Computational prediction of protein-protein interactions

An improved interolog mapping-based computational prediction of protein–protein interactions with increased network coverage

Automated and efficient methods that map ortholog interactions from several organisms and public databases (pDB) are needed to identify new interactions in an organism of interest (interolog mapping).

Template-based structure modeling of protein-protein interactions

The structure of protein-protein complexes can be constructed by using the known structure of other protein complexes as a template. The complex structure templates are generally detected either by homology-based sequence alignments or, given the structure of monomer components, by structure-based comparisons. Critical improvements have been made in recent years by utilizing interface recognition and by recombining monomer and complex template libraries. Encouraging progress has also been witnessed in genome-wide applications of template-based modeling, with modeling accuracy comparable to high-throughput experimental data. Nevertheless, bottlenecks exist due to the incompleteness of the protein-protein complex structure library and the lack of methods for distant homologous template identification and full-length complex structure refinement.

Simplified method for predicting a functional class of proteins in transcription factor complexes

Background

Initiation of transcription is essential for most of the cellular responses to environmental conditions and for cell and tissue specificity. This process is regulated through numerous proteins, their ligands and mutual interactions, as well as interactions with DNA. The key such regulatory proteins are transcription factors (TFs) and transcription co-factors (TcoFs). TcoFs are important since they modulate the transcription initiation process through interaction with TFs. In eukaryotes, transcription requires that TFs form different protein complexes with various nuclear proteins. To better understand transcription regulation, it is important to know the functional class of proteins interacting with TFs during transcription initiation. Such information is not fully available, since not all proteins that act as TFs or TcoFs are yet annotated as such, due to generally partial functional annotation of proteins. In this study we have developed a method to predict, using only sequence composition of the interacting proteins, the functional class of human TF binding partners to be (i) TF, (ii) TcoF, or (iii) other nuclear protein. This allows for complementing the annotation of the currently known pool of nuclear proteins. Since only the knowledge of protein sequences is required in addition to protein interaction, the method should be easily applicable to many species.

Results

Based on experimentally validated interactions between human TFs with different TFs, TcoFs and other nuclear proteins, our two classification systems (implemented as a web-based application) achieve high accuracies in distinguishing TFs and TcoFs from other nuclear proteins, and TFs from TcoFs respectively.

Conclusion

As demonstrated, given the fact that two proteins are capable of forming direct physical interactions and using only information about their sequence composition, we have developed a completely new method for predicting a functional class of TF interacting protein partners with high precision and accuracy.

Comparative analysis of viral protein interaction networks in Hepatitis B virus and Hepatitis C virus infected HCC

Previously, the different mechanisms of HBV infection and HCV infection were studied experimentally. Multiple studies also compared the differential network between HBV induced HCC and HCV induced HCC based on gene expression data. However network level comparison combining viral-human interaction network and dysfunctional protein interaction network for HBV and HCV-HCC has rarely been done before. In this work we did some pioneer job in construction of HBV/HCV viral dysfunctional network in HCC, in hope of investigating viral infection impact on the change of genome expression and eventually, the development of HCC. We found that HBx, the main HBV viral protein, directly acted on the gene groups of cell cycle, which could perfectly explain the dominant cell proliferation effect shown in the dysfunctional network of HBV-HCC. On the other hand, multiple important HCV viral proteins including CORE, NS3 and NS5A were found to target very important cancer related proteins such as TP53 and SMAD3, but no direct targeting to major immune response or inflammation related proteins. Therefore the dominant activation of immune response and inflammation related pathways shown in dysfunctional network of HCV-HCC might not be a direct effect of HCV infection. They might have been an indirect demonstration of activated cancer promoting pathways. Similar approaches may as well be applied to other important virus infection caused human diseases to help elucidate the mechanisms of virus-host interaction, and even help with investigations on anti-virus based therapies. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications.

The development of a universal in silico predictor of protein-protein interactions

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation.

Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration

Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins (choline kinase, PPIase and uromodulin) and three different web-based data search engines focused on protein interaction data retrieval (PSICQUIC, DASMI and BIPS) were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions.

