Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer

Shared interactions of six neurotropic viruses with 38 human proteins: a computational and literature-based exploration of viral interactions and hijacking of human proteins in neuropsychiatric disorders

INTRODUCTION

Viral infections may disrupt the structural and functional integrity of the nervous system, leading to acute conditions such as encephalitis, and neuropsychiatric conditions as mood disorders, schizophrenia, and neurodegenerative diseases. Investigating viral interactions of human proteins may reveal mechanisms underlying these effects and offer insights for therapeutic interventions. This study explores molecular interactions of virus and human proteins that may be related to neuropsychiatric disorders.

METHODS

Herpes Simplex Virus-1 (HSV-1), Cytomegalovirus (CMV), Epstein-Barr Virus (EBV), Influenza A virus (IAV) (H1N1, H5N1), and Human Immunodeficiency Virus (HIV1&2) were selected as key viruses. Protein structures for each virus were accessed from the Protein Data Bank and analyzed using the HMI-Pred web server to detect interface mimicry between viral and human proteins. The PANTHER classification system was used to categorize viral-human protein interactions based on function and cellular localization.

RESULTS

Energetically favorable viral-human protein interactions were identified for HSV-1 (467), CMV (514), EBV (495), H1N1 (3331), H5N1 (3533), and HIV 1&2 (62425). Besides immune and apoptosis-related pathways, key neurodegenerative pathways, including those associated with Parkinson's and Huntington's diseases, were frequently interacted. A total of 38 human proteins, including calmodulin 2, Ras-related botulinum toxin substrate 1 (Rac1), PDGF-β, and vimentin, were found to interact with all six viruses.

CONCLUSION

The study indicates a substantial number of energetically favorable interactions between human proteins and selected viral proteins, underscoring the complexity and breadth of viral strategies to hijack host cellular mechanisms. Further in vivo and in vitro validation is required to understand the implications of these interactions.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

Potential contribution of Helicobacter pylori proteins in the pathogenesis of type 1 gastric neuroendocrine tumor and urticaria. In silico approach

BACKGROUND

Helicobacter pylori has been linked to several diseases such as chronic urticaria, gastritis, and type 1 gastric neuroendocrine tumors (type 1 gNET). Although these diseases seem to have different mechanisms, their relationship with H. pylori suggests a common inflammatory pathway.

OBJECTIVE

To identify potential cross-reactive antigens between H. pylori and humans involved in chronic urticaria and type 1 gNET.

METHODS

Alignment was carried out among human proteins associated with urticaria (9 proteins), type 1 gNET (32 proteins), and H. pylori proteome. We performed pairwise alignment among the human and H. pylori antigens with PSI-BLAST. Modeling based on homology was done with the Swiss model server and epitope prediction with the Ellipro server. Epitopes were located on a 3D model using PYMOL software.

RESULTS

The highest conserved sequence was found between the human HSP 60 antigen and the H. pylori chaperonin GroEL with an identity of 54% and a cover of 92%, followed by the alpha and gamma enolases and two H. pylori phosphopyruvate hydratase, both with an identity and cover of 48% and 96%, respectively. The H/K ATPase (Chain A) showed high identity with two H. pylori proteins (35.21% with both P-type ATPase), but with low cover (only 6%). We observed eight linear and three discontinuous epitopes for human HSP 60 and three lineal and one discontinuous epitope for both alpha-enolase and gamma enolase, high conserved with H. pylori sequences.

CONCLUSION

Some type 1 gNET antigens shared potential cross-reactive epitopes with H. pylori proteins, suggesting that molecular mimicry could be a mechanism that explains the relationship between the infection and this disease. Studies evaluating the functional impact of this relationship are needed.

HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry

SUMMARY

HMI-PRED 2.0 is a publicly available web service for the prediction of host-microbe protein-protein interaction by interface mimicry that is intended to be used without extensive computational experience. A microbial protein structure is screened against a database covering the entire available structural space of complexes of known human proteins.

AVAILABILITY AND IMPLEMENTATION

HMI-PRED 2.0 provides user-friendly graphic interfaces for predicting, visualizing and analyzing host-microbe interactions. HMI-PRED 2.0 is available at https://hmipred.org/.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

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.

Delineating functional mechanisms of the p53/p63/p73 family of transcription factors through identification of protein-protein interactions using interface mimicry

Members of the p53 family of transcription factors-p53, p63, and p73-share a high degree of homology; however, members can be activated in response to different stimuli, perform distinct (sometimes opposing) roles and are expressed in different tissues. The level of complexity is increased further by the transcription of multiple isoforms of each homolog, which may interact or interfere with each other and can impact cellular outcome. Proteins perform their functions through interacting with other proteins (and/or with nucleic acids). Therefore, identification of the interactors of a protein and how they interact in 3D is essential to fully comprehend their roles. By utilizing an in silico protein-protein interaction prediction method-HMI-PRED-we predicted interaction partners of p53 family members and modeled 3D structures of these protein interaction complexes. This method recovered experimentally known interactions while identifying many novel candidate partners. We analyzed the similarities and differences observed among the interaction partners to elucidate distinct functions of p53 family members and provide examples of how this information may yield mechanistic insight to explain their overlapping versus distinct/opposing outcomes in certain contexts. While some interaction partners are common to p53, p63, and p73, the majority are unique to each member. Nevertheless, most of the enriched pathways associated with these partners are common to all members, indicating that the members target the same biological pathways but through unique mediators. p63 and p73 have more common enriched pathways compared to p53, supporting their similar developmental roles in different tissues.

