Decomposing protein networks into domain-domain interactions

The application of novel experimental techniques has generated large networks of protein-protein interactions. Frequently, important information on the structure and cellular function of protein-protein interactions can be gained from the domains of interacting proteins. We have designed a Cytoscape plugin that decomposes interacting proteins into their respective domains and computes a putative network of corresponding domain-domain interactions. To this end, the network graph of proteins has been extended by additional node and edge types for domain interactions, including different node and edge shapes and coloring schemes used for visualization. An additional plugin provides supplementary web links to Internet resources on domain function and structure. AVAILABILITY: Both Cytoscape plugins can be downloaded from http://www.cytoscape.org     

Protein domain annotation with predicted domain-domain interaction networks

This paper presents a framework for annotating protein domains with predicted domain-domain interaction networks. Specially, domain annotation is formalized as a multi-class classification problem in this work. The numerical experiments on InterPro domains show promising results, which proves the efficiency of our proposed methods.     

IDDI: integrated domain-domain interaction and protein interaction analysis system

Background

Deciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Several resources for the computationally predicted DDI have been generated but they are scattered in various places and their prediction show erratic performances. A well-organized PPI and DDI analysis system integrating these data with fair scoring system is necessary.

Method

We integrated three structure-based DDI datasets and twenty computationally predicted DDI datasets and constructed an interaction analysis system, named IDDI, which enables to browse protein and domain interactions with their relationships. To integrate heterogeneous DDI information, a novel scoring scheme is introduced to determine the reliability of DDI by considering the prediction scores of each DDI and the confidence levels of each prediction method in the datasets, and independencies between predicted datasets. In addition, we connected this DDI information to the comprehensive PPI information and developed a unified interface for the interaction analysis exploring interaction networks at both protein and domain level.

Result

IDDI provides 204,705 DDIs among total 7,351 Pfam domains in the current version. The result presents that total number of DDIs is increased eight times more than that of previous studies. Due to the increment of data, 50.4% of PPIs could be correlated with DDIs which is more than twice of previous resources. Newly designed scoring scheme outperformed the previous system in its accuracy too. User interface of IDDI system provides interactive investigation of proteins and domains in interactions with interconnected way. A specific example is presented to show the efficiency of the systems to acquire the comprehensive information of target protein with PPI and DDI relationships. IDDI is freely available at http://pcode.kaist.ac.kr/iddi/.

     

Functional and topological characterization of protein interaction networks

The elucidation of the cell's large-scale organization is a primary challenge for post-genomic biology, and understanding the structure of protein interaction networks offers an important starting point for such studies. We compare four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large-scale properties and the impact of the proteins' function and cellular localization on the network topology. We show how each database supports a scale-free, topology with hierarchical modularity, indicating that these features represent a robust and generic property of the protein interactions network. We also find strong correlations between the network's structure and the functional role and subcellular localization of its protein constituents, concluding that most functional and/or localization classes appear as relatively segregated subnetworks of the full protein interaction network. The uncovered systematic differences between the four protein interaction databases reflect their relative coverage for different functional and localization classes and provide a guide for their utility in various bioinformatics studies.     

Functional annotation from predicted protein interaction networks

MOTIVATION: Progress in large-scale experimental determination of protein-protein interaction networks for several organisms has resulted in innovative methods of functional inference based on network connectivity. However, the amount of effort and resources required for the elucidation of experimental protein interaction networks is prohibitive. Previously we, and others, have developed techniques to predict protein interactions for novel genomes using computational methods and data generated from other genomes. RESULTS: We evaluated the performance of a network-based functional annotation method that makes use of our predicted protein interaction networks. We show that this approach performs equally well on experimentally derived and predicted interaction networks, for both manually and computationally assigned annotations. We applied the method to predicted protein interaction networks for over 50 organisms from all domains of life, providing annotations for many previously unannotated proteins and verifying existing low-confidence annotations. AVAILABILITY: Functional predictions for over 50 organisms are available at http://bioverse.compbio.washington.edu and datasets used for analysis at http://data.compbio.washington.edu/misc/downloads/nannotation_data/. SUPPLEMENTARY INFORMATION: A supplemental appendix gives additional details not in the main text. (http://data.compbio.washington.edu/misc/downloads/nannotation_data/supplement.pdf).     

A new and unexpected domain-domain interaction in the AraC protein

An interaction between the dimerization domains and DNA binding domains of the dimeric AraC protein has previously been shown to facilitate repression of the Escherichia coli araBAD operon by AraC in the absence of arabinose. A new interaction between the domains of AraC in the presence of arabinose is reported here, the regulatory consequences of which are unknown. Evidence for the interaction is the following: the dissociation rate of arabinose-bound AraC from half-site DNA is considerably faster than that of free DNA binding domain, and the affinity of the dimerization domains for arabinose is increased when half-site DNA is bound. In addition, an increase in the fluorescence intensity of tryptophan residues located in the arabinose-bound dimerization domain is observed upon binding of half-site DNA to the DNA binding domains. Direct physical evidence of the new domain-domain interaction is demonstrated by chemical crosslinking and NMR experiments.     

