Prediction of protein-protein interactions between human host and a pathogen and its application to three pathogenic bacteria

Molecular understanding of disease processes can be accelerated if all interactions between the host and pathogen are known. The unavailability of experimental methods for large-scale detection of interactions across host and pathogen organisms hinders this process. Here we apply a simple method to predict protein-protein interactions across a host and pathogen organisms. We use homology detection approaches against the protein-protein interaction databases, DIP and iPfam in order to predict interacting proteins in a host-pathogen pair. In the present work, we first applied this approach to the test cases involving the pairs phage T4 -Escherichia coli and phage lambda -E. coli and show that previously known interactions could be recognized using our approach. We further apply this approach to predict interactions between human and three pathogens E. coli, Salmonella enterica typhimurium and Yersinia pestis. We identified several novel interactions involving proteins of host or pathogen that could be thought of as highly relevant to the disease process. Serendipitously, many interactions involve hypothetical proteins of yet unknown function. Hypothetical proteins are predicted from computational analysis of genome sequences with no laboratory analysis on their functions yet available. The predicted interactions involving such proteins could provide hints to their functions.     

Host pathogen protein interactions predicted by comparative modeling

Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host-pathogen interactions involve protein-protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host-pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for "neglected" human diseases. The method was assessed by (1) comparison to a set of known host-pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host-pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host-pathogen protein interactions.     

The landscape of human proteins interacting with viruses and other pathogens

Infectious diseases result in millions of deaths each year. Mechanisms of infection have been studied in detail for many pathogens. However, many questions are relatively unexplored. What are the properties of human proteins that interact with pathogens? Do pathogens interact with certain functional classes of human proteins? Which infection mechanisms and pathways are commonly triggered by multiple pathogens? In this paper, to our knowledge, we provide the first study of the landscape of human proteins interacting with pathogens. We integrate human–pathogen protein–protein interactions (PPIs) for 190 pathogen strains from seven public databases. Nearly all of the 10,477 human-pathogen PPIs are for viral systems (98.3%), with the majority belonging to the human–HIV system (77.9%). We find that both viral and bacterial pathogens tend to interact with hubs (proteins with many interacting partners) and bottlenecks (proteins that are central to many paths in the network) in the human PPI network. We construct separate sets of human proteins interacting with bacterial pathogens, viral pathogens, and those interacting with multiple bacteria and with multiple viruses. Gene Ontology functions enriched in these sets reveal a number of processes, such as cell cycle regulation, nuclear transport, and immune response that participate in interactions with different pathogens. Our results provide the first global view of strategies used by pathogens to subvert human cellular processes and infect human cells. Supplementary data accompanying this paper is available at http://staff.vbi.vt.edu/dyermd/publications/dyer2008a.html.     

Bacteria use structural imperfect mimicry to hijack the host interactome

Bacteria use protein-protein interactions to infect their hosts and hijack fundamental pathways, which ensures their survival and proliferation. Hence, the infectious capacity of the pathogen is closely related to its ability to interact with host proteins. Here, we show that hubs in the host-pathogen interactome are isolated in the pathogen network by adapting the geometry of the interacting interfaces. An imperfect mimicry of the eukaryotic interfaces allows pathogen proteins to actively bind to the host’s target while preventing deleterious effects on the pathogen interactome. Understanding how bacteria recognize eukaryotic proteins may pave the way for the rational design of new antibiotic molecules.     

The Human-Bacterial Pathogen Protein Interaction Networks of Bacillus anthracis,Francisella tularensis,and Yersinia pestis

Background

Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion.

Methodology

In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity.

Significance

These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions.     

A computational model for predicting protein interactions based on multidomain collaboration

Recently, several domain-based computational models for predicting protein-protein interactions (PPIs) have been proposed. The conventional methods usually infer domain or domain combination (DC) interactions from already known interacting sets of proteins, and then predict PPIs using the information. However, the majority of these models often have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair (multidomain interaction) will actually interact for the predicted protein interaction. Therefore, a more comprehensive and concrete computational model for the prediction of PPIs is needed. We developed a computational model to predict PPIs using the information of intraprotein domain cohesion and interprotein DC coupling interaction. A method of identifying the primary interacting DC pair was also incorporated into the model in order to infer actual participants in a predicted interaction. Our method made an apparent improvement in the PPI prediction accuracy, and the primary interacting DC pair identification was valid specifically in predicting multidomain protein interactions. In this paper, we demonstrate that 1) the intraprotein domain cohesion is meaningful in improving the accuracy of domain-based PPI prediction, 2) a prediction model incorporating the intradomain cohesion enables us to identify the primary interacting DC pair, and 3) a hybrid approach using the intra/interdomain interaction information can lead to a more accurate prediction.     

