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

Deciphering the mechanism of resistance by novel double mutations in pncA in Mycobacterium tuberculosis using protein structural graphs (PSG) and structural bioinformatic approaches

The evolution of MDR and XDR-TB is a growing concern and public health safety threat around the world. Gene mutations are the prime cause of drug resistance in tuberculosis, however the reports of double mutations further aggravated the situation. Despite the large-scale genomic sequencing and identification of novel mutations, structure investigation of the protein is still required to structurally and functionally characterize these novel mutations to design novel drugs for improved clinical outcome. Hence, we used structural bioinformatics approaches i.e. molecular modeling, residues communication and molecular simulation to understand the impact of novel double S59Y-L85P, D86G-V180F and S104G-V130 M mutation on the structure, function of pncA encoded Pyrazinamidase (PZase) and resistance of Pyrazinamide (PZA). Our results revealed that these mutations alter the binding paradigm and destabilize the protein to release the drug. Protein commination network (PCN) revealed variations in the hub residues and sub-networks which consequently alter the internal communication and signaling. The region 1-75 demonstrated higher flexibility in the mutant structures and minimal by the wild type which destabilize of the internally arranged beta-sheets which consequently reduce the binding of PZA and potentially Fe ion in the mutants. Hydrogen bonding analysis further validated the findings. The total binding free energy (ΔG) for each complex i.e. wild type -7.46 kcal/mol, S59Y-L85P -5.21 kcal/mol, S104G-V130 M -5.33 kcal/mol while for the D86G-V180F mutant the TBE was calculated to be -6.26 kcal/mol. This further confirms that these mutations reduce the binding energy of PZA for PZase and causes resistance in the effective therapy for TB. The trajectories motion was also observed to be affected by these mutations. In conclusion, these mutations use destabilizing approach to reduce the binding of PZA and causes resistance. These features can be used to design novel structure-based drugs against Tuberculosis.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms

Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo amino acid compositions, which were then carried out at two levels: categories and features. In brief, extra-tree was initially used for feature selection while SVM was then used to build the classification model. After that, the most suitable models for each category and each feature were selected by comparing with the three ensemble learning algorithms, that is, Random Forest, Adaboost, and Xgboost. According to our results, profile-based features were more suitable for building predictive models among the four categories. AUC value of the model constructed by Xgboost algorithm on independent data set could reach 92.66%. Moreover, Distance-based Residue, Physicochemical Distance Transformation and Profile-based Physicochemical Distance Transformation performed much better among the 17 features. AUC value of the Adaboost classifier constructed by Profile-based Physicochemical Distance Transformation on the independent dataset achieved 93.74%. Taken together, we proposed a better model with improved prediction capacity for protein-protein interactions between human and HCV in this study, which could provide practical reference for further experimental investigation into HCV-related diseases in future.Communicated by Ramaswamy H. Sarma.

Host-pathogen protein-nucleic acid interactions: A comprehensive review

Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention. Here, we discuss the newest advances in studies of molecular interactions involving pathogen nucleic acids and host factors, including their drug design, molecular structure and specific patterns. We observed that two groups of nucleic acid recognizing molecules, Toll-like receptors (TLRs) and the cytoplasmic retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) form the backbone of host responses to pathogen nucleic acids, with additional support provided by absent in melanoma 2 (AIM2) and DNA-dependent activator of Interferons (IFNs)-regulatory factors (DAI) like cytosolic activity. We review the structural, immunological, and other biological aspects of these representative groups of molecules, especially in terms of their target specificity and affinity and challenges in leveraging host-pathogen protein-nucleic acid interactions (HP-PNI) in drug discovery.

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.