Functional modules by relating protein interaction networks and gene expression

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.     

Evolutionary conservation of domain-domain interactions

Background  

Recently, there has been much interest in relating domain-domain interactions (DDIs) to protein-protein interactions (PPIs) and vice versa, in an attempt to understand the molecular basis of PPIs.     

Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

Evolutionary conservation of domain-domain interactions

Background

Recently, there has been much interest in relating domain-domain interactions (DDIs) to protein-protein interactions (PPIs) and vice versa, in an attempt to understand the molecular basis of PPIs.

Results

Here we map structurally derived DDIs onto the cellular PPI networks of different organisms and demonstrate that there is a catalog of domain pairs that is used to mediate various interactions in the cell. We show that these DDIs occur frequently in protein complexes and that homotypic interactions (of a domain with itself) are abundant. A comparison of the repertoires of DDIs in the networks of Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens shows that many DDIs are evolutionarily conserved.

Conclusion

Our results indicate that different organisms use the same 'building blocks' for PPIs, suggesting that the functionality of many domain pairs in mediating protein interactions is maintained in evolution.     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.     

Unequal evolutionary conservation of human protein interactions in interologous networks

Background  

Protein-protein interaction (PPI) networks have been transferred between organisms using interologs, allowing model organisms to supplement the interactomes of higher eukaryotes. However, the conservation of various network components has not been fully explored. Unequal conservation of certain network components may limit the ability to fully expand the target interactomes using interologs.     

Functional coherence in domain interaction networks

MOTIVATION: Extracting functional information from protein-protein interactions (PPI) poses significant challenges arising from the noisy, incomplete, generic and static nature of data obtained from high-throughput screening. Typical proteins are composed of multiple domains, often regarded as their primary functional and structural units. Motivated by these considerations, domain-domain interactions (DDI) for network-based analyses have received significant recent attention. This article performs a formal comparative investigation of the relationship between functional coherence and topological proximity in PPI and DDI networks. Our investigation provides the necessary basis for continued and focused investigation of DDIs as abstractions for functional characterization and modularization of networks. RESULTS: We investigate the problem of assessing the functional coherence of two biomolecules (or segments thereof) in a formal framework. We establish essential attributes of admissible measures of functional coherence, and demonstrate that existing, well-accepted measures are ill-suited to comparative analyses involving different entities (i.e. domains versus proteins). We propose a statistically motivated functional similarity measure that takes into account functional specificity as well as the distribution of functional attributes across entity groups to assess functional similarity in a statistically meaningful and biologically interpretable manner. Results on diverse data, including high-throughput and computationally predicted PPIs, as well as structural and computationally inferred DDIs for different organisms show that: (i) the relationship between functional similarity and network proximity is captured in a much more (biologically) intuitive manner by our measure, compared to existing measures and (ii) network proximity and functional similarity are significantly more correlated in DDI networks than in PPI networks, and that structurally determined DDIs provide better functional relevance as compared to computationally inferred DDIs.     

PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing.We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided.Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.     

Functional interaction between the pp71 protein of human cytomegalovirus and the PML-interacting protein human Daxx

The tegument protein pp71 (UL82) of human cytomegalovirus (HCMV) has previously been shown to transactivate the major immediate-early enhancer-promoter of HCMV. Furthermore, this protein is able to enhance the infectivity of viral DNA and to accelerate the infection cycle, suggesting an important regulatory function during viral replication. To gain insight into the underlying mechanisms that are used by pp71 to exert these pleiotropic effects, we sought for cellular factors interacting with pp71 in a yeast two-hybrid screen. Here, we report the isolation of the human Daxx (hDaxx) protein as a specific interaction partner of HCMV pp71. hDaxx, which was initially described as an adapter protein involved in apoptosis regulation, has recently been identified as a nuclear protein that interacts and colocalizes with PML in the nuclear domain ND10. In order to assess whether pp71 can also be detected in ND10 structures, a vector expressing pp71 in fusion with the green fluorescent protein was used for transfection of human fibroblasts. This revealed a colocalization of pp71 with the ND10 proteins PML and Sp100. In addition, cotransfection of a hDaxx expression vector resulted in an enhanced recruitment of pp71 to ND10. Targeting of pp71 to nuclear dots could also be observed in infected human fibroblasts in the absence of de novo viral protein synthesis. Moreover, cotransfection experiments revealed that pp71-mediated transactivation of the major immediate-early enhancer-promoter was synergistically enhanced in the presence of hDaxx. These results suggest an important role of hDaxx for pp71 protein function.     

