for SH3 domains can be summarized by the two previously described canonical binding modes, each individual SH3 domain that we studied utilizes unique features of its cognate ligand to achieve binding selectivity. Moreover, some domains exhibit binding specificities that are distinct from the two canonical classes. We integrated peptide-SH3 domain binding data from three complementary screening techniques using a Bayesian statistical model to generate a protein–protein interaction network for the budding yeast SH3 domain family. This network was highly enriched in endocytosis proteins and their interactions. By examining these interactions in detail, we show that our SH3 domain network can be used to predict the temporal localization of several previously uncharacterized proteins to dynamic complexes that orchestrate the process of endocytosis.

Protein-protein interactions of tandem affinity purified protein kinases from rice

Eighty-eight rice (Oryza sativa) cDNAs encoding rice leaf expressed protein kinases (PKs) were fused to a Tandem Affinity Purification tag (TAP-tag) and expressed in transgenic rice plants. The TAP-tagged PKs and interacting proteins were purified from the T1 progeny of the transgenic rice plants and identified by tandem mass spectrometry. Forty-five TAP-tagged PKs were recovered in this study and thirteen of these were found to interact with other rice proteins with a high probability score. In vivo phosphorylated sites were found for three of the PKs. A comparison of the TAP-tagged data from a combined analysis of 129 TAP-tagged rice protein kinases with a concurrent screen using yeast two hybrid methods identified an evolutionarily new rice protein that interacts with the well conserved cell division cycle 2 (CDC2) protein complex.

Intrinsic protein disorder and interaction promiscuity are widely associated with dosage sensitivity

Why are genes harmful when they are overexpressed? By testing possible causes of overexpression phenotypes in yeast, we identify intrinsic protein disorder as an important determinant of dosage sensitivity. Disordered regions are prone to make promiscuous molecular interactions when their concentration is increased, and we demonstrate that this is the likely cause of pathology when genes are overexpressed. We validate our findings in two animals, Drosophila melanogaster and Caenorhabditis elegans. In mice and humans the same properties are strongly associated with dosage-sensitive oncogenes, such that mass-action-driven molecular interactions may be a frequent cause of cancer. Dosage-sensitive genes are tightly regulated at the transcriptional, RNA, and protein levels, which may serve to prevent harmful increases in protein concentration under physiological conditions. Mass-action-driven interaction promiscuity is a single theoretical framework that can be used to understand, predict, and possibly treat the effects of increased gene expression in evolution and disease.

Role of Inn1 and its interactions with Hof1 and Cyk3 in promoting cleavage furrow and septum formation in S. cerevisiae

Cytokinesis requires coordination of actomyosin ring (AMR) contraction with rearrangements of the plasma membrane and extracellular matrix. In Saccharomyces cerevisiae, new membrane, the chitin synthase Chs2 (which forms the primary septum [PS]), and the protein Inn1 are all delivered to the division site upon mitotic exit even when the AMR is absent. Inn1 is essential for PS formation but not for Chs2 localization. The Inn1 C-terminal region is necessary for localization, and distinct PXXP motifs in this region mediate functionally important interactions with SH3 domains in the cytokinesis proteins Hof1 (an F-BAR protein) and Cyk3 (whose overexpression can restore PS formation in inn1Δ cells). The Inn1 N terminus resembles C2 domains but does not appear to bind phospholipids; nonetheless, when overexpressed or fused to Hof1, it can provide Inn1 function even in the absence of the AMR. Thus, Inn1 and Cyk3 appear to cooperate in activating Chs2 for PS formation, which allows coordination of AMR contraction with ingression of the cleavage furrow.