Structural and Dynamic Insights into the W68L, L85P, and T87A Mutations of Mycobacterium tuberculosis Inducing Resistance to Pyrazinamide

Tuberculosis (TB), the most frequent bacterium-mediated infectious disease caused by Mycobacterium tuberculosis, has been known to infect humans since ancient times. Although TB is common worldwide, the most recent report by the WHO (World Health Organization) listed the three countries of India, China, and Russia with 27%, 14%, and 8% of the global burden of TB, respectively. It has been reported that resistance to TB drugs, particularly by the pncA gene to the pyrazinamide drug due to mutations, significantly affects the effective treatment of TB. Understanding the mechanism of drug resistance using computational methods is of great interest to design effective TB treatment, exploring the structural features with these tools. Thus, keeping in view the importance of these methods, we employed state-of-the-art computational methods to study the mechanism of resistance caused by the W68L, L85P, and T87A mutations recently reported in 2021. We employed a molecular docking approach to predict the binding conformation and studied the dynamic properties of each complex using molecular dynamics simulation approaches. Our analysis revealed that compared to the wildtype, these three mutations altered the binding pattern and reduced the binding affinity. Moreover, the structural dynamic features also showed that these mutations significantly reduced the structural stability and packing, particularly by the W68L and L85P mutations. Moreover, principal component analysis, free energy landscape, and the binding free energy results revealed variation in the protein’s motion and the binding energy. The total binding free energy was for the wildtype −9.61 kcal/mol, W68L −7.57 kcal/mol, L85P −6.99 kcal/mol, and T87A −7.77 kcal/mol. Our findings can help to design a structure-based drug against the MDR (multiple drug-resistant) TB.

HantavirusesDB: Vaccinomics and RNA-based therapeutics database for the potentially emerging human respiratory pandemic agents

Hantaviruses are etiological agents of several severe respiratory illnesses in humans and their human-to-human transmission has been reported. To cope with any potential pandemic, this group of viruses needs further research and a data platform. Therefore, herein we developed a database "HantavirusesDB (HVdb)", where genomics, proteomics, immune resource, RNAi based therapeutics and information on the 3D structures of druggable targets of the Orthohantaviruses are provided on a single platform. The database allows the researchers to effectively map the therapeutic strategies by designing multi-epitopes subunit vaccine and RNA based therapeutics. Moreover, the ease of the web interface allow the users to retrieve specific information from the database. Because of the high quality and excellent functionality of the HVdb, therapeutic research of Hantaviruses can be accelerated, and data analysis might be a foundation to design better treatment strategies targeting the hantaviruses. The database is accessible at http://hvdb.dqweilab-sjtu.com/index.php.

Structural Profiling of Bacterial Effectors Reveals Enrichment of Host-Interacting Domains and Motifs

Effector proteins are bacterial virulence factors secreted directly into host cells and, through extensive interactions with host proteins, rewire host signaling pathways to the advantage of the pathogen. Despite the crucial role of globular domains as mediators of protein-protein interactions (PPIs), previous structural studies of bacterial effectors are primarily focused on individual domains, rather than domain-mediated PPIs, which limits their ability to uncover systems-level molecular recognition principles governing host-bacteria interactions. Here, we took an interaction-centric approach and systematically examined the potential of structural components within bacterial proteins to engage in or target eukaryote-specific domain-domain interactions (DDIs). Our results indicate that: 1) effectors are about six times as likely as non-effectors to contain host-like domains that mediate DDIs exclusively in eukaryotes; 2) the average domain in effectors is about seven times as likely as that in non-effectors to co-occur with DDI partners in eukaryotes rather than in bacteria; and 3) effectors are about nine times as likely as non-effectors to contain bacteria-exclusive domains that target host domains mediating DDIs exclusively in eukaryotes. Moreover, in the absence of host-like domains or among pathogen proteins without domain assignment, effectors harbor a higher variety and density of short linear motifs targeting host domains that mediate DDIs exclusively in eukaryotes. Our study lends novel quantitative insight into the structural basis of effector-induced perturbation of host-endogenous PPIs and may aid in the design of selective inhibitors of host-pathogen interactions.

