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Idrees S, Paudel KR. Bioinformatics prediction and screening of viral mimicry candidates through integrating known and predicted DMI data. Arch Microbiol 2023; 206:30. [PMID: 38117335 DOI: 10.1007/s00203-023-03764-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
Domain-motif interactions (DMIs) represent transient bonds formed when a Short Linear Motif (SLiM) engages a globular domain via a compact contact interface. Understanding the mechanics of DMIs is critical for maintaining diverse regulatory processes and deciphering how various viruses hijack host cellular machinery. However, identifying DMIs through traditional in vitro and in vivo experiments is challenging due to their degenerate nature and small contact areas. Predictions often carry a high rate of false positives, necessitating rigorous in-silico validation before embarking on experimental work. This study assessed the binding energy changes in predicted SLiM instances through in-silico peptide exchange experiment, elucidating how they interact with known 3D DMI complexes. We identified a subset of potential mimicry candidates that exhibited effective binding affinities with native DMI structures, suggesting their potential to be true mimicry candidates. The identified viral SLiMs can be potential targets in developing therapeutics, opening new opportunities for innovative treatments that can be finely tuned to address the complex molecular underpinnings of various diseases. To gain a comprehensive understanding of identified DMIs, it is imperative to conduct further validation through experimental approaches.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia.
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, Faculty of Science, School of Life Sciences, Sydney, NSW, Australia.
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, Faculty of Science, School of Life Sciences, Sydney, NSW, Australia
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2
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Idrees S, Paudel KR, Sadaf T, Hansbro PM. How different viruses perturb host cellular machinery via short linear motifs. EXCLI JOURNAL 2023; 22:1113-1128. [PMID: 38054205 PMCID: PMC10694346 DOI: 10.17179/excli2023-6328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/18/2023] [Indexed: 12/07/2023]
Abstract
The virus interacts with its hosts by developing protein-protein interactions. Most viruses employ protein interactions to imitate the host protein: A viral protein with the same amino acid sequence or structure as the host protein attaches to the host protein's binding partner and interferes with the host protein's pathways. Being opportunistic, viruses have evolved to manipulate host cellular mechanisms by mimicking short linear motifs. In this review, we shed light on the current understanding of mimicry via short linear motifs and focus on viral mimicry by genetically different viral subtypes by providing recent examples of mimicry evidence and how high-throughput methods can be a reliable source to study SLiM-mediated viral mimicry.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Tayyaba Sadaf
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Philip M. Hansbro
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
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3
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Khan T, Raza S. Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review. Curr Top Med Chem 2023; 23:1640-1663. [PMID: 36725827 DOI: 10.2174/1568026623666230201144522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/14/2022] [Accepted: 12/19/2022] [Indexed: 02/03/2023]
Abstract
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.
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Affiliation(s)
- Tahmeena Khan
- Department of Chemistry, Integral University, Lucknow, 226026, U.P., India
| | - Saman Raza
- Department of Chemistry, Isabella Thoburn College, Lucknow, 226007, U.P., India
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4
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Jain A, Mittal S, Tripathi LP, Nussinov R, Ahmad S. Host-pathogen protein-nucleic acid interactions: A comprehensive review. Comput Struct Biotechnol J 2022; 20:4415-4436. [PMID: 36051878 PMCID: PMC9420432 DOI: 10.1016/j.csbj.2022.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 12/02/2022] Open
Abstract
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.
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Affiliation(s)
- Anuja Jain
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Shikha Mittal
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, 173234, India
| | - Lokesh P. Tripathi
- National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan
- Riken Center for Integrative Medical Sciences, Tsurumi, Yokohama, Kanagawa, Japan
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National, Laboratory for Cancer Research, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Israel
| | - Shandar Ahmad
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
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5
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Fang Y, Yang Y, Liu C. New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions. Front Cell Infect Microbiol 2022; 12:931072. [PMID: 35982784 PMCID: PMC9378789 DOI: 10.3389/fcimb.2022.931072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
MotivationThe understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.ResultsThis work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.Availability and implementationThe KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.
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Affiliation(s)
- Yang Fang
- Key Laboratory of Bio-Resources and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
- Department of Laboratory Medicine, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yi Yang
- Key Laboratory of Bio-Resources and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
- *Correspondence: Chengcheng Liu, ; Yi Yang,
| | - Chengcheng Liu
- State Key Laboratory of Oral Diseases, Department of Periodontics, National Clinical Research Center for Oral Diseases, West China School & Hospital of Stomatology, Sichuan University, Chengdu, China
- *Correspondence: Chengcheng Liu, ; Yi Yang,
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6
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Yang X, Yang S, Ren P, Wuchty S, Zhang Z. Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions. Front Microbiol 2022; 13:842976. [PMID: 35495666 PMCID: PMC9051481 DOI: 10.3389/fmicb.2022.842976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 03/25/2022] [Indexed: 11/13/2022] Open
Abstract
Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.
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Affiliation(s)
- Xiaodi Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Panyu Ren
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami, FL, United States
- Department of Biology, University of Miami, Miami, FL, United States
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
- *Correspondence: Ziding Zhang,
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Abstract
Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.