Domain structure and domain-domain interactions of recombinant tissue plasminogen activator

The melting of recombinant tissue plasminogen activator (rtPA) has been investigated by differential scanning calorimetry and fluorescence spectroscopy. At neutral pH, rtPA melts with only partial reversibility in a single sharp peak that can be deconvoluted into four transitions. By contrast, at acidic pH the melting process is spread over a broad range of temperature and is highly reversible. Under these conditions five transitions are resolved by deconvolution analysis. Additional measurements in 6 M guanidinium chloride reveal a sixth transition representing an extremely stable domain. Comparison of the melting curves of several fragments with those of the parent protein allowed all of the transitions to be assigned. The results indicate that rtPA is comprised of six independently folded domains. Two of these domains correspond to the two kringle modules whose thermodynamic properties are similar to those of the kringles in plasminogen. Two additional domains are formed by the epidermal growth factor (EGF)-like and finger modules, the latter of which is extremely stable, requiring the presence of a chemical denaturant for its melting to be observed. The serine protease module contains two more domains which at neutral pH melt cooperatively in a single transition but at low pH melt independently, accounting for the greater number of transitions observed there. Measurements with a 50-kDa fragment lacking the C-terminal half of the serine protease module and with a variant lacking the finger and EGF domains indicate that the serine protease domains interact strongly with and are stabilized by the finger and/or EGF domains in the intact protein. This interaction between domains located at opposite ends of the rtPA molecule produces a more compact structure. A better understanding of such interactions may enhance efforts to engineer plasminogen activators with improved thrombolytic properties.     

Energetics of domain-domain interactions and entropy driven association of beta-crystallins

Beta-crystallins are major protein constituents of the mammalian lens, where their stability and association into higher order complexes are critical for lens clarity and refraction. They undergo modification as the lens ages, including cleavage of their terminal extensions. The energetics of betaA3- and betaB2-crystallin association was studied using site-directed mutagenesis and analytical ultracentrifugation. Recombinant (r) murine wild type betaA3- and betaB2-crystallins were modified by removal of either the N-terminal extension of betaA3 (rbetaA3Ntr) or betaB2 (rbetaB2Ntr), or both the N- and C-terminal extensions of betaB2 (rbetaB2NCtr). The proteins were expressed in Sf9 insect cells or Escherichia coli and purified by gel-filtration and ion-exchange chromatography. All beta-crystallins studied demonstrated fast reversible monomer-dimer equilibria over the temperature range studied (5-35 degrees C) with a tendency to form tighter dimers at higher temperatures. The N-terminal deletion of rbetaA3 (rbetaA3Ntr) significantly increases the enthalpy (+10.9 kcal/mol) and entropy (+40.7 cal/deg mol) of binding relative to unmodified protein. Removal of both N- and C-terminal extensions of rbetaB2 also increases these parameters but to a lesser degree. Deletion of the betaB2-crystallin N-terminal extension alone (rbetaB2Ntr) gave almost no change relative to rbetaB2. The resultant net negative changes in the binding energy suggest that betaAlpha3- and betaB2-crystallin association is entropically driven. The thermodynamic consequences of the loss of betaAlpha3-crystallin terminal extensions by in vivo proteolytic processing could increase their tendency to associate and so promote the formation of higher order associates in the aging and cataractous lens.     

Domain-domain interactions underlying herpesvirus-human protein-protein interaction networks

Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host.     

An integrated approach to the prediction of domain-domain interactions

Background  

The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.     

The Cartographers toolbox: building bigger and better human protein interaction networks

One of the greatest challenges of the post-genomic era is theconstruction of a more comprehensive human protein interactionmap. While this process may take many years to complete, thedevelopment of stringent high throughput techniques and theemergence of complementary assays mean that the aim of buildinga detailed binary map of the human interactome is now a veryrealistic goal. In particular, methods which facilitate theanalysis of large numbers of membrane-protein interactions meanthat it will be possible to construct more extensive networks,which in turn provide new insights into the functional connectivitybetween intra- and extra-cellular processes. This is importantas many therapeutic strategies are designed to elicit effectsvia ‘tractable’ cell-surface proteins. Therefore,the construction of maps depicting the complexity of trans-cellularcommunication networks will not only improve our understandingof physiological processes, it will also aid the design of rationaltherapeutic strategies, with fewer potential side effects. Thisreview aims to provide a basic insight into the approaches currentlybeing used to construct binary human protein interaction networks,with particular reference to newer techniques, which have thepotential to extend network coverage and aid the conditionalannotation of interactome-scale protein interaction maps.