From Helicobacter pylori infection to gastric cancer: Current evidence on the immune response

Gastric cancer (GC) is the result of a multifactorial process whose main components are infection by Helicobacter pylori (H. pylori), bacterial virulence factors, host immune response and environmental factors. The development of the neoplastic microenvironment also depends on genetic and epigenetic changes in oncogenes and tumor suppressor genes, which results in deregulation of cell signaling pathways and apoptosis process. This review summarizes the main aspects of the pathogenesis of GC and the immune response involved in chronic inflammation generated by H. pylori.

Host-microbiome protein-protein interactions capture disease-relevant pathways

Background

Host-microbe interactions are crucial for normal physiological and immune system development and are implicated in a variety of diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), obesity, and type 2 diabetes (T2D). Despite large-scale case-control studies aimed at identifying microbial taxa or genes involved in pathogeneses, the mechanisms linking them to disease have thus far remained elusive.

Results

To identify potential pathways through which human-associated bacteria impact host health, we leverage publicly-available interspecies protein-protein interaction (PPI) data to find clusters of microbiome-derived proteins with high sequence identity to known human-protein interactors. We observe differential targeting of putative human-interacting bacterial genes in nine independent metagenomic studies, finding evidence that the microbiome broadly targets human proteins involved in immune, oncogenic, apoptotic, and endocrine signaling pathways in relation to IBD, CRC, obesity, and T2D diagnoses.

Conclusions

This host-centric analysis provides a mechanistic hypothesis-generating platform and extensively adds human functional annotation to commensal bacterial proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02643-9.

SARS-CoV-2 Interactome 3D: A Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions

SUMMARY

We present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy.

AVAILABILITY AND IMPLEMENTATION

The server is available at https://interactome.ku.edu.tr/sars/.

Fermented kimchi rejuvenated precancerous atrophic gastritis via mitigating Helicobacter pylori-associated endoplasmic reticulum and oxidative stress

Dietary intervention to prevent Helicobacter pylori (H. pylori)-gastric cancer might be ideal by long-term intervention, rejuvenating action, and no risk of bacterial resistance. Stimulated with finding that kimchi prevented H. pylori-gastric cancer, we compared the efficacy of cancer preventive kimchi (cpkimchi) and standard recipe kimchi (skimchi) and the efficacy between fermented kimchi and non-fermented kimchi (kimuchi) in H. pylori-initiated gastric cancer model and explored novel mechanisms hinted from RNAseq transcriptome analysis. Animal models assessing gastric pathology on 24 and 36 weeks after H. pylori initiated, salt diet-promoted gastric mutagenesis model showed fermented cpkimchi afforded the best outcome of either rejuvenating atrophic gastritis or inhibiting tumorigenesis compared to skimchi and kimuchi. Highest inhibition of atrophic gastritis was achieved with cpkimchi, while significantly lower in kimuchi. Transcriptomic analysis showed ameliorated-endoplasmic reticulum (ER) stress, -oxidative stress, and -apoptosis as major rejuvenating action of cpkimchi. Homogenates from animal model showed that elevated expressions of p-PERK, IRE, ATF6, p-elf, and XBP1 in control group, while significantly decreased with dietary intake of only cpkimchi. Significantly increased expressions of HO-1 and γ-GCS were only noted with cpkimchi. Conclusively, long-term dietary intervention of fermented cpkimchi can be potential way preventing H. pylori-associated carcinogenesis via rejuvenation of atrophic gastritis.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Computational evaluation of anticipated PE_PGRS39 protein involvement in host-pathogen interplay and its integration into vaccine development

Mycobacterium tuberculosis causes more than 1 million deaths every year, which is higher than any other bacterial pathogen. Its success depends on its interaction with the host and its ability to regulate the host's immune system for its own survival. Mycobacterium tuberculosis H37Rv (Mtb) proteome consists of unique PE_PGRS family proteins, which present a significant role in bacterial pathogenesis over the past years. Earlier evidence suggests that some PE_PGRS proteins display fibronectin-binding activity. In this manuscript, computational characterization of the PE_PGRS39 protein has indicated something peculiar about this protein. Investigation showed that PE_PGRS39 is an extracellular protein that, instead of acting as fibronectin-binding protein, might mimic fibronectin which binds to alpha-5 beta-1 (α5β1) integrin. PE_PGRS39 protein additionally turned into proven pieces of evidence to have motifs such as DXXG and GGXGXD and PXXP that bind with guanosine triphosphate (GTP), calcium, and host Src homology 3 (SH3) domains, respectively, in conjunction with RGD-integrin binding. These interactions designate the direct role of PE_PGRS39 in bacterial pathogenesis via cell adhesion and signaling. Additionally, the analysis showed that PE_PGRS39 is an antigenic protein and epitope prediction provided functional regions of the protein that trigger a cellular immune response facilitated by T or B cells. Further, an experimental analysis could also open up new avenues for developing novel drugs by targeting signaling motifs or novel vaccines using functional epitopes that could evoke an immune response in the host.

APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data

Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud‐based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host‐pathogen protein‐protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud‐based multiomics data is discussed. In particular, a novel two‐layer machine learning model, namely APEX2S, is proposed for discovery of the protein‐protein interactions data. The results show that our model can better learn and predict from the accumulated host‐pathogen protein‐protein interactions.

HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry

Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.

In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks

Pathogen-host protein interactions are fundamental for pathogens to manipulate host signaling pathways and subvert host immune defense. For most pathogens, very few or no experimental studies have been conducted to investigate their signaling cross-talks with host. In this study, we propose a computational framework to validate the biological assumption that human protein-protein interaction (PPI) networks alone are sufficient to infer pathogen-host PPIs via pathogen functional mimicry. Pathogen functional mimicry assumes that a pathogen functionally mimics and substitutes host counterpart proteins in order for the pathogen to get involved in or hijack the host cellular processes. Through pathogen functional mimicry defined via gene ontology (GO) semantic similarity, we first use the known human PPIs as templates to infer pathogen-host PPIs, and the PPIs are further used as training data to build an l₂-regularized logistic regression model for novel pathogen-host PPI prediction. Independent tests on the experimental data from human immunodeficiency virus and Francisella tularensis validate the effectiveness of the proposed pathogen functional mimicry technique. Performance comparisons also show that the proposed technique y excels the existing pathogen sequence mimicry approaches and transfer learning methods. The proposed framework provides a new avenue to study the experimentally less-studied pathogens in the worst scenarios that very few or no experimental pathogen-host PPIs are available. As two case studies, we apply the proposed framework to Salmonella typhimurium and Human respiratory syncytial virus to reconstruct the pathogen-host PPI networks and further investigate the interference of these two pathogens with human immune signaling and transcription regulatory system.

Investigating host-bacterial interactions among enteric pathogens

Background

In 2017, World Health Organization (WHO) published a catalogue of 12 families of antibiotic-resistant “priority pathogens” that are posing the greatest threats to human health. Six of these dreaded pathogens are known to infect the human gastrointestinal system. In addition to causing gastrointestinal and systemic infections, these pathogens can also affect the composition of other microbes constituting the healthy gut microbiome. Such aberrations in gut microbiome can significantly affect human physiology and immunity. Identifying the virulence mechanisms of these enteric pathogens are likely to help in developing newer therapeutic strategies to counter them.

Results

Using our previously published in silico approach, we have evaluated (and compared) Host-Pathogen Protein-Protein Interaction (HPI) profiles of four groups of enteric pathogens, namely, different species of Escherichia, Shigella, Salmonella and Vibrio. Results indicate that in spite of genus/ species specific variations, most enteric pathogens possess a common repertoire of HPIs. This core set of HPIs are probably responsible for the survival of these pathogen in the harsh nutrient-limiting environment within the gut. Certain genus/ species specific HPIs were also observed.

Conslusions

The identified bacterial proteins involved in the core set of HPIs are expected to be helpful in understanding the pathogenesis of these dreaded gut pathogens in greater detail. Possible role of genus/ species specific variations in the HPI profiles in the virulence of these pathogens are also discussed. The obtained results are likely to provide an opportunity for development of novel therapeutic strategies against the most dreaded gut pathogens.

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.

Structural biology meets data science: does anything change?

Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask: can these two fields impact one another in deep and hitherto unforeseen ways? We posit that the answer is yes. New biological knowledge lies in the relationships between sequence, structure, function and disease, all of which play out on the stage of evolution, and data science enables us to elucidate these relationships at scale. Here, we consider the above question from the five key pillars of data science: acquisition, engineering, analytics, visualization and policy, with an emphasis on machine learning as the premier analytics approach.

Helicobacter pylori associated factors in the development of gastric cancer with special reference to the early-onset subtype

Nowadays, gastric cancer is one of the most common neoplasms and the fourth cause of cancer-related death on the world. Regarding the age at the diagnosis it is divided into early-onset gastric carcinoma (45 years or younger) and conventional gastric cancer (older than 45). Gastric carcinomas are rarely observed in young population and rely mostly on genetic factors, therefore provide the unique model to study genetic and environmental alternations. The latest research on early-onset gastric cancer are trying to explain molecular and genetic basis, because young patients are less exposed to environmental factors predisposing to cancer. In the general population, Helicobacter pylori, has been particularly associated with intestinal subtype of gastric cancers. The significant association of Helicobacter pylori infection in young patients with gastric cancers suggests that the bacterium has an etiologic role in both diffuse and intestinal subtypes of early-onset gastric cancers. In this paper we would like to ascertain the possible role of Helicobacter pylori infection in the development of gastric carcinoma in young patients. The review summarizes recent literature on early-onset gastric cancers with special reference to Helicobacter pylori infection.