Predicting protein-protein interactions in the post synaptic density

The post synaptic density (PSD) is a specialization of the cytoskeleton at the synaptic junction, composed of hundreds of different proteins. Characterizing the protein components of the PSD and their interactions can help elucidate the mechanism of long-term changes in synaptic plasticity, which underlie learning and memory. Unfortunately, our knowledge of the proteome and interactome of the PSD is still partial and noisy. In this study we describe a computational framework to improve the reconstruction of the PSD network. The approach is based on learning the characteristics of PSD protein interactions from a set of trusted interactions, expanding this set with data collected from large scale repositories, and then predicting novel interaction with proteins that are suspected to reside in the PSD. Using this method we obtained thirty predicted interactions, with more than half of which having supporting evidence in the literature. We discuss in details two of these new interactions, Lrrtm1 with PSD-95 and Src with Capg. The first may take part in a mechanism underlying glutamatergic dysfunction in schizophrenia. The second suggests an alternative mechanism to regulate dendritic spines maturation.

Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge

Recent technological developments in the field of genetics have given rise to an abundance of research tools, such as genome-wide genotyping, that allow researchers to conduct genome-wide association studies (GWAS) for detecting genetic variants that confer increased or decreased susceptibility to disease. However, discovering epistatic, or gene-gene, interactions in high dimensional datasets is a problem due to the computational complexity that results from the analysis of all possible combinations of single-nucleotide polymorphisms (SNPs). A recently explored approach to this problem employs biological expert knowledge, such as pathway or protein-protein interaction information, to guide an analysis by the selection or weighting of SNPs based on this knowledge. Narrowing the evaluation to gene combinations that have been shown to interact experimentally provides a biologically concise reason why those two genes may be detected together statistically. This chapter discusses the challenges of discovering epistatic interactions in GWAS and how biological expert knowledge can be used to facilitate genome-wide genetic studies.

Protein interaction network of Arabidopsis thaliana female gametophyte development identifies novel proteins and relations

Although the female gametophyte in angiosperms consists of just seven cells, it has a complex biological network. In this study, female gametophyte microarray data from Arabidopsis thaliana were integrated into the Arabidopsis interactome database to generate a putative interaction map of the female gametophyte development including proteome map based on biological processes and molecular functions of proteins. Biological and functional groups as well as topological characteristics of the network were investigated by analyzing phytohormones, plant defense, cell death, transporters, regulatory factors, and hydrolases. This approach led to the prediction of critical members and bottlenecks of the network. Seventy-four and 24 upregulated genes as well as 171 and 3 downregulated genes were identified in subtracted networks based on biological processes and molecular function respectively, including novel genes such as the pathogenesis-related protein 4, ER type Ca(2+) ATPase 3, dihydroflavonol reductase, and ATP disulfate isomerase. Biologically important relationships between genes, critical nodes, and new essential proteins such as AT1G26830, AT5G20850, CYP74A, AT1G42396, PR4 and MEA were found in the interactome's network. The positions of novel genes, both upregulated and downregulated, and their relationships with biological pathways, in particular phytohormones, were highlighted in this study.

Computational prediction of protein-protein interactions in Leishmania predicted proteomes

The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

A network-based approach for predicting missing pathway interactions

Embedded within large-scale protein interaction networks are signaling pathways that encode response cascades in the cell. Unfortunately, even for well-studied species like S. cerevisiae, only a fraction of all true protein interactions are known, which makes it difficult to reason about the exact flow of signals and the corresponding causal relations in the network. To help address this problem, we introduce a framework for predicting new interactions that aid connectivity between upstream proteins (sources) and downstream transcription factors (targets) of a particular pathway. Our algorithms attempt to globally minimize the distance between sources and targets by finding a small set of shortcut edges to add to the network. Unlike existing algorithms for predicting general protein interactions, by focusing on proteins involved in specific responses our approach homes-in on pathway-consistent interactions. We applied our method to extend pathways in osmotic stress response in yeast and identified several missing interactions, some of which are supported by published reports. We also performed experiments that support a novel interaction not previously reported. Our framework is general and may be applicable to edge prediction problems in other domains.

Proteome-wide prediction of protein-protein interactions from high-throughput data

In this paper, we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data. The availability of various types of omics data provides great opportunity and also unprecedented challenge to infer the interactome in cells. Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes. The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations. In this paper, we describe the main steps of predicting protein-protein interaction networks and categorize the available approaches to couple the physical and functional linkages. The future topics and the analyses beyond prediction are also discussed and concluded.