Structural, functional, and bioinformatic studies demonstrate the crucial role of an extended peptide binding site for the SH3 domain of yeast Abp1p

SH3 domains, which are among the most frequently occurring protein interaction modules in nature, bind to peptide targets ranging in length from 7 to more than 25 residues. Although the bulk of studies on the peptide binding properties of SH3 domains have focused on interactions with relatively short peptides (less than 10 residues), a number of domains have been recently shown to require much longer sequences for optimal binding affinity. To gain greater insight into the binding mechanism and biological importance of interactions between an SH3 domain and extended peptide sequences, we have investigated interactions of the yeast Abp1p SH3 domain (AbpSH3) with several physiologically relevant 17-residue target peptide sequences. To obtain a molecular model for AbpSH3 interactions, we solved the structure of the AbpSH3 bound to a target peptide from the yeast actin patch kinase, Ark1p. Peptide target complexes from binding partners Scp1p and Sjl2p were also characterized, revealing that the AbpSH3 uses a common extended interface for interaction with these peptides, despite K(d) values for these peptides ranging from 0.3 to 6 mum. Mutagenesis studies demonstrated that residues across the whole 17-residue binding site are important both for maximal in vitro binding affinity and for in vivo function. Sequence conservation analysis revealed that both the AbpSH3 and its extended target sequences are highly conserved across diverse fungal species as well as higher eukaryotes. Our data imply that the AbpSH3 must bind extended target sites to function efficiently inside the cell.

Formin-binding proteins: modulators of formin-dependent actin polymerization

Formins represent a major branch of actin nucleators along with the Arp2/3 complex, Spire and Cordon-bleu. Formin-mediated actin nucleation requires the formin homology 2 domain and, although the nucleation per se does not require additional factors, formin-binding proteins have been shown to be essential for the regulation of formin-dependent actin assembly in vivo. This regulation could be accomplished by formin-binding proteins being directly involved in formin-driven actin nucleation, by formin-binding proteins influencing the activated state of the formins, by linking formin-driven actin polymerization to Arp2/3 driven actin polymerization, or by influencing the subcellular localization of the formins. This review article will focus on mammalian formin-binding proteins and their roles during vital cellular processes, such as cell migration, cell division and intracellular trafficking.

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

Background

A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.

Results

We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.

Conclusion

Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN.

Regulators of yeast endocytosis identified by systematic quantitative analysis

Endocytosis of receptors at the plasma membrane is controlled by a complex mechanism that includes clathrin, adaptors, and actin regulators. Many of these proteins are conserved in yeast yet lack observable mutant phenotypes, which suggests that yeast endocytosis may be subject to different regulatory mechanisms. Here, we have systematically defined genes required for internalization using a quantitative genome-wide screen that monitors localization of the yeast vesicle-associated membrane protein (VAMP)/synaptobrevin homologue Snc1. Genetic interaction mapping was used to place these genes into functional modules containing known and novel endocytic regulators, and cargo selectivity was evaluated by an array-based comparative analysis. We demonstrate that clathrin and the yeast AP180 clathrin adaptor proteins have a cargo-specific role in Snc1 internalization. We additionally identify low dye binding 17 (LDB17) as a novel conserved component of the endocytic machinery. Ldb17 is recruited to cortical actin patches before actin polymerization and regulates normal coat dynamics and actin assembly. Our findings highlight the conserved machinery and reveal novel mechanisms that underlie endocytic internalization.

A core-attachment based method to detect protein complexes in PPI networks

BACKGROUND

How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent organization within protein complexes.

RESULTS

To provide insights into the organization of protein complexes, this paper presents a novel core-attachment based method (COACH) which detects protein complexes in two stages. It first detects protein-complex cores as the "hearts" of protein complexes and then includes attachments into these cores to form biologically meaningful structures. We evaluate and analyze our predicted protein complexes from two aspects. First, we perform a comprehensive comparison between our proposed method and existing techniques by comparing the predicted complexes against benchmark complexes. Second, we also validate the core-attachment structures using various biological evidence and knowledge.

