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.     

Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.     

Screening for peptide drugs from the natural repertoire of biodiverse protein folds

Although monoclonal antibody (mAb) drugs targeting protein interactions exist, these therapeutics cannot access intracellular proteins involved in disease complexes. Moreover, mAbs are more difficult to deliver and are frequently associated with a prohibitive 'royalty stack.' Outlined here is an alternative approach based on libraries of natural, highly structured peptides that offers new opportunities for identifying effective, specific inhibitors of protein-protein interactions. Libraries of such peptides (referred to hereafter as phylomers) comprise both random and structured peptides encoded by natural genes of diverse bacterial genomes. Because the number of protein subdomain structures found in nature is limited, diverse libraries containing millions of phylomers constitute virtually all of the available classes of protein fold structures, providing a rich source of peptides that interact specifically and with high affinity to human proteins. This approach may help not only in understanding the implications of each interaction identified within the interactome but also in the development of effective drugs targeted to particular protein functions. Although phylomers are active in animal models, the challenge remains to demonstrate efficacy and safety in a clinical setting.     

HINT: High-quality protein interactomes and their applications in understanding human disease

ABSTRACT: BACKGROUND: A global map of protein-protein interactions in cellular systems provides key insights into the working of an organism. A repository of well-validated high-quality protein-protein interactions can be used in both large- and small-scale studies to generate and validate a wide range of functional hypotheses. RESULTS: We develop HINT (http://hint.yulab.org) - a database of high-quality protein-protein interactions for human, Saccharomyces cerevisiae and Schizosaccharomyces pombe. These were collected from several databases and filtered both systematically and manually to remove low-quality/erroneous interactions. The resulting datasets are classified by type (binary physical interactions vs. co-complex associations) and data source (high-throughput systematic setups vs. literature-curated small-scale experiments). We find strong sociological sampling biases in literature-curated datasets of small-scale interactions. An interactome without such sampling biases was used to understand network properties of human disease-genes - hubs are unlikely to cause disease, but if they do, they usually cause multiple disorders. CONCLUSIONS: HINT is of significant interest to researchers in all fields of biology as it addresses the ubiquitous need of having a repository of high-quality protein-protein interactions. These datasets can be utilized to generate specific hypotheses about specific proteins and/or pathways, as well as analyzing global properties of cellular networks. HINT will be regularly updated and all versions will be tracked.     

Improving understanding of chromatin regulatory proteins and potential implications for drug discovery

Many epigenetic-based therapeutics, including drugs such as histone deacetylase inhibitors, are now used in the clinic or are undergoing advanced clinical trials. The study of chromatin-modifying proteins has benefited from the rapid advances in high-throughput sequencing methods, the organized efforts of major consortiums and by individual groups to profile human epigenomes in diverse tissues and cell types. However, while such initiatives have carefully characterized healthy human tissue, disease epigenomes and drug–epigenome interactions remain very poorly understood. Reviewed here is how high-throughput sequencing improves our understanding of chromatin regulator proteins and the potential implications for the study of human disease and drug development and discovery.     

Protein microarrays, biosensors, and cell-based methods for secretome-wide extracellular protein-protein interaction mapping

Approximately one quarter of all human genes encode proteins that function in the extracellular space or serve to bridge the extracellular and intracellular environments. Physical associations between these secretome proteins serve to regulate a wide range of biological activities and consequently represent important therapeutic targets. Moreover, some extracellular proteins are targeted by pathogens to allow host access or immune evasion. Despite the importance of extracellular protein-protein interactions, our knowledge in this area has remained sparse. Weak affinities and low abundance have often hindered efforts to identify these interactions using traditional methods such as biochemical purification and cDNA library expression cloning. Moreover, current large-scale protein-protein interaction mapping techniques largely under represent extracellular protein-protein interactions. This review highlights emerging biosensor and protein microarray technology, along with more traditional cell-based techniques, that are compatible with secretome-wide screens for extracellular protein-protein interaction discovery. A combination of these approaches will serve to rapidly expand our knowledge of the extracellular protein-protein interactome.     