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8
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Chai H, Gu Q, Hughes J, Robertson DL. In silico prediction of HIV-1-host molecular interactions and their directionality. PLoS Comput Biol 2022; 18:e1009720. [PMID: 35134057 PMCID: PMC8856524 DOI: 10.1371/journal.pcbi.1009720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 02/18/2022] [Accepted: 12/03/2021] [Indexed: 11/18/2022] Open
Abstract
Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of the molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the ‘direction’ of the HIV-1-host molecular interactions is also predictable due to different characteristics of ‘forward’/pro-viral versus ‘backward’/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available at http://hivpre.cvr.gla.ac.uk/.
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Affiliation(s)
- Haiting Chai
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Quan Gu
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - David L. Robertson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- * E-mail:
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9
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Sudhakar P, Machiels K, Verstockt B, Korcsmaros T, Vermeire S. Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions. Front Microbiol 2021; 12:618856. [PMID: 34046017 PMCID: PMC8148342 DOI: 10.3389/fmicb.2021.618856] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 03/19/2021] [Indexed: 12/11/2022] Open
Abstract
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.
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Affiliation(s)
- Padhmanand Sudhakar
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Kathleen Machiels
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
| | - Bram Verstockt
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Séverine Vermeire
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
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Lian X, Yang X, Yang S, Zhang Z. Current status and future perspectives of computational studies on human-virus protein-protein interactions. Brief Bioinform 2021; 22:6161422. [PMID: 33693490 DOI: 10.1093/bib/bbab029] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 01/14/2021] [Accepted: 01/20/2021] [Indexed: 12/19/2022] Open
Abstract
The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.
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Affiliation(s)
- Xianyi Lian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xiaodi Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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11
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Lian X, Yang X, Shao J, Hou F, Yang S, Pan D, Zhang Z. Prediction and analysis of human-herpes simplex virus type 1 protein-protein interactions by integrating multiple methods. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0222-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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12
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Khorsand B, Savadi A, Naghibzadeh M. Comprehensive host-pathogen protein-protein interaction network analysis. BMC Bioinformatics 2020; 21:400. [PMID: 32912135 PMCID: PMC7488060 DOI: 10.1186/s12859-020-03706-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 07/31/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. RESULTS As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins' marker. CONCLUSIONS Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens' favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.
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Affiliation(s)
- Babak Khorsand
- Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Abdorreza Savadi
- Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
- Ferdowsi University of Mashhad, Azadi Square, Mashhad, 9177948974 Iran
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13
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Andrighetti T, Bohar B, Lemke N, Sudhakar P, Korcsmaros T. MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions. Cells 2020; 9:cells9051278. [PMID: 32455748 PMCID: PMC7291277 DOI: 10.3390/cells9051278] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 05/15/2020] [Accepted: 05/19/2020] [Indexed: 02/07/2023] Open
Abstract
Microbiome–host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe–host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions—as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn’s disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.
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Affiliation(s)
- Tahila Andrighetti
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK; (T.A.); (B.B.)
- Institute of Biosciences, São Paulo University (UNESP), Botucatu 18618-689, SP, Brazil;
| | - Balazs Bohar
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK; (T.A.); (B.B.)
- Department of Genetics, Eötvös Loránd University, Budapest 1117, Hungary
| | - Ney Lemke
- Institute of Biosciences, São Paulo University (UNESP), Botucatu 18618-689, SP, Brazil;
| | - Padhmanand Sudhakar
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK; (T.A.); (B.B.)
- Quadram Institute Bioscience, Norwich Research Park, Norwich NR4 7UQ, UK
- Department of Chronic Diseases, Metabolism and Ageing, KU Leuven BE-3000, Leuven, Belgium
- Correspondence: (T.K.); (P.S.)
| | - Tamas Korcsmaros
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK; (T.A.); (B.B.)
- Quadram Institute Bioscience, Norwich Research Park, Norwich NR4 7UQ, UK
- Correspondence: (T.K.); (P.S.)
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14
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Guven-Maiorov E, Hakouz A, Valjevac S, Keskin O, Tsai CJ, Gursoy A, Nussinov R. HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry. J Mol Biol 2020; 432:3395-3403. [PMID: 32061934 PMCID: PMC7261632 DOI: 10.1016/j.jmb.2020.01.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/28/2019] [Accepted: 01/14/2020] [Indexed: 02/07/2023]
Abstract
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.
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Affiliation(s)
- Emine Guven-Maiorov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - Asma Hakouz
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Sukejna Valjevac
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA; Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel.