Genome-wide identification of molecular mimicry candidates in parasites

Among the many strategies employed by parasites for immune evasion and host manipulation, one of the most fascinating is molecular mimicry. With genome sequences available for host and parasite, mimicry of linear amino acid epitopes can be investigated by comparative genomics. Here we developed an in silico pipeline for genome-wide identification of molecular mimicry candidate proteins or epitopes. The predicted proteome of a given parasite was broken down into overlapping fragments, each of which was screened for close hits in the human proteome. Control searches were carried out against unrelated, free-living eukaryotes to eliminate the generally conserved proteins, and with randomized versions of the parasite proteins to get an estimate of statistical significance. This simple but computation-intensive approach yielded interesting candidates from human-pathogenic parasites. From Plasmodium falciparum, it returned a 14 amino acid motif in several of the PfEMP1 variants identical to part of the heparin-binding domain in the immunosuppressive serum protein vitronectin. And in Brugia malayi, fragments were detected that matched to periphilin-1, a protein of cell-cell junctions involved in barrier formation. All the results are publicly available by means of mimicDB, a searchable online database for molecular mimicry candidates from pathogens. To our knowledge, this is the first genome-wide survey for molecular mimicry proteins in parasites. The strategy can be adopted to any pair of host and pathogen, once appropriate negative control organisms are chosen. MimicDB provides a host of new starting points to gain insights into the molecular nature of host-pathogen interactions.     

Uncovering New Pathogen–Host Protein–Protein Interactions by Pairwise Structure Similarity

Pathogens usually evade and manipulate host-immune pathways through pathogen–host protein–protein interactions (PPIs) to avoid being killed by the host immune system. Therefore, uncovering pathogen–host PPIs is critical for determining the mechanisms underlying pathogen infection and survival. In this study, we developed a computational method, which we named pairwise structure similarity (PSS)-PPI, to predict pathogen–host PPIs. First, a high-quality and non-redundant structure–structure interaction (SSI) template library was constructed by exhaustively exploring heteromeric protein complex structures in the PDB database. New interactions were then predicted by searching for PSS with complex structures in the SSI template library. A quantitative score named the PSS score, which integrated structure similarity and residue–residue contact-coverage information, was used to describe the overall similarity of each predicted interaction with the corresponding SSI template. Notably, PSS-PPI yielded experimentally confirmed pathogen–host PPIs of human immunodeficiency virus type 1 (HIV-1) with performance close to that of in vitro high-throughput screening approaches. Finally, a pathogen–host PPI network of human pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis, was constructed using PSS-PPI and refined using filtration steps based on cellular localization information. Analysis of the resulting network indicated that secreted proteins of the STPK, ESX-1, and PE/PPE family in M. tuberculosis targeted human proteins involved in immune response and phagocytosis. M. tuberculosis also targeted host factors known to regulate HIV replication. Taken together, our findings provide insights into the survival mechanisms of M. tuberculosis in human hosts, as well as co-infection of tuberculosis and HIV. With the rapid pace of three-dimensional protein structure discovery, the SSI template library we constructed and the PSS-PPI method we devised can be used to uncover new pathogen–host PPIs in the future.     

Shared molecular strategies of the malaria parasite P. falciparum and the human virus HIV-1

We augmented existing computationally predicted and experimentally determined interactions with evolutionarily conserved interactions between proteins of the malaria parasite, P. falciparum, and the human host. In a validation step, we found that conserved interacting host-parasite protein pairs were specifically expressed in host tissues where both the parasite and host proteins are known to be active. We compared host-parasite interactions with experimentally verified interactions between human host proteins and a very different pathogen, HIV-1. Both pathogens were found to use their protein repertoire in a combinatorial manner, providing a broad connection to host cellular processes. Specifically, the two biologically distinct pathogens predominately target central proteins to take control of a human host cell, effectively reaching into diversified cellular host cellular functions. Interacting signaling pathways and a small set of regulatory and signaling proteins were prime targets of both pathogens, suggesting remarkably similar patterns of host-pathogen interactions despite the vast biological differences of both pathogens. Such an identification of shared molecular strategies by the virus HIV-1 and the eukaryotic intracellular pathogen P. falciparum may allow us to illuminate new avenues of disease intervention.     

Computational analysis of interactomes: Current and future perspectives for bioinformatics approaches to model the host-pathogen interaction space

Bacterial and viral pathogens affect their eukaryotic host partly by interacting with proteins of the host cell. Hence, to investigate infection from a systems' perspective we need to construct complete and accurate host-pathogen protein-protein interaction networks. Because of the paucity of available data and the cost associated with experimental approaches, any construction and analysis of such a network in the near future has to rely on computational predictions. Specifically, this challenge consists of a number of sub-problems: First, prediction of possible pathogen interactors (e.g. effector proteins) is necessary for bacteria and protozoa. Second, the prospective host binding partners have to be determined and finally, the impact on the host cell analyzed. This review gives an overview of current bioinformatics approaches to obtain and understand host-pathogen interactions. As an application example of the methods covered, we predict host-pathogen interactions of Salmonella and discuss the value of these predictions as a prospective for further research.     