CONCLUSION

Our proposed COACH method has been applied on two different yeast PPI networks and the experimental results show that COACH performs significantly better than the state-of-the-art techniques. In addition, the identified complexes with core-attachment structures are demonstrated to match very well with existing biological knowledge and thus provide more insights for future biological study.

SH3 domains of Grb2 adaptor bind to PXpsiPXR motifs within the Sos1 nucleotide exchange factor in a discriminate manner

Ubiquitously encountered in a wide variety of cellular processes, the Grb2-Sos1 interaction is mediated through the combinatorial binding of nSH3 and cSH3 domains of Grb2 to various sites containing PXpsiPXR motifs within Sos1. Here, using isothermal titration calorimetry, we demonstrate that while the nSH3 domain binds with affinities in the physiological range to all four sites containing PXpsiPXR motifs, designated S1, S2, S3, and S4, the cSH3 domain can only do so at the S1 site. Further scrutiny of these sites yields rationale for the recognition of various PXpsiPXR motifs by the SH3 domains in a discriminate manner. Unlike the PXpsiPXR motifs at S2, S3, and S4 sites, the PXpsiPXR motif at the S1 site is flanked at its C-terminus with two additional arginine residues that are absolutely required for high-affinity binding of the cSH3 domain. In striking contrast, these two additional arginine residues augment the binding of the nSH3 domain to the S1 site, but their role is not critical for the recognition of S2, S3, and S4 sites. Site-directed mutagenesis suggests that the two additional arginine residues flanking the PXpsiPXR motif at the S1 site contribute to free energy of binding via the formation of salt bridges with specific acidic residues in SH3 domains. Molecular modeling is employed to project these novel findings into the 3D structures of SH3 domains in complex with a peptide containing the PXpsiPXR motif and flanking arginine residues at the S1 site. Taken together, this study furthers our understanding of the assembly of a key signaling complex central to cellular machinery.

Protein-protein interaction based on pairwise similarity

Background

Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines.

Results

To assess the ability of the proposed method to recognize the difference between "interacted" and "non-interacted" proteins pairs, we applied it on different datasets from the available yeast saccharomyces cerevisiae protein interaction. The proposed method achieved reasonable improvement over the existing state-of-the-art methods for PPI prediction.

Conclusion

Pairwise similarity score provides a relevant measure of similarity between protein sequences. This similarity incorporates biological knowledge about proteins and it is extremely powerful when combined with support vector machine to predict PPI.

Shifted Transversal Design smart-pooling for high coverage interactome mapping

"Smart-pooling," in which test reagents are multiplexed in a highly redundant manner, is a promising strategy for achieving high efficiency, sensitivity, and specificity in systems-level projects. However, previous applications relied on low redundancy designs that do not leverage the full potential of smart-pooling, and more powerful theoretical constructions, such as the Shifted Transversal Design (STD), lack experimental validation. Here we evaluate STD smart-pooling in yeast two-hybrid (Y2H) interactome mapping. We employed two STD designs and two established methods to perform ORFeome-wide Y2H screens with 12 baits. We found that STD pooling achieves similar levels of sensitivity and specificity as one-on-one array-based Y2H, while the costs and workloads are divided by three. The screening-sequencing approach is the most cost- and labor-efficient, yet STD identifies about twofold more interactions. Screening-sequencing remains an appropriate method for quickly producing low-coverage interactomes, while STD pooling appears as the method of choice for obtaining maps with higher coverage.