A Molecular Interaction Map of Klebsiella pneumoniae and Its Human Host Reveals Potential Mechanisms of Host Cell Subversion

Klebsiella pneumoniae is a leading cause of pneumonia and septicemia across the world. The rapid emergence of multidrug-resistant K. pneumoniae strains necessitates the discovery of effective drugs against this notorious pathogen. However, there is a dearth of knowledge on the mechanisms by which this deadly pathogen subverts host cellular machinery. To fill this knowledge gap, our study attempts to identify the potential mechanisms of host cell subversion by building a K. pneumoniae-human interactome based on rigorous computational methodology. The putative host targets inferred from the predicted interactome were found to be functionally enriched in the host's immune surveillance system and allied functions like apoptosis, hypoxia, etc. A multifunctionality-based scoring system revealed P53 as the most multifunctional protein among host targets accompanied by HIF1A and STAT1. Moreover, mining of host protein-protein interaction (PPI) network revealed that host targets interact among themselves to form a network (TTPPI), where P53 and CDC5L occupy a central position. The TTPPI is composed of several inter complex interactions which indicate that K. pneumoniae might disrupt functional coordination between these protein complexes through targeting of P53 and CDC5L. Furthermore, we identified four pivotal K. pneumoniae-targeted transcription factors (TTFs) that are part of TTPPI and are involved in generating host's transcriptional response to K. pneumoniae-mediated sepsis. In a nutshell, our study identifies some of the pivotal molecular targets of K. pneumoniae which primarily correlate to the physiological response of host during K. pneumoniae-mediated sepsis.

Co-evolution between codon usage and protein-protein interaction in bacteria

We study the correlation between the codon usage bias of genetic sequences and the network features of protein-protein interaction (PPI) in bacterial species. We use PCA techniques in the space of codon bias indices to show that genes with similar patterns of codon usage have a significantly higher probability that their encoded proteins are functionally connected and interacting. Importantly, this signal emerges when multiple aspects of codon bias are taken into account at the same time. The present study extends our previous observations on E. coli over a wide set of 34 bacteria. These findings could allow for future investigations on the possible effects of codon bias on the topology of the PPI network, with the aim of improving existing bioinformatics methods for predicting protein interactions.

Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches

BACKGROUND

Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions.

METHODS

In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods.

RESULTS

We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions.

CONCLUSION

The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry

Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces-interfaces-of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.

Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.

A new sequence based encoding for prediction of host-pathogen protein interactions

Pathogen-host interactions are very important to figure out the infection process at the molecular level, where pathogen proteins physically bind to human proteins to manipulate critical biological processes in the host cell. Data scarcity and data unavailability are two major problems for computational approaches in the prediction of pathogen-host interactions. Developing a computational method to predict pathogen-host interactions with high accuracy, based on protein sequences alone, is of great importance because it can eliminate these problems. In this study, we propose a novel and robust sequence based feature extraction method, named Location Based Encoding, to predict pathogen-host interactions with machine learning based algorithms. In this context, we use Bacillus Anthracis and Yersinia Pestis data sets as the pathogen organisms and human proteins as the host model to compare our method with sequence based protein encoding methods, which are widely used in the literature, namely amino acid composition, amino acid pair, and conjoint triad. We use these encoding methods with decision trees (Random Forest, j48), statistical (Bayesian Networks, Naive Bayes), and instance based (kNN) classifiers to predict pathogen-host interactions. We conduct different experiments to evaluate the effectiveness of our method. We obtain the best results among all the experiments with RF classifier in terms of F1, accuracy, MCC, and AUC.

Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions

Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.

Structure-based prediction of West Nile virus-human protein-protein interactions

In recent years, West Nile virus (WNV) has posed a great threat to global human health due to its explosive spread. Studying the protein-protein interactions (PPIs) between WNV and human is beneficial for understanding the pathogenesis of WNV and the immune response mechanism of human against WNV infection at the molecular level. In this study, we identified the human target proteins which interact with WNV based on protein structure similarity, and then the interacting pairs were filtered by the subcellular co-localization information. As a result, a network of 3346 interactions was constructed, involving 6 WNV proteins and 1970 human target proteins. To our knowledge, this is the first predicted interactome for WNV-human. By analyzing the topological properties and evolutionary rates of the human target proteins, it was demonstrated that these proteins tend to be the hub and bottleneck proteins in the human PPI network and are more conserved than the non-target ones. Triplet analysis showed that the target proteins are adjacent to each other in the human PPI network, suggesting that these proteins may have similar biological functions. Further, the functional enrichment analysis indicated that the target proteins are mainly involved in virus process, transcription regulation, cell adhesion, and so on. In addition, the common and specific targets were identified and compared based on the networks between WNV-human and Dengue virus II (DENV2)-human. Finally, by combining topological features and existing drug target information, we identified 30 potential anti-WNV human targets, among which 11 ones were reported to be associated with WNV infection. Communicated by Ramaswamy H. Sarma.

Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions

Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.

Protein-protein interactions of human viruses

Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.

Computational and Experimental Approaches to Predict Host-Parasite Protein-Protein Interactions

In host-parasite systems, protein-protein interactions are key to allow the pathogen to enter the host and persist within the host. The study of host-parasite molecular communication improves the understanding the mechanisms of infection, evasion of the host immune system and tropism across different tissues. Current trends in parasitology focus on unraveling host-parasite protein-protein interactions to aid the development of new strategies to combat pathogenic parasites with better treatments and prevention mechanisms. Due to the complexity of capturing experimentally these interactions, computational approaches integrating data from different sources (mainly "omics" data) become key to complement or support experimental approaches. Here, we focus on the application of experimental and computational methods in the prediction of host-parasite interactions and highlight the potential of each of these methods in specific contexts.

Structural host-microbiota interaction networks

Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins

BACKGROUND

Fusobacterium nucleatum is a gram-negative anaerobic species residing in the oral cavity and implicated in several inflammatory processes in the human body. Although F. nucleatum abundance is increased in inflammatory bowel disease subjects and is prevalent in colorectal cancer patients, the causal role of the bacterium in gastrointestinal disorders and the mechanistic details of host cell functions subversion are not fully understood.

RESULTS

We devised a computational strategy to identify putative secreted F. nucleatum proteins (FusoSecretome) and to infer their interactions with human proteins based on the presence of host molecular mimicry elements. FusoSecretome proteins share similar features with known bacterial virulence factors thereby highlighting their pathogenic potential. We show that they interact with human proteins that participate in infection-related cellular processes and localize in established cellular districts of the host-pathogen interface. Our network-based analysis identified 31 functional modules in the human interactome preferentially targeted by 138 FusoSecretome proteins, among which we selected 26 as main candidate virulence proteins, representing both putative and known virulence proteins. Finally, six of the preferentially targeted functional modules are implicated in the onset and progression of inflammatory bowel diseases and colorectal cancer.

CONCLUSIONS

Overall, our computational analysis identified candidate virulence proteins potentially involved in the F. nucleatum-human cross-talk in the context of gastrointestinal diseases.

Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data

Pathogenic microorganisms exploit host cellular mechanisms and evade host defense mechanisms through molecular pathogen-host interactions (PHIs). Therefore, comprehensive analysis of these PHI networks should be an initial step for developing effective therapeutics against infectious diseases. Computational prediction of PHI data is gaining increasing demand because of scarcity of experimental data. Prediction of protein-protein interactions (PPIs) within PHI systems can be formulated as a classification problem, which requires the knowledge of non-interacting protein pairs. This is a restricting requirement since we lack datasets that report non-interacting protein pairs. In this study, we formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data. This eliminates the abovementioned requirement and enables us to predict new interactions without randomly labeling protein pairs as non-interacting. Domain-domain associations are used to filter the predicted results leading to 175 novel PHIs between 170 human proteins and 105 viral proteins. To compare our results with the state-of-the-art studies that use a binary classification formulation, we modified our settings to consider the same formulation. Detailed evaluations are conducted and our results provide more than 10 percent improvements for accuracy and AUC (area under the receiving operating curve) results in comparison with state-of-the-art methods.

Identification of Histone Deacetylase (HDAC) as a drug target against MRSA via interolog method of protein-protein interaction prediction

Patently, Protein-Protein Interactions (PPIs) lie at the core of significant biological functions and make the foundation of host-pathogen relationships. Hence, the current study is aimed to use computational biology techniques to predict host-pathogen Protein-Protein Interactions (HP-PPIs) between MRSA and Humans as potential drug targets ultimately proposing new possible inhibitors against them. As a matter of fact this study is based on the Interolog method which implies that homologous proteins retain their ability to interact. A distant homolog approach based on Interolog method was employed to speculate MRSA protein homologs in Humans using PSI-BLAST. In addition the protein interaction partners of these homologs as listed in Database of Interacting Proteins (DIP) were predicted to interact with MRSA as well. Moreover, a direct approach using BLAST was also applied so as to attain further confidence in the strategy. Consequently, the common HP-PPIs predicted by both approaches are suggested as potential drug targets (22%) whereas, the unique HP-PPIs estimated only through distant homolog approach are presented as novel drug targets (12%). Furthermore, the most repeated entry in our results was found to be MRSA Histone Deacetylase (HDAC) which was then modeled using SWISS-MODEL. Eventually, small molecules from ZINC, selected randomly, were docked against HDAC using Auto Dock and are suggested as potential binders (inhibitors) based on their energetic profiles. Thus the current study provides basis for further in-depth analysis of such data which not only include MRSA but other deadly pathogens as well.

Issues in performance evaluation for host-pathogen protein interaction prediction

The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein-protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host-pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose.

Experimental approaches to phenotypic diversity in infection

Microbial infections are burdening human health, even after the advent of antibiotics, vaccines and hygiene. Thus, infection biology has aimed at the molecular understanding of the pathogen-host interaction. This has revealed key virulence factors, host cell signaling pathways and immune responses. However, our understanding of the infection process is still incomplete. Recent evidence suggests that phenotypic diversity can have important consequences for the infection process. Diversity arises from the formation of distinct subpopulations of pathogen cells (with distinct virulence factor expression patterns) and host cells (with distinct response capacities). For technical reasons, such phenotypic diversity has often been overlooked. We are highlighting several striking examples and discuss the experimental approaches available for analyzing the different subpopulations. Single cell reporters and approaches from systems biology do hold much promise.

Mining host-pathogen protein interactions to characterize Burkholderia mallei infectivity mechanisms

Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections and intracellular spread.

Burkholderia species need to manipulate many host processes and pathways in order to establish a successful intracellular infection in eukaryotic host organisms. Burkholderia mallei uses secreted virulence factor proteins as a means to execute host-pathogen interactions and promote pathogenesis. While validated virulence factor proteins have been shown to attenuate infection in animal models, their actual roles in modifying and influencing host processes are not well understood. Here, we used host-pathogen protein-protein interactions derived from yeast two-hybrid screens to study nine known B. mallei virulence factors and map out potential virulence mechanisms. From the data, we derived both general and specific insights into Burkholderia host-pathogen infectivity pathways. We showed that B. mallei virulence factors tended to target multifunctional host proteins, proteins that interacted with each other, and host proteins with a large number of interacting partners. We also identified similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica using a novel host-pathogen interactions alignment algorithm. Importantly, our data are compatible with a framework in which multiple B. mallei virulence factors broadly influence key host processes related to ubiquitin-mediated proteolysis and focal adhesion. This provides B. mallei the means to modulate and adapt the host-cell environment to advance infection.

Computational approaches for prediction of pathogen-host protein-protein interactions

Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions.

Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions

Salmonellosis is the most frequent foodborne disease worldwide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs) between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed.

Network representations of immune system complexity

The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

2

Laboratory Methods and Technologies > Macromolecular Interactions, Methods

Prediction of interactions between viral and host proteins using supervised machine learning methods

BACKGROUND

Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics.

METHODS

In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques.

RESULTS

Out of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus "C protein" binds to membrane docking protein, while "X protein" and "P protein" interacts with cell-killing and metabolic process proteins, respectively.

CONCLUSION

The proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties

Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.

Impact of Salmonella enterica Type III Secretion System Effectors on the Eukaryotic Host Cell

Type III secretion systems are molecular machines used by many Gram-negative bacterial pathogens to inject proteins, known as effectors, directly into eukaryotic host cells. These proteins manipulate host signal transduction pathways and cellular processes to the pathogen’s advantage. Salmonella enterica possesses two virulence-related type III secretion systems that deliver more than forty effectors. This paper reviews our current knowledge about the functions, biochemical activities, host targets, and impact on host cells of these effectors. First, the concerted action of effectors at the cellular level in relevant aspects of the interaction between Salmonella and its hosts is analyzed. Then, particular issues that will drive research in the field in the near future are discussed. Finally, detailed information about each individual effector is provided.