RXLR-mediated entry of Phytophthora sojae effector Avr1b into soybean cells does not require pathogen-encoded machinery

Effector proteins secreted by oomycete and fungal pathogens have been inferred to enter host cells, where they interact with host resistance gene products. Using the effector protein Avr1b of Phytophthora sojae, an oomycete pathogen of soybean (Glycine max), we show that a pair of sequence motifs, RXLR and dEER, plus surrounding sequences, are both necessary and sufficient to deliver the protein into plant cells. Particle bombardment experiments demonstrate that these motifs function in the absence of the pathogen, indicating that no additional pathogen-encoded machinery is required for effector protein entry into host cells. Furthermore, fusion of the Avr1b RXLR-dEER domain to green fluorescent protein (GFP) allows GFP to enter soybean root cells autonomously. The conclusion that RXLR and dEER serve to transduce oomycete effectors into host cells indicates that the >370 RXLR-dEER-containing proteins encoded in the genome sequence of P. sojae are candidate effectors. We further show that the RXLR and dEER motifs can be replaced by the closely related erythrocyte targeting signals found in effector proteins of Plasmodium, the protozoan that causes malaria in humans. Mutational analysis of the RXLR motif shows that the required residues are very similar in the motifs of Plasmodium and Phytophthora. Thus, the machinery of the hosts (soybean and human) targeted by the effectors may be very ancient.     

Including probe-level uncertainty in model-based gene expression clustering

Background

Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.

Results

DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.

Conclusion

We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.     

Predictive and comparative analysis of Ebolavirus proteins

Ebolavirus is the pathogen for Ebola Hemorrhagic Fever (EHF). This disease exhibits a high fatality rate and has recently reached a historically epidemic proportion in West Africa. Out of the 5 known Ebolavirus species, only Reston ebolavirus has lost human pathogenicity, while retaining the ability to cause EHF in long-tailed macaque. Significant efforts have been spent to determine the three-dimensional (3D) structures of Ebolavirus proteins, to study their interaction with host proteins, and to identify the functional motifs in these viral proteins. Here, in light of these experimental results, we apply computational analysis to predict the 3D structures and functional sites for Ebolavirus protein domains with unknown structure, including a zinc-finger domain of VP30, the RNA-dependent RNA polymerase catalytic domain and a methyltransferase domain of protein L. In addition, we compare sequences of proteins that interact with Ebolavirus proteins from RESTV-resistant primates with those from RESTV-susceptible monkeys. The host proteins that interact with GP and VP35 show an elevated level of sequence divergence between the RESTV-resistant and RESTV-susceptible species, suggesting that they may be responsible for host specificity. Meanwhile, we detect variable positions in protein sequences that are likely associated with the loss of human pathogenicity in RESTV, map them onto the 3D structures and compare their positions to known functional sites. VP35 and VP30 are significantly enriched in these potential pathogenicity determinants and the clustering of such positions on the surfaces of VP35 and GP suggests possible uncharacterized interaction sites with host proteins that contribute to the virulence of Ebolavirus.     

Purification and characterization of HIV–human protein complexes

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

Conservation of a gliding motility and cell invasion machinery in Apicomplexan parasites

Most Apicomplexan parasites, including the human pathogens Plasmodium, Toxoplasma, and Cryptosporidium, actively invade host cells and display gliding motility, both actions powered by parasite microfilaments. In Plasmodium sporozoites, thrombospondin-related anonymous protein (TRAP), a member of a group of Apicomplexan transmembrane proteins that have common adhesion domains, is necessary for gliding motility and infection of the vertebrate host. Here, we provide genetic evidence that TRAP is directly involved in a capping process that drives both sporozoite gliding and cell invasion. We also demonstrate that TRAP-related proteins in other Apicomplexa fulfill the same function and that their cytoplasmic tails interact with homologous partners in the respective parasite. Therefore, a mechanism of surface redistribution of TRAP-related proteins driving gliding locomotion and cell invasion is conserved among Apicomplexan parasites.     

Characterization of Burkholderia pseudomallei infection and identification of novel virulence factors using a Caenorhabditis elegans host system

The environmental saphrophyte Burkholderia pseudomallei is the causative agent of melioidosis, a systemic, potentially life-threatening condition endemic to many parts of south-east Asia and northern Australia. We have used the soil nematode Caenorhabditis elegans as a model host to characterize the mechanisms by which this bacterium mounts a successful infection. We find that C. elegans is susceptible to a broad range of Burkholderia species, and that the virulence mechanisms used by this pathogen to kill nematodes may be similar to those used to infect mammals. We also find that the specific dynamics of the C. elegans-B. pseudomallei host-pathogen interaction can be highly influenced by environmental factors, and that nematode killing results at least in part from the presence of a diffusible toxin. Finally, by screening for bacterial mutants attenuated in their ability to kill C. elegans, we genetically identify several new potential virulence factors in B. pseudomallei. The use of C. elegans as a model host should greatly facilitate future investigations into how B. pseudomallei can interact with host organisms.     

DomPep--a general method for predicting modular domain-mediated protein-protein interactions

Protein-protein interactions (PPIs) are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains.