Host-pathogen Protein-protein Interactions Research Articles

Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research.

Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.

Covalent Inhibition of a Host-Pathogen Protein-Protein Interaction Reduces the Infectivity of Streptococcus pneumoniae.

The ever-expanding antibiotic resistance urgently calls for novel antibacterial therapeutics, especially those with a new mode of action. We report herein our exploration of protein-protein interaction (PPI) inhibition as a new mechanism to thwart bacterial pathogenesis. Specifically, we describe potent and specific inhibitors of the pneumococcal surface protein PspC, an important virulence factor that facilitates the infection of Streptococcus pneumoniae. Specifically, PspC has been documented to recruit human complement factor H (hFH) to suppress host complement activation and/or promote the bacterial attachment to host tissues. The CCP9 domain of hFH was recombinantly expressed to inhibit the PspC-hFH interaction as demonstrated on live pneumococcal cells. The inhibitor allowed for the first pharmacological intervention of the PspC-hFH interaction. This PPI inhibition reduced pneumococci's attachment to epithelial cells and also resensitized the D39 strain of S. pneumoniae for opsonization. Importantly, we have further devised covalent versions of CCP9, which afforded long-lasting PspC inhibition with low nanomolar potency. Overall, our results showcase the promise of PPI inhibition for combating bacterial infections as well as the power of covalent inhibitors.

An Extended Feature Representation Technique for Predicting Sequenced-based Host-pathogen Protein-protein Interaction

Background: The use of machine learning models in sequence-based Protein-Protein Interaction prediction typically requires the conversion of amino acid sequences into feature vectors. From the literature, two approaches have been used to achieve this transformation. These are referred to as the Independent Protein Feature (IPF) and Merged Protein Feature (MPF) extraction methods. As observed, studies have predominantly adopted the IPF approach, while others preferred the MPF method, in which host and pathogen sequences are concatenated before feature encoding. Objective: This presents the challenge of determining which approach should be adopted for improved HPPPI prediction. Therefore, this work introduces the Extended Protein Feature (EPF) method. Methods: The proposed method combines the predictive capabilities of IPF and MPF, extracting essential features, handling multicollinearity, and removing features with zero importance. EPF, IPF, and MPF were tested using bacteria, parasite, virus, and plant HPPPI datasets and were deployed to machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Naïve Bayes (NB), Logistic Regression (LR), and Deep Forest (DF). Results: The results indicated that MPF exhibited the lowest performance overall, whereas IPF performed better with decision tree-based models, such as RF and DF. In contrast, EPF demonstrated improved performance with SVM, LR, NB, and MLP and also yielded competitive results with DF and RF. Conclusion: In conclusion, the EPF approach developed in this study exhibits substantial improvements in four out of the six models evaluated. This suggests that EPF offers competitiveness with IPF and is particularly well-suited for traditional machine learning models.

Current progress on the computational methods for prediction of host-pathogen protein-protein interaction in the Ganoderma boninense-oil palm pathosystem

Ganoderma boninense is a major pathogen for basal stem rot disease that can depolymerize lignocellulosic materials of the oil palm plant by secreting the cell wall-degrading enzymes. The management of this disease is complicated by the asymptomatic phase mediated by the protein-protein interaction (PPI) between the pathogen's effector and plant proteins. There is a lack of network-wide studies on the PPI to elucidate the action of effectors on plant pathways from a systemic perspective as the computational prediction of host-pathogen PPI (HP PPI) is focused on human interaction. Hence, this minireview explores the current computational methods for predicting HP PPI and proposes an approach to predict PPI between G. boninense and oil palm.

Current trends of host–pathogen relationship in shrimp infectious disease via computational protein–protein interaction: A bibliometric analysis

Protein–protein interactions (PPIs) are essential for understanding cell physiology in normal and pathological conditions, as they might involve in all cellular processes. PPIs have been widely used to elucidate the pathobiology of human and plant diseases. Therefore, they can also be used to unveil the pathobiology of infectious diseases in shrimp, which is one of the high-risk factors influencing the success or failure of shrimp production. PPI network analysis, specifically host–pathogen PPI (HP-PPI), provides insights into the molecular interactions between the shrimp and pathogens. This review quantitatively analyzed the research trends within this field through bibliometric analysis using specific keywords, countries, authors, organizations, journals, and documents. This analysis has screened 206 records from the Scopus database for determining eligibility, resulting in 179 papers that were retrieved for bibliometric analysis. The analysis revealed that China and Thailand were the driving forces behind this specific field of research and frequently collaborated with the United States. Aquaculture and Diseases of Aquatic Organisms were the prominent sources for publications in this field. The main keywords identified included “white spot syndrome virus,” “WSSV,” and “shrimp.” We discovered that studies on HP-PPI are currently quite scarce. As a result, we further discussed the significance of HP-PPI by highlighting various approaches that have been previously adopted. These findings not only emphasize the importance of HP-PPI but also pave the way for future researchers to explore the pathogenesis of infectious diseases in shrimp. By doing so, preventative measures and enhanced treatment strategies can be identified.

Identification of virulence-associated factors in Vibrio parahaemolyticus with special reference to moonlighting protein: a secretomics study.

Vibrio parahaemolyticus causes seafood-borne gastroenteritis infection in human which can even lead to death. The pathogenic strain of V. parahaemolyticus secretes different types of virulence factors thatare directly injected into the host cell by a different type of secretion system which helps bacteria to establish its own ecological niche within the organism. Therefore, the aim of this study was to isolate the extracellular secreted proteins from the trh positive strain of V. parahaemolyticus and identify them using two-dimensional gel electrophoresis and MALDI-TOFMS/MS. Seventeen different cellular proteins viz, Carbamoyl-phosphate synthase, 5-methyltetrahydropteroyltriglutamate, tRNA-dihydrouridine synthase, Glycerol-3-phosphate dehydrogenase, Orotidine 5'-phosphate decarboxylase, Molybdenum import ATP-binding protein, DnaJ, DNA polymerase IV, Ribosomal RNA small subunit methyltransferase G, ATP synthase subunit delta and gamma, Ribosome-recycling factor, 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase, tRNA pseudouridine synthase B, Ditrans, polycis-undecaprenyl-diphosphate synthase, Oxygen-dependent coproporphyrinogen-III oxidase, and Peptide deformylase 2 were identified whichare mainly involved in different metabolic and biosynthetic pathways. Furthermore, the molecular function of the identified proteins were associated with catalytic activity, ligase activity, transporter, metal binding, and ATP synthase when they are intercellular. However, to understand the importance of these secreted proteins in the infection and survival of bacteria inside the host cell, pathogen-host protein-protein interactions (PPIs) were carried out which identified the association of eight secreted proteins with 41 human proteins involved in different cellular pathways, including ubiquitination degradation, adhesion, inflammation, immunity, and programmed cell death. The present study provides unreported strategies on host-cell environment's survival and adaptation mechanisms for the successful establishment of infections and intracellular propagation.

Understanding the mechanism of pathogenicity through interactome studies between Arachis hypogaea L. and Aspergillus flavus

Aspergillus flavus (A. flavus) infects the peanut seeds during pre-and post-harvest stages, causing seed quality destruction for humans and livestock consumption. Even though many resistant varieties were developed, the molecular mechanism of defense interactions of peanut against A. flavus still needs further investigation. Hence, an interologous host-pathogen protein interaction (HPPI) network was constructed to understand the subcellular level interaction mechanism between peanut and A. flavus. Out of the top 10 hub proteins of both organisms, protein phosphatase 2C and cyclic nucleotide-binding/kinase domain-containing protein and different ribosomal proteins were identified as candidate proteins involved in defense. Functional annotation and subcellular localization based characterization of HPPI identified protein SGT1 homolog, calmodulin and Rac-like GTP-binding proteins to be involved in defense response against fungus. The relevance of HPPI in infectious conditions was assessed using two transcriptome data which identified the interplay of host kinase class R proteins, bHLH TFs and cell wall related proteins to impart resistance against pathogen infection. Further, the pathogenicity analysis identified glycogen phosphorylase and molecular chaperone and allergen Mod-E/Hsp90/Hsp1 as potential pathogen targets to enhance the host defense mechanism. Hence, the computationally predicted host-pathogen PPI network could provide valuable support for molecular biology experiments to understand the host-pathogen interaction. SignificanceProtein-protein interactions execute significant cellular interactions in an organism and are influenced majorly by stress conditions. Here we reported the host-pathogen protein-protein interaction between peanut and A. flavus, and a detailed network analysis based on function, subcellular localization, gene co-expression, and pathogenicity was performed. The network analysis identified key proteins such as host kinase class R proteins, calmodulin, SGT1 homolog, Rac-like GTP-binding proteins bHLH TFs and cell wall related to impart resistance against pathogen infection. We observed the interplay of defense related proteins and cell wall related proteins predominantly, which could be subjected to further studies. The network analysis described in this study could be applied to understand other host-pathogen systems generally.

Protein-protein interaction network analysis on the whiteleg shrimp Penaeus vannamei and Vibrio parahaemolyticus host-pathogen relationship reveals possible proteins and pathways involved during infection

Whiteleg shrimp, Penaeus vannamei, is the prime candidate in shrimp aquaculture that significantly contributes to the economic growth of the global aquaculture sector. However, P. vannamei culture is constantly plagued with pathogenic infection of Vibrio spp., including Vibrio parahaemolyticus, which impairs shrimp production and consequently results in significant financial losses. Despite this persistent problem, the relationship between P. vannamei and V. parahaemolyticus remains unclear. The discovery of protein-protein interactions (PPIs) between host and pathogen might improve the understanding of bacterial infection in shrimp since most pathogenic bacteria infect hosts through multiple mechanisms. Hence, this study aims to identify the candidate effector proteins of V. parahaemolyticus and their targeted host proteins and potential pathways involved in its infection using a host-pathogen PPI approach. The P. vannamei – V. parahaemolyticus PPI (PVPPI) network was constructed using in silico methods, followed by clustering and pathway enrichment analyses. The constructed PVPPI network consisted of 448871 interactions between 4427 P. vannamei and 18119 V. parahaemolyticus proteins. Clustering analysis identified several effector proteins, i.e., secretion systems proteins, and their targeted proteins, including cell signalling proteins. The bacterial secretion system, mTOR signalling, and endocytosis were among the enriched pathways that might be involved during V. parahaemolyticus infection in P. vannamei. This study reports the first host-pathogen PPI network between P. vannamei and V. parahaemolyticus, where several hosts and pathogen proteins with potential pathways are highlighted. These findings offer new insights into the interaction between V. parahaemolyticus and P. vannamei, a vital aspect that could serve as a baseline for future disease prevention and treatment in the shrimp aquaculture industry.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions.

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Photo-ANA enables profiling of host-bacteria protein interactions during infection.

Bacterial pathogens rapidly change and adapt their proteome to cope with the environment in host cells and secrete effector proteins to hijack host targets and ensure their survival and proliferation during infection. Excessive host proteins make it difficult to profile pathogens' proteome dynamics by conventional proteomics. It is even more challenging to map pathogen-host protein-protein interactions in real time, given the low abundance of bacterial effectors and weak and transient interactions in which they may be involved. Here we report a method for selectively labeling bacterial proteomes using a bifunctional amino acid, photo-ANA, equipped with a bio-orthogonal handle and a photoreactive warhead, which enables simultaneous analysis of bacterial proteome reprogramming and pathogen-host protein interactions of Salmonella enterica serovar Typhimurium (S. Typhimurium) during infection. Using photo-ANA, we identified FLOT1/2 as host interactors of S. Typhimurium effector PipB2 in late-stage infection and globally profiled the extensive interactions between host proteins and pathogens during infection.

Drug repositioning via host-pathogen protein-protein interactions for the treatment of cervical cancer.

Integrating interaction data with biological knowledge can be a critical approach for drug development or drug repurposing. In this context, host-pathogen-protein-protein interaction (HP-PPI) networks are useful instrument to uncover the phenomena underlying therapeutic effects in infectious diseases, including cervical cancer, which is almost exclusively due to human papillomavirus (HPV) infections. Cervical cancer is one of the second leading causes of death, and HPV16 and HPV18 are the most common subtypes worldwide. Given the limitations of traditionally used virus-directed drug therapies for infectious diseases and, at the same time, recent cancer statistics for cervical cancer cases, the need for innovative treatments becomes clear. Accordingly, in this study, we emphasize the potential of host proteins as drug targets and identify promising host protein candidates for cervical cancer by considering potential differences between HPV subtypes (i.e., HPV16 and HPV18) within a novel bioinformatics framework that we have developed. Subsequently, subtype-specific HP-PPI networks were constructed to obtain host proteins. Using this framework, we next selected biologically significant host proteins. Using these prominent host proteins, we performed drug repurposing analysis. Finally, by following our framework we identify the most promising host-oriented drug candidates for cervical cancer. As a result of this framework, we discovered both previously associated and novel drug candidates, including interferon alfacon-1, pimecrolimus, and hyaluronan specifically for HPV16 and HPV18 subtypes, respectively. Consequently, with this study, we have provided valuable data for further experimental and clinical efforts and presented a novel bioinformatics framework that can be applied to any infectious disease.

PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets.

Semantic web standards have shown importance in the last 20 years in promoting data formalization and interlinking between the existing knowledge graphs. In this context, several ontologies and data integration initiatives have emerged in recent years for the biological area, such as the broadly used Gene Ontology that contains metadata to annotate gene function and subcellular location. Another important subject in the biological area is protein-protein interactions (PPIs) which have applications like protein function inference. Current PPI databases have heterogeneous exportation methods that challenge their integration and analysis. Presently, several initiatives of ontologies covering some concepts of the PPI domain are available to promote interoperability across datasets. However, the efforts to stimulate guidelines for automatic semantic data integration and analysis for PPIs in these datasets are limited. Here, we present PPIntegrator, a system that semantically describes data related to protein interactions. We also introduce an enrichment pipeline to generate, predict and validate new potential host-pathogen datasets by transitivity analysis. PPIntegrator contains a data preparation module to organize data from three reference databases and a triplification and data fusion module to describe the provenance information and results. This work provides an overview of the PPIntegrator system applied to integrate and compare host-pathogen PPI datasets from four bacterial species using our proposed transitivity analysis pipeline. We also demonstrated some critical queries to analyze this kind of data and highlight the importance and usage of the semantic data generated by our system. https://github.com/YasCoMa/ppintegrator, https://github.com/YasCoMa/ppi_validation_process and https://github.com/YasCoMa/predprin.

Affinity-Purification Combined with Crosslinking Mass Spectrometry for Identification and Structural Modeling of Host-Pathogen Protein-Protein Complexes.

Host-pathogen protein-protein interactions are highly complex and dynamic and mediate key steps in pathogen adhesion to host, host invasion, and colonization as well as immune evasion. In bacteria, these interactions most often involve specialized virulence factors or effector proteins that specifically target central host proteins. Here, I present a mass spectrometry-based proteomics approach starting with the identification of host-pathogen interactions by affinity-purification followed by mapping the specific host-pathogen protein-protein interaction interfaces by crosslinking mass spectrometry and structural modeling of the complexes.

Recent developments of sequence-based prediction of protein-protein interactions.

The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.

Analysis of Host–Bacteria Protein Interactions Reveals Conserved Domains and Motifs That Mediate Fundamental Infection Pathways

Adhesion and colonization of host cells by pathogenic bacteria depend on protein–protein interactions (PPIs). These interactions are interesting from the pharmacological point of view since new molecules that inhibit host-pathogen PPIs would act as new antimicrobials. Most of these interactions are discovered using high-throughput methods that may display a high false positive rate. The absence of curation of these databases can make the available data unreliable. To address this issue, a comprehensive filtering process was developed to obtain a reliable list of domains and motifs that participate in PPIs between bacteria and human cells. From a structural point of view, our analysis revealed that human proteins involved in the interactions are rich in alpha helix and disordered regions and poorer in beta structure. Disordered regions in human proteins harbor short sequence motifs that are specifically recognized by certain domains in pathogenic proteins. The most relevant domain–domain interactions were validated by AlphaFold, showing that a proper analysis of host-pathogen PPI databases can reveal structural conserved patterns. Domain–motif interactions, on the contrary, were more difficult to validate, since unstructured regions were involved, where AlphaFold could not make a good prediction. Moreover, these interactions are also likely accommodated by post-translational modifications, especially phosphorylation, which can potentially occur in 25–50% of host proteins. Hence, while common structural patterns are involved in host–pathogen PPIs and can be retrieved from available databases, more information is required to properly infer the full interactome. By resolving these issues, and in combination with new prediction tools like Alphafold, new classes of antimicrobials could be discovered from a more detailed understanding of these interactions.

ImitateDB: A database for domain and motif mimicry incorporating host and pathogen protein interactions.

Molecular mimicry of host proteins by pathogens constitutes a strategy to hijack the host pathways. At present, there is no dedicated resource for mimicked domains and motifs in the host–pathogen interactome. In this work, the experimental host–pathogen (HP) and host–host (HH) protein–protein interactions (PPIs) were collated. The domains and motifs of these proteins were annotated using CD Search and ScanProsite, respectively. Host and pathogen proteins with a shared host interactor and similar domain/motif constitute a mimicry pair exhibiting global structural similarity (domain mimicry pair; DMP) or local sequence motif similarity (motif mimicry pair; MMP). Mimicry pairs are likely to be co-expressed and co-localized. 1,97,607 DMPs and 32,67,568 MMPs were identified in 49,265 experimental HP-PPIs and organized in a web-based resource, ImitateDB (http://imitatedb.sblab-nsit.net) that can be easily queried. The results are externally integrated using hyperlinked domain PSSM ID, motif ID, protein ID and PubMed ID. Kinase, UL36, Smc and DEXDc were frequent DMP domains whereas protein kinase C phosphorylation, casein kinase 2 phosphorylation, glycosylation and myristoylation sites were frequent MMP motifs. Novel DMP domains SANT, Tudor, PhoX and MMP motif microbody C-terminal targeting signal, cornichon signature and lipocalin signature were proposed. ImitateDB is a novel resource for identifying mimicry in interacting host and pathogen proteins.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00726-022-03163-3.

A functional map of HIV-host interactions in primary human T cells

Human Immunodeficiency Virus (HIV) relies on host molecular machinery for replication. Systematic attempts to genetically or biochemically define these host factors have yielded hundreds of candidates, but few have been functionally validated in primary cells. Here, we target 426 genes previously implicated in the HIV lifecycle through protein interaction studies for CRISPR-Cas9-mediated knock-out in primary human CD4+ T cells in order to systematically assess their functional roles in HIV replication. We achieve efficient knockout (>50% of alleles) in 364 of the targeted genes and identify 86 candidate host factors that alter HIV infection. 47 of these factors validate by multiplex gene editing in independent donors, including 23 factors with restrictive activity. Both gene editing efficiencies and HIV-1 phenotypes are highly concordant among independent donors. Importantly, over half of these factors have not been previously described to play a functional role in HIV replication, providing numerous novel avenues for understanding HIV biology. These data further suggest that host-pathogen protein-protein interaction datasets offer an enriched source of candidates for functional host factor discovery and provide an improved understanding of the mechanics of HIV replication in primary T cells.

Computational Systems Biology of Alfalfa - Bacterial Blight Host-Pathogen Interactions: Uncovering the Complex Molecular Networks for Developing Durable Disease Resistant Crop.

Medicago sativa (also known as alfalfa), a forage legume, is widely cultivated due to its high yield and high-value hay crop production. Infectious diseases are a major threat to the crops, owing to huge economic losses to the agriculture industry, worldwide. The protein-protein interactions (PPIs) between the pathogens and their hosts play a critical role in understanding the molecular basis of pathogenesis. Pseudomonas syringae pv. syringae ALF3 suppresses the plant’s innate immune response by secreting type III effector proteins into the host cell, causing bacterial stem blight in alfalfa. The alfalfa-P. syringae system has little information available for PPIs. Thus, to understand the infection mechanism, we elucidated the genome-scale host-pathogen interactions (HPIs) between alfalfa and P. syringae using two computational approaches: interolog-based and domain-based method. A total of ∼14 M putative PPIs were predicted between 50,629 alfalfa proteins and 2,932 P. syringae proteins by combining these approaches. Additionally, ∼0.7 M consensus PPIs were also predicted. The functional analysis revealed that P. syringae proteins are highly involved in nucleotide binding activity (GO:0000166), intracellular organelle (GO:0043229), and translation (GO:0006412) while alfalfa proteins are involved in cellular response to chemical stimulus (GO:0070887), oxidoreductase activity (GO:0016614), and Golgi apparatus (GO:0005794). According to subcellular localization predictions, most of the pathogen proteins targeted host proteins within the cytoplasm and nucleus. In addition, we discovered a slew of new virulence effectors in the predicted HPIs. The current research describes an integrated approach for deciphering genome-scale host-pathogen PPIs between alfalfa and P. syringae, allowing the researchers to better understand the pathogen’s infection mechanism and develop pathogen-resistant lines.

A Comparative Computational Analysis Approach to Predict Significant Protein-Protein Interactions of Human and Vancomycin Resistant Enterococcus faecalis (VRE) to Prioritize Potential Drug Targets

Background: Various challenges exist in the treatment of infectious diseases due to the significant rise in drug resistance, resulting in the failure of antibiotic treatment. As a consequence, a dire need has arisen for the rethinking of the drug discovery cycle because of the challenge of drug resistance. The underlying cause of the infectious diseases depends upon associations within the Host-pathogen Protein- Protein Interactions (HP-PPIs) network, which represents a key to unlock new pathogenesis mechanisms. Hence, the elucidation of significant PPIs is a promising approach for the identification of potential drug targets. Objective: Identification of the most significant HP-PPIs and their partners, and targeting them to prioritize potential new drug targets against Vancomycin-resistant Enterococcus faecalis (VRE). Methods: We applied a computational approach based on one of the emerging techniques i.e. Interolog methodology to predict the significant Host-Pathogen PPIs. Structure-Based Studies were applied to model shortlisted protein structures and validate them through PSIPRED, PROCHECK, VERIFY3D, and ERRAT tools. Furthermore, 18,000 drug-like compounds from the ZINC library were docked against these proteins to study protein-chemical interactions using the AutoDock based molecular docking method. Results: The study resulted in the identification of 118 PPIs for Enterococcus faecalis, and prioritized two novel drug targets i.e. Exodeoxyribonuclease (ExoA) and ATP-dependent Clp protease proteolytic subunit (ClpP). Consequently, the docking program ranked 2,670 and 3,154 compounds as potential binders against Exodeoxyribonuclease and ATP-dependent Clp protease proteolytic subunit, respectively. Conclusion: Thereby, the current study enabled us to identify and prioritize potential PPIs in VRE and their interacting proteins in human hosts along with the pool of novel drug candidates.

HPIPred: Host–pathogen interactome prediction with phenotypic scoring

Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein–protein interactions as potential targets for antibacterial drug development. Available at: https://github.com/SysBioUAB/hpi_predictor.