Systems biology of fungal infection

Elucidation of pathogenicity mechanisms of the most important human-pathogenic fungi, Aspergillus fumigatus and Candida albicans, has gained great interest in the light of the steadily increasing number of cases of invasive fungal infections. A key feature of these infections is the interaction of the different fungal morphotypes with epithelial and immune effector cells in the human host. Because of the high level of complexity, it is necessary to describe and understand invasive fungal infection by taking a systems biological approach, i.e., by a comprehensive quantitative analysis of the non-linear and selective interactions of a large number of functionally diverse, and frequently multifunctional, sets of elements, e.g., genes, proteins, metabolites, which produce coherent and emergent behaviors in time and space. The recent advances in systems biology will now make it possible to uncover the structure and dynamics of molecular and cellular cause-effect relationships within these pathogenic interactions. We review current efforts to integrate omics and image-based data of host-pathogen interactions into network and spatio-temporal models. The modeling will help to elucidate pathogenicity mechanisms and to identify diagnostic biomarkers and potential drug targets for therapy and could thus pave the way for novel intervention strategies based on novel antifungal drugs and cell therapy.

A classification of bioinformatics algorithms from the viewpoint of maximizing expected accuracy (MEA)

Many estimation problems in bioinformatics are formulated as point estimation problems in a high-dimensional discrete space. In general, it is difficult to design reliable estimators for this type of problem, because the number of possible solutions is immense, which leads to an extremely low probability for every solution-even for the one with the highest probability. Therefore, maximum score and maximum likelihood estimators do not work well in this situation although they are widely employed in a number of applications. Maximizing expected accuracy (MEA) estimation, in which accuracy measures of the target problem and the entire distribution of solutions are considered, is a more successful approach. In this review, we provide an extensive discussion of algorithms and software based on MEA. We describe how a number of algorithms used in previous studies can be classified from the viewpoint of MEA. We believe that this review will be useful not only for users wishing to utilize software to solve the estimation problems appearing in this article, but also for developers wishing to design algorithms on the basis of MEA.

Prediction and characterization of protein-protein interaction networks in swine

Background

Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.

Results

We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.

Conclusion

The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

Network approaches to the functional analysis of microbial proteins

Large amounts of detailed biological data have been generated over the past few decades. Much of these data is freely available in over 1000 online databases; an enticing, but frustrating resource for microbiologists interested in a systems-level view of the structure and function of microbial cells. The frustration engendered by the need to trawl manually through hundreds of databases in order to accumulate information about a gene, protein, pathway, or organism of interest can be alleviated by the use of computational data integration to generated network views of the system of interest. Biological networks can be constructed from a single type of data, such as protein-protein binding information, or from data generated by multiple experimental approaches. In an integrated network, nodes usually represent genes or gene products, while edges represent some form of interaction between the nodes. Edges between nodes may be weighted to represent the probability that the edge exists in vivo. Networks may also be enriched with ontological annotations, facilitating both visual browsing and computational analysis via web service interfaces. In this review, we describe the construction, analysis of both single-data source and integrated networks, and their application to the inference of protein function in microbes.

Separate inputs modulate phosphorylation-dependent and -independent type VI secretion activation

Productive intercellular delivery of cargo by secretory systems requires exquisite temporal and spatial choreography. Our laboratory has demonstrated that the haemolysin co-regulated secretion island I (HSI-I)-encoded type VI secretion system (H1-T6SS) of Pseudomonas aeruginosa transfers effector proteins to other bacterial cells. The activity of these effectors requires cell contact-dependent delivery by the secretion apparatus, and thus their export is highly repressed under planktonic growth conditions. Here we define regulatory pathways that orchestrate efficient secretion by this system. We identified a T6S-associated protein, TagF, as a posttranslational repressor of the H1-T6SS. Strains activated by TagF derepression or stimulated through a previously identified threonine phosphorylation pathway (TPP) share the property of secretory ATPase recruitment to the T6S apparatus, yet display different effector output levels and genetic requirements for their export. We also found that these two pathways respond to distinct stimuli; we identified surface growth as a physiological cue that activates the H1-T6SS exclusively through the TPP. Coordination of posttranslational triggering with cell contact-promoting growth conditions provides a mechanism for the T6SS to avoid wasteful release of effectors.

Efficient algorithms for reconstructing gene content by co-evolution

BACKGROUND

In a previous study we demonstrated that co-evolutionary information can be utilized for improving the accuracy of ancestral gene content reconstruction. To this end, we defined a new computational problem, the Ancestral Co-Evolutionary (ACE) problem, and developed algorithms for solving it.

RESULTS

In the current paper we generalize our previous study in various ways. First, we describe new efficient computational approaches for solving the ACE problem. The new approaches are based on reductions to classical methods such as linear programming relaxation, quadratic programming, and min-cut. Second, we report new computational hardness results related to the ACE, including practical cases where it can be solved in polynomial time.Third, we generalize the ACE problem and demonstrate how our approach can be used for inferring parts of the genomes of non-ancestral organisms. To this end, we describe a heuristic for finding the portion of the genome ('dominant set') that can be used to reconstruct the rest of the genome with the lowest error rate. This heuristic utilizes both evolutionary information and co-evolutionary information.We implemented these algorithms on a large input of the ACE problem (95 unicellular organisms, 4,873 protein families, and 10, 576 of co-evolutionary relations), demonstrating that some of these algorithms can outperform the algorithm used in our previous study. In addition, we show that based on our approach a 'dominant set' cab be used reconstruct a major fraction of a genome (up to 79%) with relatively low error-rate (e.g. 0.11). We find that the 'dominant set' tends to include metabolic and regulatory genes, with high evolutionary rate, and low protein abundance and number of protein-protein interactions.

CONCLUSIONS

The ACE problem can be efficiently extended for inferring the genomes of organisms that exist today. In addition, it may be solved in polynomial time in many practical cases. Metabolic and regulatory genes were found to be the most important groups of genes necessary for reconstructing gene content of an organism based on other related genomes.

Recent advances in protein-protein interaction prediction: experimental and computational methods

INTRODUCTION

Proteins within the cell act as part of complex networks, which allow pathways and processes to function. Therefore, understanding how proteins interact is a significant area of current research.

AREAS COVERED

This review aims to present an overview of key experimental techniques (yeast two-hybrid, tandem affinity purification and protein microarrays) used to discover protein-protein interactions (PPIs), as well as to briefly discuss certain computational methods for predicting protein interactions based on gene localization, phylogenetic information, 3D structural modeling or primary protein sequence data. Due to the large-scale applicability of primary sequence-based methods, the authors have chosen to focus on this strategy for our review. There is an emphasis on a recent algorithm called Protein Interaction Prediction Engine (PIPE) that can predict global PPIs. The readers will discover recent advances both in the practical determination of protein interaction and the strategies that are available to attempt to anticipate interactions without the time and costs of experimental work.

EXPERT OPINION

Global PPI maps can help understand the biology of complex diseases and facilitate the identification of novel drug target sites. This study describes different techniques used for PPI prediction that we believe will significantly impact the development of the field in a new future. We expect to see a growing number of similar techniques capable of large-scale PPI predictions.

Towards the prediction of protein interaction partners using physical docking

Prediction of physical protein-protein interactions represents a key challenge in computational systems biology. This study provides a proof-of-principle that high-throughput in silico protein docking results can be used to predict interaction partners.

Deciphering the whole network of protein interactions for a given proteome (‘interactome') is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well-established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non-redundant potential interactors. We additionally show that true interactions can be distinguished from non-likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed ‘funnel-energy model'; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non-binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks.

PRIN: a predicted rice interactome network

Background

Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa.

Results

To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization.

Conclusions

PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology.

PRIN is available online at http://bis.zju.edu.cn/prin/.

Transient protein-protein interactions: structural, functional, and network properties

Transient interactions, which involve protein interactions that are formed and broken easily, are important in many aspects of cellular function. Here we describe structural and functional properties of transient interactions between globular domains and between globular domains, short peptides, and disordered regions. The importance of posttranslational modifications in transient interactions is also considered. We review techniques used in the detection of the different types of transient protein-protein interactions. We also look at the role of transient interactions within protein-protein interaction networks and consider their contribution to different aspects of these networks.

The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein-protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org.

Filtering and interpreting large-scale experimental protein-protein interaction data

Rarely acting in isolation, it is invariably the physical associations among proteins that define their biological activity, necessitating the study of the cellular meshwork of protein-protein interactions (PPI) before a full appreciation of gene function can be achieved. The past few years have seen a marked expansion in the both the sheer volume and number of organisms for which high-quality interaction data is available, with high-throughput interaction screening and detection techniques showing consistent improvement both in scale and sensitivity. Although techniques for large-scale PPI mapping are increasingly being applied to new organisms, including human, there is a corresponding need to rigorously evaluate, benchmark, and impartially filter the results. This chapter explores methods for PPI dataset evaluation, including a survey of previous techniques applied by landmark studies in the field and a discussion of promising new experimental approaches. We further outline practical suggestions and useful tools for interpreting newly generated PPI data. As the majority of large-scale experimental data has been generated for the budding yeast S. cerevisiae, most of the techniques and datasets described are from the perspective of this model unicellular eukaryote; however, extensions to other organisms including mammals are mentioned where possible.