She3p possesses a novel activity required for ASH1 mRNA localization in Saccharomyces cerevisiae

Intracellular and intercellular polarity requires that specific proteins be sorted to discreet locations within and between cells. One mechanism for sorting proteins is through RNA localization. In Saccharomyces cerevisiae, ASH1 mRNA localizes to the distal tip of the bud, resulting in the asymmetric sorting of the transcriptional repressor Ash1p. ASH1 mRNA localization requires four cis-acting localization elements and the trans-acting factors Myo4p, She3p, and She2p. Myo4p is a type V myosin motor that functions to directly transport ASH1 mRNA to the bud. She2p is an RNA-binding protein that directly interacts with the ASH1 mRNA cis-acting elements. Currently, the role for She3p in ASH1 mRNA localization is as an adaptor protein, since it can simultaneously associate with Myo4p and She2p. Here, we present data for two novel mutants of She3p, S348E and the double mutant S343E S361E, that are defective for ASH1 mRNA localization, and yet both of these mutants retain the ability to associate with Myo4p and She2p. These observations suggest that She3p possesses a novel activity required for ASH1 mRNA localization, and our data imply that this function is related to the ability of She3p to associate with ASH1 mRNA. Interestingly, we determined that She3p is phosphorylated, and global mass spectrometry approaches have determined that Ser 343, 348, and 361 are sites of phosphorylation, suggesting that the novel function for She3p could be negatively regulated by phosphorylation. The present study reveals that the current accepted model for ASH1 mRNA localization does not fully account for the function of She3p in ASH1 mRNA localization.

Charting the molecular network of the drug target Bcr-Abl

The tyrosine kinase Bcr-Abl causes chronic myeloid leukemia and is the cognate target of tyrosine kinase inhibitors like imatinib. We have charted the protein-protein interaction network of Bcr-Abl by a 2-pronged approach. Using a monoclonal antibody we have first purified endogenous Bcr-Abl protein complexes from the CML K562 cell line and characterized the set of most tightly-associated interactors by MS. Nine interactors were subsequently subjected to tandem affinity purifications/MS analysis to obtain a molecular interaction network of some hundred cellular proteins. The resulting network revealed a high degree of interconnection of 7 "core" components around Bcr-Abl (Grb2, Shc1, Crk-I, c-Cbl, p85, Sts-1, and SHIP-2), and their links to different signaling pathways. Quantitative proteomics analysis showed that tyrosine kinase inhibitors lead to a disruption of this network. Certain components still appear to interact with Bcr-Abl in a phosphotyrosine-independent manner. We propose that Bcr-Abl and other drug targets, rather than being considered as single polypeptides, can be considered as complex protein assemblies that remodel upon drug action.

Prediction of protein binding regions in disordered proteins

Many disordered proteins function via binding to a structured partner and undergo a disorder-to-order transition. The coupled folding and binding can confer several functional advantages such as the precise control of binding specificity without increased affinity. Additionally, the inherent flexibility allows the binding site to adopt various conformations and to bind to multiple partners. These features explain the prevalence of such binding elements in signaling and regulatory processes. In this work, we report ANCHOR, a method for the prediction of disordered binding regions. ANCHOR relies on the pairwise energy estimation approach that is the basis of IUPred, a previous general disorder prediction method. In order to predict disordered binding regions, we seek to identify segments that are in disordered regions, cannot form enough favorable intrachain interactions to fold on their own, and are likely to gain stabilizing energy by interacting with a globular protein partner. The performance of ANCHOR was found to be largely independent from the amino acid composition and adopted secondary structure. Longer binding sites generally were predicted to be segmented, in agreement with available experimentally characterized examples. Scanning several hundred proteomes showed that the occurrence of disordered binding sites increased with the complexity of the organisms even compared to disordered regions in general. Furthermore, the length distribution of binding sites was different from disordered protein regions in general and was dominated by shorter segments. These results underline the importance of disordered proteins and protein segments in establishing new binding regions. Due to their specific biophysical properties, disordered binding sites generally carry a robust sequence signal, and this signal is efficiently captured by our method. Through its generality, ANCHOR opens new ways to study the essential functional sites of disordered proteins.

Intrinsically unstructured/disordered proteins (IUPs/IDPs) do not adopt a stable structure in isolation but exist as a highly flexible ensemble of conformations. Despite the lack of a well-defined structure these proteins carry out important functions. Many IUPs/IDPs function via binding specifically to other macromolecules that involves a disorder-to-order transition. The molecular recognition functions of IUPs/IDPs include regulatory and signaling interactions where binding to multiple partners and high-specificity/low-affinity interactions play a crucial role. Due to their specific functional and structural properties, these binding regions have distinct properties compared to both globular proteins and disordered regions in general. Here, we present a general method to identify disordered binding regions from the amino acid sequence. Our method targets the essential feature of these regions: they behave in a characteristically different manner in isolation than bound to their partner protein. This prediction method allows us to compare the binding properties of short and long binding sites. The evolutionary relationship between the amount of disordered binding regions and general disordered regions in various organisms was also analyzed. Our results suggest that disordered binding regions can be recognized even without taking into account their adopted secondary structure or their specific binding partner.

Biological process linkage networks

Background

The traditional approach to studying complex biological networks is based on the identification of interactions between internal components of signaling or metabolic pathways. By comparison, little is known about interactions between higher order biological systems, such as biological pathways and processes.

We propose a methodology for gleaning patterns of interactions between biological processes by analyzing protein-protein interactions, transcriptional co-expression and genetic interactions. At the heart of the methodology are the concept of Linked Processes and the resultant network of biological processes, the Process Linkage Network (PLN).

Results

We construct, catalogue, and analyze different types of PLNs derived from different data sources and different species. When applied to the Gene Ontology, many of the resulting links connect processes that are distant from each other in the hierarchy, even though the connection makes eminent sense biologically. Some others, however, carry an element of surprise and may reflect mechanisms that are unique to the organism under investigation. In this aspect our method complements the link structure between processes inherent in the Gene Ontology, which by its very nature is species-independent.

As a practical application of the linkage of processes we demonstrate that it can be effectively used in protein function prediction, having the power to increase both the coverage and the accuracy of predictions, when carefully integrated into prediction methods.

Conclusions

Our approach constitutes a promising new direction towards understanding the higher levels of organization of the cell as a system which should help current efforts to re-engineer ontologies and improve our ability to predict which proteins are involved in specific biological processes.

Domain Interaction Footprint: a multi-classification approach to predict domain-peptide interactions

MOTIVATION

The flow of information within cellular pathways largely relies on specific protein-protein interactions. Discovering such interactions that are mostly mediated by peptide recognition modules (PRM) is therefore a fundamental step towards unravelling the complexity of varying pathways. Since peptides can be recognized by more than one PRM and high-throughput experiments are both time consuming and expensive, it would be preferable to narrow down all potential peptide ligands for one specific PRM by a computational method. We at first present Domain Interaction Footprint (DIF) a new approach to predict binding peptides to PRMs merely based on the sequence of the peptides. Second, we show that our method is able to create a multi-classification model that assesses the binding specificity of a given peptide to all examined PRMs at once.

RESULTS

We first applied our approach to a previously investigated dataset of different SH3 domains and predicted their appropriate peptide ligands with an exceptionally high accuracy. This result outperforms all recent methods trained on the same dataset. Furthermore, we used our technique to build two multi-classification models (SH3 and PDZ domains) to predict the interaction preference between a peptide and every single domain in the corresponding domain family at once. Predicting the domain specificity most reliably, our proposed approach can be seen as a first step towards a complete multi-domain classification model comprised of all domains of one family. Such a comprehensive domain specificity model would benefit the quest for highly specific peptide ligands interacting solely with the domain of choice.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A top-down approach to infer and compare domain-domain interactions across eight model organisms

Knowledge of specific domain-domain interactions (DDIs) is essential to understand the functional significance of protein interaction networks. Despite the availability of an enormous amount of data on protein-protein interactions (PPIs), very little is known about specific DDIs occurring in them. Here, we present a top-down approach to accurately infer functionally relevant DDIs from PPI data. We created a comprehensive, non-redundant dataset of 209,165 experimentally-derived PPIs by combining datasets from five major interaction databases. We introduced an integrated scoring system that uses a novel combination of a set of five orthogonal scoring features covering the probabilistic, evolutionary, evidence-based, spatial and functional properties of interacting domains, which can map the interacting propensity of two domains in many dimensions. This method outperforms similar existing methods both in the accuracy of prediction and in the coverage of domain interaction space. We predicted a set of 52,492 high-confidence DDIs to carry out cross-species comparison of DDI conservation in eight model species including human, mouse, Drosophila, C. elegans, yeast, Plasmodium, E. coli and Arabidopsis. Our results show that only 23% of these DDIs are conserved in at least two species and only 3.8% in at least 4 species, indicating a rather low conservation across species. Pair-wise analysis of DDI conservation revealed a ‘sliding conservation’ pattern between the evolutionarily neighboring species. Our methodology and the high-confidence DDI predictions generated in this study can help to better understand the functional significance of PPIs at the modular level, thus can significantly impact further experimental investigations in systems biology research.

Accurate prediction of peptide binding sites on protein surfaces

Many important protein-protein interactions are mediated by the binding of a short peptide stretch in one protein to a large globular segment in another. Recent efforts have provided hundreds of examples of new peptides binding to proteins for which a three-dimensional structure is available (either known experimentally or readily modeled) but where no structure of the protein-peptide complex is known. To address this gap, we present an approach that can accurately predict peptide binding sites on protein surfaces. For peptides known to bind a particular protein, the method predicts binding sites with great accuracy, and the specificity of the approach means that it can also be used to predict whether or not a putative or predicted peptide partner will bind. We used known protein-peptide complexes to derive preferences, in the form of spatial position specific scoring matrices, which describe the binding-site environment in globular proteins for each type of amino acid in bound peptides. We then scan the surface of a putative binding protein for sites for each of the amino acids present in a peptide partner and search for combinations of high-scoring amino acid sites that satisfy constraints deduced from the peptide sequence. The method performed well in a benchmark and largely agreed with experimental data mapping binding sites for several recently discovered interactions mediated by peptides, including RG-rich proteins with SMN domains, Epstein-Barr virus LMP1 with TRADD domains, DBC1 with Sir2, and the Ago hook with Argonaute PIWI domain. The method, and associated statistics, is an excellent tool for predicting and studying binding sites for newly discovered peptides mediating critical events in biology.

General M13 phage display: M13 phage display in identification and characterization of protein-protein interactions

In M13 phage display, proteins and peptides are exposed on one of the surface proteins of filamentous phage particles and become accessible to affinity enrichment against a bait of interest. We describe the construction of fragmented whole genome and gene fragment phage display libraries and interaction selection by panning. This strategy allows the identification and characterization of interacting proteins on a genomic scale by screening the fragmented "proteome" against protein baits. Gene fragment libraries allow a more in depth characterization of the protein-protein interaction site by identification of the protein region involved in the interaction.

The SH3 domains of two PCH family members cooperate in assembly of the Schizosaccharomyces pombe contractile ring

Schizosaccharomyces pombe cdc15 homology (PCH) family members participate in many cellular processes by bridging the plasma membrane and cytoskeleton. Their F-BAR domains bind and curve membranes, whereas other domains, typically SH3 domains, are expected to provide cytoskeletal links. We tested this prevailing model of functional division in the founding member of the family, Cdc15, which is essential for cytokinesis in S. pombe, and in the related PCH protein, Imp2. We find that the distinct functions of Imp2 and Cdc15 are SH3 domain independent. However, the Cdc15 and Imp2 SH3 domains share an essential role in recruiting proteins to the contractile ring, including Pxl1 and Fic1. Together, Pxl1 and Fic1, a previously uncharacterized C2 domain protein, add structural integrity to the contractile ring and prevent it from fragmenting during division. Our data indicate that the F-BAR proteins Cdc15 and Imp2 contribute to a single biological process with both distinct and overlapping functions.