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15
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Li J, Wang S, Chen Z, Wang Y. A Bipartite Network Module-Based Project to Predict Pathogen-Host Association. Front Genet 2020; 10:1357. [PMID: 32038713 PMCID: PMC6992693 DOI: 10.3389/fgene.2019.01357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/11/2019] [Indexed: 12/23/2022] Open
Abstract
Pathogen-host interactions play an important role in understanding the mechanism by which a pathogen can infect its host. Some approaches for predicting pathogen-host association have been developed, but prediction accuracy is still low. In this paper, we propose a bipartite network module-based approach to improve prediction accuracy. First, a bipartite network with pathogens and hosts is constructed. Next, pathogens and hosts are divided into different modules respectively. Then, modular information on the pathogens and hosts is added into a bipartite network projection model and the association scores between pathogens and hosts are calculated. Finally, leave-one-out cross-validation is used to estimate the performance of the proposed method. Experimental results show that the proposed method performs better in predicting pathogen-host association than other methods, and some potential pathogen-host associations with higher prediction scores are also confirmed by the results of biological experiments in the publically available literature.
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Affiliation(s)
- Jie Li
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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16
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Zheng N, Wang K, Zhan W, Deng L. Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches. Curr Drug Metab 2019; 20:177-184. [PMID: 30156155 DOI: 10.2174/1389200219666180829121038] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 05/21/2018] [Accepted: 08/02/2018] [Indexed: 01/15/2023]
Abstract
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.
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Affiliation(s)
- Nantao Zheng
- School of Software, Central South University, Changsha, 410075, China
| | - Kairou Wang
- School of Software, Central South University, Changsha, 410075, China
| | - Weihua Zhan
- School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo 315100, China
| | - Lei Deng
- School of Software, Central South University, Changsha, 410075, China.,Shanghai Key Lab of Intelligent Information Processing, Shanghai 200433, China
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17
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Lyon KF, Cai X, Young RJ, Mamun AA, Rajasekaran S, Schiller MR. Minimotif Miner 4: a million peptide minimotifs and counting. Nucleic Acids Res 2019; 46:D465-D470. [PMID: 29140456 PMCID: PMC5753208 DOI: 10.1093/nar/gkx1085] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 11/09/2017] [Indexed: 12/27/2022] Open
Abstract
Minimotif Miner (MnM) is a database and web system for analyzing short functional peptide motifs, termed minimotifs. We present an update to MnM growing the database from ∼300 000 to >1 000 000 minimotif consensus sequences and instances. This growth comes largely from updating data from existing databases and annotation of articles with high-throughput approaches analyzing different types of post-translational modifications. Another update is mapping human proteins and their minimotifs to know human variants from the dbSNP, build 150. Now MnM 4 can be used to generate mechanistic hypotheses about how human genetic variation affect minimotifs and outcomes. One example of the utility of the combined minimotif/SNP tool identifies a loss of function missense SNP in a ubiquitylation minimotif encoded in the excision repair cross-complementing 2 (ERCC2) nucleotide excision repair gene. This SNP reaches genome wide significance for many types of cancer and the variant identified with MnM 4 reveals a more detailed mechanistic hypothesis concerning the role of ERCC2 in cancer. Other updates to the web system include a new architecture with migration of the web system and database to Docker containers for better performance and management. Weblinks:minimotifminer.org and mnm.engr.uconn.edu
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Affiliation(s)
- Kenneth F Lyon
- Nevada Institute of Personalized Medicine and School of Life Sciences, University of Nevada, Las Vegas, 89154 4004 NV, USA
| | - Xingyu Cai
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 2155, USA
| | - Richard J Young
- Nevada Institute of Personalized Medicine and School of Life Sciences, University of Nevada, Las Vegas, 89154 4004 NV, USA
| | - Abdullah-Al Mamun
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 2155, USA
| | - Sanguthevar Rajasekaran
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 2155, USA
| | - Martin R Schiller
- Nevada Institute of Personalized Medicine and School of Life Sciences, University of Nevada, Las Vegas, 89154 4004 NV, USA
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18
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Saha S, Sengupta K, Chatterjee P, Basu S, Nasipuri M. Analysis of protein targets in pathogen-host interaction in infectious diseases: a case study on Plasmodium falciparum and Homo sapiens interaction network. Brief Funct Genomics 2019; 17:441-450. [PMID: 29028886 DOI: 10.1093/bfgp/elx024] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Infection and disease progression is the outcome of protein interactions between pathogen and host. Pathogen, the role player of Infection, is becoming a severe threat to life as because of its adaptability toward drugs and evolutionary dynamism in nature. Identifying protein targets by analyzing protein interactions between host and pathogen is the key point. Proteins with higher degree and possessing some topologically significant graph theoretical measures are found to be drug targets. On the other hand, exceptional nodes may be involved in infection mechanism because of some pathway process and biologically unknown factors. In this article, we attempt to investigate characteristics of host-pathogen protein interactions by presenting a comprehensive review of computational approaches applied on different infectious diseases. As an illustration, we have analyzed a case study on infectious disease malaria, with its causative agent Plasmodium falciparum acting as 'Bait' and host, Homo sapiens/human acting as 'Prey'. In this pathogen-host interaction network based on some interconnectivity and centrality properties, proteins are viewed as central, peripheral, hub and non-hub nodes and their significance on infection process. Besides, it is observed that because of sparseness of the pathogen and host interaction network, there may be some topologically unimportant but biologically significant proteins, which can also act as Bait/Prey. So, functional similarity or gene ontology mapping can help us in this case to identify these proteins.