Identification of VCP/p97, carboxyl terminus of Hsp70-interacting protein (CHIP), and amphiphysin II interaction partners using membrane-based human proteome arrays

Proteins mediate their biological function through interactions with other proteins. Therefore, the systematic identification and characterization of protein-protein interactions have become a powerful proteomic strategy to understand protein function and comprehensive cellular regulatory networks. For the screening of valosin-containing protein, carboxyl terminus of Hsp70-interacting protein (CHIP), and amphiphysin II interaction partners, we utilized a membrane-based array technology that allows the identification of human protein-protein interactions with crude bacterial cell extracts. Many novel interaction pairs such as valosin-containing protein/autocrine motility factor receptor, CHIP/caytaxin, or amphiphysin II/DLP4 were identified and subsequently confirmed by pull-down, two-hybrid and co-immunoprecipitation experiments. In addition, assays were performed to validate the interactions functionally. CHIP e.g. was found to efficiently polyubiquitinate caytaxin in vitro, suggesting that it might influence caytaxin degradation in vivo. Using peptide arrays, we also identified the binding motifs in the proteins DLP4, XRCC4, and fructose-1,6-bisphosphatase, which are crucial for the association with the Src homology 3 domain of amphiphysin II. Together these studies indicate that our human proteome array technology permits the identification of protein-protein interactions that are functionally involved in neurodegenerative disease processes, the degradation of protein substrates, and the transport of membrane vesicles.     

Systematic identification of SH3 domain-mediated human protein-protein interactions by peptide array target screening

Systematic identification of direct protein-protein interactions is often hampered by difficulties in expressing and purifying the corresponding full-length proteins. By taking advantage of the modular nature of many regulatory proteins, we attempted to simplify protein-protein interactions to the corresponding domain-ligand recognition and employed peptide arrays to identify such binding events. A group of 12 Src homology (SH) 3 domains from eight human proteins (Swiss-Prot ID: SRC, PLCG1, P85A, NCK1, GRB2, FYN, CRK) were used to screen a peptide target array composed of 1536 potential ligands, which led to the identification of 921 binary interactions between these proteins and 284 targets. To assess the efficiency of the peptide array target screening (PATS) method in identifying authentic protein-protein interactions, we examined a set of interactions mediated by the PLCgamma1 SH3 domain by coimmunoprecipitation and/or affinity pull-downs using full-length proteins and achieved a 75% success rate. Furthermore, we characterized a novel interaction between PLCgamma1 and hematopoietic progenitor kinase 1 (HPK1) identified by PATS and demonstrated that the PLCgamma1 SH3 domain negatively regulated HPK1 kinase activity. Compared to protein interactions listed in the online predicted human interaction protein database (OPHID), the majority of interactions identified by PATS are novel, suggesting that, when extended to the large number of peptide interaction domains encoded by the human genome, PATS should aid in the mapping of the human interactome.     

Protein complementation as tool for studying protein-protein interactions in living cells

The association and dissociation of protein-protein complexes play an important role in various processes in living cells. The disruption of protein-protein interactions is observed in various pathologies. The study of the nature of these interactions will contribute to a better understanding of the molecular basis of the pathogenesis of the disease and the development of new approaches to therapy. Now there is a set of methods that allow one to reveal and analyze the interaction of proteins in vitro. However, more accurate data can be obtained by studying protein-protein interactions in vivo. One of a few prospective methods is based on the effect of the complementation of fragments of reporter proteins. These reporter systems are based on the change in the fluorescent properties or enzymatic activity of the proteins that can be measured using colorimetric, fluorescent, or other substrates. The principle of the complementation is widely used to analyze protein interactions, to determine of order of interaction of protein partners in different signaling pathways, as well as in high-performance screening studies for detecting and mapping previously unknown protein-protein interactions. The possibilities of existing complementation reporter systems allow one to solve problems that are far beyond the simple registration of the interactions of two or more proteins.     

Investigations on the interactions of scorpion neurotoxins with the predicted structure of D1 dopamine receptor by protein-protein docking method. A bioinformatics approach

Dopamine receptors play a critical role in the cell signalling process responsible for information transfer in neurons functioning in the nervous system. Development of improved therapeutics for disorders like Parkinson's disease and schizophrenia would be significantly enhanced with the availability of the 3D structure for the dopamine receptors. Scorpion neurotoxins are unique source of structural templates from which new therapeutic agents might be developed. We report here the 3D structure of the human D1 dopamine receptor, predicted from primary sequence using computational techniques. The predicted structure of the human D1 dopamine receptor is used to understand the mechanism of interactions between scorpion neurotoxins through the protein-protein docking method. CHARMM force field was used for the energy minimization step before applying the docking method.     

Ankyrin repeats in context with human population variation

Ankyrin protein repeats bind to a wide range of substrates and are one of the most common protein motifs in nature. Here, we collate a high-quality alignment of 7,407 ankyrin repeats and examine for the first time, the distribution of human population variants from large-scale sequencing of healthy individuals across this family. Population variants are not randomly distributed across the genome but are constrained by gene essentiality and function. Accordingly, we interpret the population variants in context with evolutionary constraint and structural features including secondary structure, accessibility and protein-protein interactions across 383 three-dimensional structures of ankyrin repeats. We find five positions that are highly conserved across homologues and also depleted in missense variants within the human population. These positions are significantly enriched in intra-domain contacts and so likely to be key for repeat packing. In contrast, a group of evolutionarily divergent positions are found to be depleted in missense variants in human and significantly enriched in protein-protein interactions. Our analysis also suggests the domain has three, not two surfaces, each with different patterns of enrichment in protein-substrate interactions and missense variants. Our findings will be of interest to those studying or engineering ankyrin-repeat containing proteins as well as those interpreting the significance of disease variants.     

Gene Fusion Analysis in the Battle against the African Endemic Sleeping Sickness

The protozoan Trypanosoma brucei causes African Trypanosomiasis or sleeping sickness in humans, which can be lethal if untreated. Most available pharmacological treatments for the disease have severe side-effects. The purpose of this analysis was to detect novel protein-protein interactions (PPIs), vital for the parasite, which could lead to the development of drugs against this disease to block the specific interactions. In this work, the Domain Fusion Analysis (Rosetta Stone method) was used to identify novel PPIs, by comparing T. brucei to 19 organisms covering all major lineages of the tree of life. Overall, 49 possible protein-protein interactions were detected, and classified based on (a) statistical significance (BLAST e-value, domain length etc.), (b) their involvement in crucial metabolic pathways, and (c) their evolutionary history, particularly focusing on whether a protein pair is split in T. brucei and fused in the human host. We also evaluated fusion events including hypothetical proteins, and suggest a possible molecular function or involvement in a certain biological process. This work has produced valuable results which could be further studied through structural biology or other experimental approaches so as to validate the protein-protein interactions proposed here. The evolutionary analysis of the proteins involved showed that, gene fusion or gene fission events can happen in all organisms, while some protein domains are more prone to fusion and fission events and present complex evolutionary patterns.     

Proteomics in clinical trials and practice: present uses and future promise

The study of clinical proteomics is a promising new field that has the potential to have many applications, including the identification of biomarkers and monitoring of disease, especially in the field of oncology. Expression proteomics evaluates the cellular production of proteins encoded by a particular gene and exploits the differential expression and post-translational modifications of proteins between healthy and diseased states. These biomarkers may be applied towards early diagnosis, prognosis, and prediction of response to therapy. Functional proteomics seeks to decipher protein-protein interactions and biochemical pathways involved in disease biology and targeted by newer molecular therapeutics. Advanced spectrometry technologies and new protein array formats have improved these analyses and are now being applied prospectively in clinical trials. Further advancement of proteomics technology could usher in an era of personalized molecular medicine, where diseases are diagnosed at earlier stages and where therapies are more effective because they are tailored to the protein expression of a patient's malignancy.     

Proteome-wide prediction of overlapping small molecule and protein binding sites using structure

Small molecules that modulate protein-protein interactions are of great interest for chemical biology and therapeutics. Here I present a structure-based approach to predict 'bi-functional' sites able to bind both small molecule ligands and proteins, in proteins of unknown structure. First, I develop a homology-based annotation method that transfers binding sites of known three-dimensional structure onto protein sequences, predicting residues in ligand and protein binding sites with estimated true positive rates of 98% and 88%, respectively, at 1% false positive rates. Applying this method to the human proteome predicts 8463 proteins with bi-functional residues and correctly recovers the targets of known interaction modulators. Proteins with significantly (p < 0.01) more bi-functional residues than expected were found to be enriched in regulatory and depleted in metabolism functions. Finally, I demonstrate the utility of the method by describing examples of predicted overlap and evidence of their biological and therapeutic relevance. The results suggest that combining the structures of known binding sites with established fold detection algorithms can predict regions of protein-protein interfaces that are amenable to small molecule modulation. Open-source software and the results for several complete proteomes are available at http://pibase.janelia.org/homolobind.     

Computational prediction of host-pathogen protein-protein interactions

MOTIVATION: Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. RESULTS: We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. AVAILABILITY: Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Reverse MAPPIT: screening for protein-protein interaction modifiers in mammalian cells

Interactions between proteins are at the heart of the cellular machinery. It is therefore not surprising that altered interaction profiles caused by aberrant protein expression patterns or by the presence of mutations can trigger cellular dysfunction, eventually leading to disease. Moreover, many viral and bacterial pathogens rely on protein-protein interactions to exert their damaging effects. Interfering with such interactions is an obvious pharmaceutical goal, but detailed insights into the protein binding properties as well as efficient screening platforms are needed. In this report, we describe a cytokine receptor-based assay with a positive readout to screen for disrupters of designated protein-protein interactions in intact mammalian cells and evaluate this concept using polypeptides as well as small organic molecules. These reverse mammalian protein-protein interaction trap (MAPPIT) screens were developed to monitor interactions between the erythropoietin receptor (EpoR) and suppressors of cytokine signaling (SOCS) proteins, between FKBP12 and ALK4, and between MDM2 and p53.     

A Novel Phosphopeptide Microarray Based Interactome Map in Breast Cancer Cells Reveals Phosphoprotein-GRB2 Cell Signaling Networks

The architecture of cellular proteins connected to form signaling pathways in response to internal and external cues is much more complex than a group of simple protein-protein interactions. Post translational modifications on proteins (e.g., phosphorylation of serine, threonine and tyrosine residues on proteins) initiate many downstream signaling events leading to protein-protein interactions and subsequent activation of signaling cascades leading to cell proliferation, cell differentiation and cell death. As evidenced by a rapidly expanding mass spectrometry database demonstrating protein phosphorylation at specific motifs, there is currently a large gap in understanding the functional significance of phosphoproteins with respect to their specific protein connections in the signaling cascades. A comprehensive map that interconnects phospho-motifs in pathways will enable identification of nodal protein interactions that are sensitive signatures indicating a disease phenotype from the physiological hemostasis and provide clues into control of disease. Using a novel phosphopeptide microarray technology, we have mapped endogenous tyrosine-phosphoproteome interaction networks in breast cancer cells mediated by signaling adaptor protein GRB2, which transduces cellular responses downstream of several RTKs through the Ras-ERK signaling cascade. We have identified several previously reported motif specific interactions and novel interactions. The peptide microarray data indicate that various phospho-motifs on a single protein are differentially regulated in various cell types and shows global downregulation of phosphoprotein interactions specifically in cells with metastatic potential. The study has revealed novel phosphoprotein mediated signaling networks, which warrants further detailed analysis of the nodes of protein-protein interaction to uncover their biomarker or therapeutic potential.