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins

Background

Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.

Results

We develop suitable statistical resampling schemes that can incorporate these two potential sources of correlation into a single inferential framework. To illustrate our approach we apply it to protein interaction data in yeast and investigate whether the phylogenetic trees of interacting proteins in a panel of yeast species are more similar than would be expected by chance.

Conclusions

While we find only negligible evidence for such increased levels of similarities, our statistical approach allows us to resolve the previously reported contradictory results on the levels of co-evolution induced by protein-protein interactions. We conclude with a discussion as to how we may employ the statistical framework developed here in further functional and evolutionary analyses of biological networks and systems.

Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information

Background

Protein-protein interactions are crucially important for cellular processes. Knowledge of these interactions improves the understanding of cell cycle, metabolism, signaling, transport, and secretion. Information about interactions can hint at molecular causes of diseases, and can provide clues for new therapeutic approaches. Several (usually expensive and time consuming) experimental methods can probe protein - protein interactions. Data sets, derived from such experiments make the development of prediction methods feasible, and make the creation of protein-protein interaction network predicting tools possible.

Methods

Here we report the development of a simple open source program module (OpenPPI_predictor) that can generate a putative protein-protein interaction network for target genomes. This tool uses the orthologous interactome network data from a related, experimentally studied organism.

Results

Results from our predictions can be visualized using the Cytoscape visualization software, and can be piped to downstream processing algorithms. We have employed our program to predict protein-protein interaction network for the human parasite roundworm Brugia malayi, using interactome data from the free living nematode Caenorhabditis elegans.

Availability

The OpenPPI_predictor source code is available from http://tools.neb.com/~posfai/.

Modular composition predicts kinase/substrate interactions

Background

Phosphorylation events direct the flow of signals and metabolites along cellular protein networks. Current annotations of kinase-substrate binding events are far from complete. In this study, we scanned the entire human protein sequences using the PROSITE domain annotation tool to identify patterns of domain composition in kinases and their substrates. We identified statistically enriched pairs of strings of domains (signature pairs) in kinase-substrate couples presented in the 2006 version of the PTM database.

Results

The signature pairs enriched in kinase - substrate binding interactions turned out to be highly specific to kinase subtypes. The resulting list of signature pairs predicted kinase-substrate interactions in validation dataset not used in learning with high statistical accuracy.

Conclusions

The method presented here produces predictions of protein phosphorylation events with high accuracy and mid-level coverage. Our method can be used in expanding the currently available drafts of cell signaling pathways and thus will be an important tool in the development of combination drug therapies targeting complex diseases.

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Background

The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.

Results

We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH^® database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.

Conclusions

It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at http://www.ibmc.msk.ru/PAAS/.

Protein interface conservation across structure space

With the advent of Systems Biology, the prediction of whether two proteins form a complex has become a problem of increased importance. A variety of experimental techniques have been applied to the problem, but three-dimensional structural information has not been widely exploited. Here we explore the range of applicability of such information by analyzing the extent to which the location of binding sites on protein surfaces is conserved among structural neighbors. We find, as expected, that interface conservation is most significant among proteins that have a clear evolutionary relationship, but that there is a significant level of conservation even among remote structural neighbors. This finding is consistent with recent evidence that information available from structural neighbors, independent of classification, should be exploited in the search for functional insights. The value of such structural information is highlighted through the development of a new protein interface prediction method, PredUs, that identifies what residues on protein surfaces are likely to participate in complexes with other proteins. The performance of PredUs, as measured through comparisons with other methods, suggests that relationships across protein structure space can be successfully exploited in the prediction of protein-protein interactions.

Integrative structure modeling of macromolecular assemblies from proteomics data

Proteomics techniques have been used to generate comprehensive lists of protein interactions in a number of species. However, relatively little is known about how these interactions result in functional multiprotein complexes. This gap can be bridged by combining data from proteomics experiments with data from established structure determination techniques. Correspondingly, integrative computational methods are being developed to provide descriptions of protein complexes at varying levels of accuracy and resolution, ranging from complex compositions to detailed atomic structures.

Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.

Role for protein-protein interaction databases in human genetics

Proteomics and the study of protein-protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein-protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein-protein interactions in human genetics and genetic epidemiology. Since protein-protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.

Computational modeling in systems biology

Interactions among cellular constituents play a crucial role in overall cellular function and organization. These interactions can be viewed as being complementary to the usual "parts list" of genes and proteins and, in conjunction with the expression states of these parts, are key to a systems level understanding of the cell. Here, we review computational approaches to the understanding of the functional roles of cellular networks, ranging from "static" models of network topology to dynamical and stochastic simulations.

Development of a Novel Bioinformatics Tool for In Silico Validation of Protein Interactions

Protein interactions are crucial in most biological processes. Several in silico methods have been recently developed to predict them. This paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions. Given a target protein, the approach selects the most likely interactors among the candidates revealed by experimental techniques, but not yet in vivo validated. The sequence and the structural information of the in vivo confirmed proteins and complexes are exploited to evaluate the candidate interactors. Finally, a score is calculated to suggest the most likely interactors of the target protein. As an example, we searched for GRB2 interactors. We ranked a set of 46 candidate interactors by the presented method. These candidates were then reduced to 21, through a score threshold chosen by means of a cross-validation strategy. Among them, the isoform 1 of MAPK14 was in silico confirmed as a GRB2 interactor. Finally, given a set of already confirmed interactors of GRB2, the accuracy and the precision of the approach were 75% and 86%, respectively. In conclusion, the proposed method can be conveniently exploited to select the proteins to be experimentally investigated within a set of potential interactors.

Antibody-array interaction mapping, a new method to detect protein complexes applied to the discovery and study of serum amyloid P interactions with kininogen in human plasma

Protein-protein interactions are fundamentally important in biological processes, but the existing analytical tools have limited ability to sensitively and precisely measure the dynamic composition of protein complexes in biological samples. We report here the development of antibody-array interaction mapping (AAIM) to address that need. We used AAIM to probe interactions among a set of 48 proteins in serum and found several known interactions as well potentially novel interactions, including multiprotein clusters of interactions. A novel interaction initially identified between the innate immune system protein C-reactive protein and the inflammatory protein kininogen (KNG) was confirmed in subsequent experiments to involve serum amyloid P instead of its highly related family member, C-reactive protein. AAIM was used in a variety of formats to further study this interaction. In vitro studies confirmed the ability of the purified proteins to interact and revealed a zinc dependence of the interaction. Studies using plasma samples collected longitudinally following a controlled myocardial infarction revealed no consistent changes in the serum amyloid P-KNG interaction levels but consistent changes in KNG activation and interactions with plasma prekallikrein. These results demonstrate a versatile platform for measuring the dynamic composition of protein complexes in biological samples that should have value for studies of normal and disease-related signaling networks, multiprotein clusters, or enzymatic cascades.

Bioinformatics in bioinorganic chemistry

Bioinformatics is a central discipline in modern life sciences aimed at describing the complex properties of living organisms starting from large-scale data sets of cellular constituents such as genes and proteins. In order for this wealth of information to provide useful biological knowledge, databases and software tools for data collection, analysis and interpretation need to be developed. In this paper, we review recent advances in the design and implementation of bioinformatics resources devoted to the study of metals in biological systems, a research field traditionally at the heart of bioinorganic chemistry. We show how metalloproteomes can be extracted from genome sequences, how structural properties can be related to function, how databases can be implemented, and how hints on interactions can be obtained from bioinformatics.

Bioinformatic analyses of transmembrane transport: novel software for deducing protein phylogeny, topology, and evolution

During the past decade, we have experienced a revolution in the biological sciences resulting from the flux of information generated by genome-sequencing efforts. Our understanding of living organisms, the metabolic processes they catalyze, the genetic systems encoding cellular protein and stable RNA constituents, and the pathological conditions caused by some of these organisms has greatly benefited from the availability of complete genomic sequences and the establishment of comprehensive databases. Many research institutes around the world are now devoting their efforts largely to genome sequencing, data collection and data analysis. In this review, we summarize tools that are in routine use in our laboratory for characterizing transmembrane transport systems. Applications of these tools to specific transporter families are presented. Many of the computational approaches described should be applicable to virtually all classes of proteins and RNA molecules.