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Affiliation(s)
- Sovan Saha
- Department of Computer Science and Engineering at Dr Sudhir Chandra Sur Degree Engineering College, India
| | - Kaustav Sengupta
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Piyali Chatterjee
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia, India
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, India
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19
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Lian X, Yang S, Li H, Fu C, Zhang Z. Machine-Learning-Based Predictor of Human–Bacteria Protein–Protein Interactions by Incorporating Comprehensive Host-Network Properties. J Proteome Res 2019; 18:2195-2205. [DOI: 10.1021/acs.jproteome.9b00074] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Xianyi Lian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Shiping Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Hong Li
- Key Laboratory of Tropical Biological Resources of Ministry of Education, Hainan University, Haikou, 570228, China
| | - Chen Fu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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20
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Guven-Maiorov E, Tsai CJ, Ma B, Nussinov R. Interface-Based Structural Prediction of Novel Host-Pathogen Interactions. Methods Mol Biol 2019; 1851:317-335. [PMID: 30298406 PMCID: PMC8192064 DOI: 10.1007/978-1-4939-8736-8_18] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
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.
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Affiliation(s)
- Emine Guven-Maiorov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Buyong Ma
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA.
- Department of Human Genetics and Molecular Medicine, Sackler Inst. of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
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21
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Soyemi J, Isewon I, Oyelade J, Adebiyi E. Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed. Curr Bioinform 2018; 13:396-406. [PMID: 31496926 PMCID: PMC6691774 DOI: 10.2174/1574893613666180108155851] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 12/31/2017] [Accepted: 01/02/2018] [Indexed: 01/01/2023]
Abstract
BACKGROUND Host-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases. OBJECTIVE Many studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose. METHODS The various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed. RESULT We discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information. CONCLUSION This review presents useful knowledge and future directions on the subject matter.
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Affiliation(s)
- Jumoke Soyemi
- Department of Computer Science, The Federal Polytechnic, Ilaro, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Ota, Nigeria
| | - Itunnuoluwa Isewon
- Department of Computer & Information Sciences, Covenant University, Ota, Nigeria and
- Covenant University Bioinformatics Research (CUBRe), Ota, Nigeria
| | - Jelili Oyelade
- Department of Computer & Information Sciences, Covenant University, Ota, Nigeria and
- Covenant University Bioinformatics Research (CUBRe), Ota, Nigeria
| | - Ezekiel Adebiyi
- Department of Computer & Information Sciences, Covenant University, Ota, Nigeria and
- Covenant University Bioinformatics Research (CUBRe), Ota, Nigeria
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22
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García-Pérez CA, Guo X, Navarro JG, Aguilar DAG, Lara-Ramírez EE. Proteome-wide analysis of human motif-domain interactions mapped on influenza a virus. BMC Bioinformatics 2018; 19:238. [PMID: 29940841 PMCID: PMC6019528 DOI: 10.1186/s12859-018-2237-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 06/07/2018] [Indexed: 01/27/2023] Open
Abstract
Background The influenza A virus (IAV) is a constant threat for humans worldwide. The understanding of motif-domain protein participation is essential to combat the pathogen. Results In this study, a data mining approach was employed to extract influenza-human Protein-Protein interactions (PPI) from VirusMentha,Virus MINT, IntAct, and Pfam databases, to mine motif-domain interactions (MDIs) stored as Regular Expressions (RegExp) in 3DID database. A total of 107 RegExp related to human MDIs were searched on 51,242 protein fragments from H1N1, H1N2, H2N2, H3N2 and H5N1 strains obtained from Virus Variation database. A total 46 MDIs were frequently mapped on the IAV proteins and shared between the different strains. IAV kept host-like MDIs that were associated with the virus survival, which could be related to essential biological process such as microtubule-based processes, regulation of cell cycle check point, regulation of replication and transcription of DNA, etc. in human cells. The amino acid motifs were searched for matches in the immune epitope database and it was found that some motifs are part of experimentally determined epitopes on IAV, implying that such interactions exist. Conclusion The directed data-mining method employed could be used to identify functional motifs in other viruses for envisioning new therapies. Electronic supplementary material The online version of this article (10.1186/s12859-018-2237-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Carlos A García-Pérez
- Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, Mexico
| | - Xianwu Guo
- Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, Mexico
| | | | | | - Edgar E Lara-Ramírez
- Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Interior Alameda # 45, Colonia Centro, CP. 98000, Zacatecas, Zac, Mexico.
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23
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Abstract
Pathogen-host interactions (PHIs) underlie the process of infection. The systems biology view of the whole PHI system is superior to the investigation of the pathogen or host separately in understanding the infection mechanisms. Especially, the identification of host-oriented drug targets for the next-generation anti-infection therapeutics requires the properties of the host factors targeted by pathogens. Here, we provide an outline of computational analysis of PHI networks, focusing on the properties of the pathogen-targeted host proteins. We also provide information about the available PHI data and the related Web-based resources.
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Affiliation(s)
- Müberra Fatma Cesur
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Saliha Durmuş
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
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24
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Guven-Maiorov E, Tsai CJ, Ma B, Nussinov R. Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer. J Mol Biol 2017; 429:3925-3941. [PMID: 29106933 PMCID: PMC7906438 DOI: 10.1016/j.jmb.2017.10.023] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/16/2017] [Accepted: 10/16/2017] [Indexed: 02/07/2023]
Abstract
There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.
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Affiliation(s)
- Emine Guven-Maiorov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Buyong Ma
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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25
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Yang S, Li H, He H, Zhou Y, Zhang Z. Critical assessment and performance improvement of plant–pathogen protein–protein interaction prediction methods. Brief Bioinform 2017; 20:274-287. [DOI: 10.1093/bib/bbx123] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Indexed: 01/15/2023] Open
Affiliation(s)
- Shiping Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University
| | - Hong Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University
| | - Huaqin He
- College of Life Sciences, Fujian Agriculture and Forestry University
| | - Yuan Zhou
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University
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26
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Zanzoni A, Spinelli L, Braham S, Brun C. Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins. MICROBIOME 2017; 5:89. [PMID: 28793925 PMCID: PMC5551000 DOI: 10.1186/s40168-017-0307-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 07/13/2017] [Indexed: 05/10/2023]
Abstract
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.
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Affiliation(s)
- Andreas Zanzoni
- Aix-Marseille Université, Inserm, TAGC UMR_S1090, Marseille, France.
| | - Lionel Spinelli
- Aix-Marseille Université, Inserm, TAGC UMR_S1090, Marseille, France
| | - Shérazade Braham
- Aix-Marseille Université, Inserm, TAGC UMR_S1090, Marseille, France
| | - Christine Brun
- Aix-Marseille Université, Inserm, TAGC UMR_S1090, Marseille, France
- CNRS, Marseille, France
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27
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Mariano R, Wuchty S. Structure-based prediction of host–pathogen protein interactions. Curr Opin Struct Biol 2017; 44:119-124. [DOI: 10.1016/j.sbi.2017.02.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 02/28/2017] [Indexed: 11/25/2022]
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28
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Bose T, Venkatesh KV, Mande SS. Computational Analysis of Host-Pathogen Protein Interactions between Humans and Different Strains of Enterohemorrhagic Escherichia coli. Front Cell Infect Microbiol 2017; 7:128. [PMID: 28469995 PMCID: PMC5395655 DOI: 10.3389/fcimb.2017.00128] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 03/28/2017] [Indexed: 01/18/2023] Open
Abstract
Serotype O157:H7, an enterohemorrhagic Escherichia coli (EHEC), is known to cause gastrointestinal and systemic illnesses ranging from diarrhea and hemorrhagic colitis to potentially fatal hemolytic uremic syndrome. Specific genetic factors like ompA, nsrR, and LEE genes are known to play roles in EHEC pathogenesis. However, these factors are not specific to EHEC and their presence in several non-pathogenic strains indicates that additional factors are involved in pathogenicity. We propose a comprehensive effort to screen for such potential genetic elements, through investigation of biomolecular interactions between E. coli and their host. In this work, an in silico investigation of the protein–protein interactions (PPIs) between human cells and four EHEC strains (viz., EDL933, Sakai, EC4115, and TW14359) was performed in order to understand the virulence and host-colonization strategies of these strains. Potential host–pathogen interactions (HPIs) between human cells and the “non-pathogenic” E. coli strain MG1655 were also probed to evaluate whether and how the variations in the genomes could translate into altered virulence and host-colonization capabilities of the studied bacterial strains. Results indicate that a small subset of HPIs are unique to the studied pathogens and can be implicated in virulence. This subset of interactions involved E. coli proteins like YhdW, ChuT, EivG, and HlyA. These proteins have previously been reported to be involved in bacterial virulence. In addition, clear differences in lineage and clade-specific HPI profiles could be identified. Furthermore, available gene expression profiles of the HPI-proteins were utilized to estimate the proportion of proteins which may be involved in interactions. We hypothesized that a cumulative score of the ratios of bound:unbound proteins (involved in HPIs) would indicate the extent of colonization. Thus, we designed the Host Colonization Index (HCI) measure to determine the host colonization potential of the E. coli strains. Pathogenic strains of E. coli were observed to have higher HCIs as compared to a non-pathogenic laboratory strain. However, no significant differences among the HCIs of the two pathogenic groups were observed. Overall, our findings are expected to provide additional insights into EHEC pathogenesis and are likely to aid in designing alternate preventive and therapeutic strategies.
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Affiliation(s)
- Tungadri Bose
- Bio-Sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services LimitedPune, India.,Department of Chemical Engineering, Indian Institute of Technology BombayMumbai, India
| | - K V Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology BombayMumbai, India
| | - Sharmila S Mande
- Bio-Sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services LimitedPune, India
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29
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Becerra A, Bucheli VA, Moreno PA. Prediction of virus-host protein-protein interactions mediated by short linear motifs. BMC Bioinformatics 2017; 18:163. [PMID: 28279163 PMCID: PMC5345135 DOI: 10.1186/s12859-017-1570-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 02/24/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). RESULTS We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. CONCLUSION We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.
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Affiliation(s)
- Andrés Becerra
- Escuela de ingeniería de sistemas y computación, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia
| | - Victor A Bucheli
- Escuela de ingeniería de sistemas y computación, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia
| | - Pedro A Moreno
- Escuela de ingeniería de sistemas y computación, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia.
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Zhang A, He L, Wang Y. Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions. BMC Bioinformatics 2017; 18:145. [PMID: 28253857 PMCID: PMC5335770 DOI: 10.1186/s12859-017-1500-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Accepted: 01/27/2017] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Grass carp hemorrhagic disease, caused by grass carp reovirus (GCRV), is the most fatal causative agent in grass carp aquaculture. Protein-protein interactions between virus and host are one avenue through which GCRV can trigger infection and induce disease. Experimental approaches for the detection of host-virus interactome have many inherent limitations, and studies on protein-protein interactions between GCRV and its host remain rare. RESULTS In this study, based on known motif-domain interaction information, we systematically predicted the GCRV virus-host protein interactome by using motif-domain interaction pair searching strategy. These proteins derived from different domain families and were predicted to interact with different motif patterns in GCRV. JAM-A protein was successfully predicted to interact with motifs of GCRV Sigma1-like protein, and shared the similar binding mode compared with orthoreovirus. Differentially expressed genes during GCRV infection process were extracted and mapped to our predicted interactome, the overlapped genes displayed different tissue expression distributions on the whole, the overall expression level in intestinal is higher than that of other three tissues, which may suggest that the functions of these genes are more active in intestinal. Function annotation and pathway enrichment analysis revealed that the host targets were largely involved in signaling pathway and immune pathway, such as interferon-gamma signaling pathway, VEGF signaling pathway, EGF receptor signaling pathway, B cell activation, and T cell activation. CONCLUSIONS Although the predicted PPIs may contain some false positives due to limited data resource and poor research background in non-model species, the computational method still provide reasonable amount of interactions, which can be further validated by high throughput experiments. The findings of this work will contribute to the development of system biology for GCRV infectious diseases, and help guide the identification of novel receptors of GCRV in its host.
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Affiliation(s)
- Aidi Zhang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
| | - Libo He
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
| | - Yaping Wang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
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31
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Chiang AWT, Wu WYL, Wang T, Hwang MJ. Identification of Entry Factors Involved in Hepatitis C Virus Infection Based on Host-Mimicking Short Linear Motifs. PLoS Comput Biol 2017; 13:e1005368. [PMID: 28129350 PMCID: PMC5302801 DOI: 10.1371/journal.pcbi.1005368] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 02/10/2017] [Accepted: 01/17/2017] [Indexed: 12/15/2022] Open
Abstract
Host factors that facilitate viral entry into cells can, in principle, be identified from a virus-host protein interaction network, but for most viruses information for such a network is limited. To help fill this void, we developed a bioinformatics approach and applied it to hepatitis C virus (HCV) infection, which is a current concern for global health. Using this approach, we identified short linear sequence motifs, conserved in the envelope proteins of HCV (E1/E2), that potentially can bind human proteins present on the surface of hepatocytes so as to construct an HCV (envelope)-host protein interaction network. Gene Ontology functional and KEGG pathway analyses showed that the identified host proteins are enriched in cell entry and carcinogenesis functionalities. The validity of our results is supported by much published experimental data. Our general approach should be useful when developing antiviral agents, particularly those that target virus-host interactions. Viruses recruit host proteins, called entry factors, to help gain entry to host cells. Identification of entry factors can provide targets for developing antiviral drugs. By exploring the concept that short linear peptide motifs involved in human protein-protein interactions may be mimicked by viruses to hijack certain host cellular processes and thereby assist viral infection/survival, we developed a bioinformatics strategy to computationally identify entry factors of hepatitis C virus (HCV) infection, which is a worldwide health problem. Analysis of cellular functions and biochemical pathways indicated that the human proteins we identified usually play a role in cell entry and/or carcinogenesis, and results of the analysis are generally supported by experimental studies on HCV infection, including the ~80% (15 of 19) prediction rate of known HCV hepatocyte entry factors. Because molecular mimicry is a general concept, our bioinformatics strategy is a timely approach to identify new targets for antiviral research, not only for HCV but also for other viruses.
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Affiliation(s)
| | - Walt Y. L. Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ting Wang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ming-Jing Hwang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- * E-mail:
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32
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Saha D, Podder S, Ghosh TC. Overlapping Regions in HIV-1 Genome Act as Potential Sites for Host-Virus Interaction. Front Microbiol 2016; 7:1735. [PMID: 27867372 PMCID: PMC5095123 DOI: 10.3389/fmicb.2016.01735] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 10/17/2016] [Indexed: 01/05/2023] Open
Abstract
More than a decade, overlapping genes in RNA viruses became a subject of research which has explored various effect of gene overlapping on the evolution and function of viral genomes like genome size compaction. Additionally, overlapping regions (OVRs) are also reported to encode elevated degree of protein intrinsic disorder (PID) in unspliced RNA viruses. With the aim to explore the roles of OVRs in HIV-1 pathogenesis, we have carried out an in-depth analysis on the association of gene overlapping with PID in 35 HIV1- M subtypes. Our study reveals an over representation of PID in OVR of HIV-1 genomes. These disordered residues endure several vital, structural features like short linear motifs (SLiMs) and protein phosphorylation (PP) sites which are previously shown to be involved in massive host–virus interaction. Moreover, SLiMs in OVRs are noticed to be more functionally potential as compared to that of non-overlapping region. Although, density of experimentally verified SLiMs, resided in 9 HIV-1 genes, involved in host–virus interaction do not show any bias toward clustering into OVR, tat and rev two important proteins mediates host–pathogen interaction by their experimentally verified SLiMs, which are mostly localized in OVR. Finally, our analysis suggests that the acquisition of SLiMs in OVR is mutually exclusive of the occurrence of disordered residues, while the enrichment of PPs in OVR is solely dependent on PID and not on overlapping coding frames. Thus, OVRs of HIV-1 genomes could be demarcated as potential molecular recognition sites during host–virus interaction.
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Affiliation(s)
- Deeya Saha
- Bioinformatics Centre, Bose Institute Kolkata, India
| | - Soumita Podder
- Department of Microbiology, Raiganj University Raiganj, India
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33
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Sen R, Nayak L, De RK. A review on host-pathogen interactions: classification and prediction. Eur J Clin Microbiol Infect Dis 2016; 35:1581-99. [PMID: 27470504 DOI: 10.1007/s10096-016-2716-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 06/22/2016] [Indexed: 01/01/2023]
Abstract
The research on host-pathogen interactions is an ever-emerging and evolving field. Every other day a new pathogen gets discovered, along with comes the challenge of its prevention and cure. As the intelligent human always vies for prevention, which is better than cure, understanding the mechanisms of host-pathogen interactions gets prior importance. There are many mechanisms involved from the pathogen as well as the host sides while an interaction happens. It is a vis-a-vis fight of the counter genes and proteins from both sides. Who wins depends on whether a host gets an infection or not. Moreover, a higher level of complexity arises when the pathogens evolve and become resistant to a host's defense mechanisms. Such pathogens pose serious challenges for treatment. The entire human population is in danger of such long-lasting persistent infections. Some of these infections even increase the rate of mortality. Hence there is an immediate emergency to understand how the pathogens interact with their host for successful invasion. It may lead to discovery of appropriate preventive measures, and the development of rational therapeutic measures and medication against such infections and diseases. This review, a state-of-the-art updated scenario of host-pathogen interaction research, has been done by keeping in mind this urgency. It covers the biological and computational aspects of host-pathogen interactions, classification of the methods by which the pathogens interact with their hosts, different machine learning techniques for prediction of host-pathogen interactions, and future scopes of this research field.
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Affiliation(s)
- R Sen
- Machine Intelligence Unit, Indian Statistical Institute, 203, Barrackpore Trunk Road, Kolkata, 700108, India
| | - L Nayak
- Machine Intelligence Unit, Indian Statistical Institute, 203, Barrackpore Trunk Road, Kolkata, 700108, India
| | - R K De
- Machine Intelligence Unit, Indian Statistical Institute, 203, Barrackpore Trunk Road, Kolkata, 700108, India.
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Abstract
The limitations of high-throughput genomic methods used for studying virus-host interactions make it difficult to directly obtain insights on virus pathogenesis. In this chapter, the central steps of a protein structure similarity based computational approach used to predict the host interactors of Chikungunya virus are explained by highlighting the important aspects that need to be considered. Identification of such conserved set of putative interactions that allow the virus to take control of the host has the potential to deepen our understanding of the virus-specific remodeling processes of the host cell and illuminate new arenas of disease intervention.
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35
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Eid FE, ElHefnawi M, Heath LS. DeNovo: virus-host sequence-based protein–protein interaction prediction. Bioinformatics 2015; 32:1144-50. [DOI: 10.1093/bioinformatics/btv737] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 12/12/2015] [Indexed: 01/02/2023] Open
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36
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Interactions of HIV-1 proteins as targets for developing anti-HIV-1 peptides. Future Med Chem 2015; 7:1055-77. [DOI: 10.4155/fmc.15.46] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Protein–protein interactions (PPI) are essential in every step of the HIV replication cycle. Mapping the interactions between viral and host proteins is a fundamental target for the design and development of new therapeutics. In this review, we focus on rational development of anti-HIV-1 peptides based on mapping viral–host and viral–viral protein interactions all across the HIV-1 replication cycle. We also discuss the mechanism of action, specificity and stability of these peptides, which are designed to inhibit PPI. Some of these peptides are excellent tools to study the mechanisms of PPI in HIV-1 replication cycle and for the development of anti-HIV-1 drug leads that modulate PPI.
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37
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Asnet Mary J, Paramasivan R, Shenbagarathai R. Identification of sequence motifs involved in Dengue virus-host interactions. J Biomol Struct Dyn 2015; 34:676-87. [PMID: 25905427 DOI: 10.1080/07391102.2015.1042914] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Dengue fever is a rapidly spreading mosquito-borne virus infection, which remains a serious global public health problem. As there is no specific treatment or commercial vaccine available for effective control of the disease, the attempts on developing novel control strategies are underway. Viruses utilize the surface receptor proteins of host to enter into the cells. Though various proteins were said to be receptors of Dengue virus (DENV) using Virus Overlay Protein Binding Assay, the precise interaction between DENV and host is not explored. Understanding the structural features of domain III envelope glycoprotein would help in developing efficient antiviral inhibitors. Therefore, an attempt was made to identify the sequence motifs present in domain III envelope glycoprotein of Dengue virus. Computational analysis revealed that the NGR motif is present in the domain III envelope glycoprotein of DENV-1 and DENV-3. Similarly, DENV-1, DENV-2 and DENV-4 were found to contain Yxxphi motif which is a tyrosine-based sorting signal responsible for the interaction with a mu subunit of adaptor protein complex. High-throughput virtual screening resulted in five compounds as lead molecules based on glide score, which ranges from -4.664 to -6.52 kcal/Mol. This computational prediction provides an additional tool for understanding the virus-host interactions and helps to identify potential targets in the host. Further, experimental evidence is warranted to confirm the virus-host interactions and also inhibitory activity of reported lead compounds.
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Affiliation(s)
- J Asnet Mary
- a Department of Zoology , Fatima College , Madurai , Tamil Nadu 625018 , India.,b PG & Research Department of Zoology and Biotechnology , Lady Doak College , Madurai , Tamil Nadu 625002 , India
| | - R Paramasivan
- c Centre for Research in Medical Entomology , Madurai , India
| | - R Shenbagarathai
- b PG & Research Department of Zoology and Biotechnology , Lady Doak College , Madurai , Tamil Nadu 625002 , India
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38
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The Road Less Traveled: HIV's Use of Alternative Routes through Cellular Pathways. J Virol 2015; 89:5204-12. [PMID: 25762730 DOI: 10.1128/jvi.03684-14] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Pathogens such as HIV-1, with their minimalist genomes, must navigate cellular networks and rely on hijacking and manipulating the host machinery for successful replication. Limited overlap of host factors identified as vital for pathogen replication may be explained by considering that pathogens target, rather than specific cellular factors, crucial cellular pathways by targeting different, functionally equivalent, protein-protein interactions within that pathway. The ability to utilize alternative routes through cellular pathways may be essential for pathogen survival when restricted and provide flexibility depending on the viral replication stage and the environment in the infected host. In this minireview, we evaluate evidence supporting this notion, discuss specific HIV-1 examples, and consider the molecular mechanisms which allow pathogens to flexibly exploit different routes.
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39
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Nourani E, Khunjush F, Durmuş S. Computational approaches for prediction of pathogen-host protein-protein interactions. Front Microbiol 2015; 6:94. [PMID: 25759684 PMCID: PMC4338785 DOI: 10.3389/fmicb.2015.00094] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Accepted: 01/26/2015] [Indexed: 12/25/2022] Open
Abstract
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.
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Affiliation(s)
- Esmaeil Nourani
- Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University Shiraz, Iran
| | - Farshad Khunjush
- Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University Shiraz, Iran ; School of Computer Science, Institute for Research in Fundamental Sciences (IPM) Tehran, Iran
| | - Saliha Durmuş
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University Kocaeli, Turkey
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Subramanian N, Torabi-Parizi P, Gottschalk RA, Germain RN, Dutta B. Network representations of immune system complexity. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:13-38. [PMID: 25625853 PMCID: PMC4339634 DOI: 10.1002/wsbm.1288] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2014] [Revised: 12/09/2014] [Accepted: 12/11/2014] [Indexed: 12/25/2022]
Abstract
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:
Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Macromolecular Interactions, Methods
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Affiliation(s)
- Naeha Subramanian
- Institute for Systems Biology, Seattle, WA, USA; Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
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Bandyopadhyay S, Ray S, Mukhopadhyay A, Maulik U. A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions. Brief Bioinform 2014; 16:830-51. [PMID: 25479794 DOI: 10.1093/bib/bbu041] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2014] [Indexed: 12/19/2022] Open
Abstract
The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community.
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Mulder NJ, Akinola RO, Mazandu GK, Rapanoel H. Using biological networks to improve our understanding of infectious diseases. Comput Struct Biotechnol J 2014; 11:1-10. [PMID: 25379138 PMCID: PMC4212278 DOI: 10.1016/j.csbj.2014.08.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.
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Affiliation(s)
- Nicola J Mulder
- Computational Biology Group, Department of Clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, South Africa
| | - Richard O Akinola
- Computational Biology Group, Department of Clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, South Africa
| | - Gaston K Mazandu
- Computational Biology Group, Department of Clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, South Africa
| | - Holifidy Rapanoel
- Computational Biology Group, Department of Clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, South Africa
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Zhou H, Gao S, Nguyen NN, Fan M, Jin J, Liu B, Zhao L, Xiong G, Tan M, Li S, Wong L. Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions. Biol Direct 2014; 9:5. [PMID: 24708540 PMCID: PMC4022245 DOI: 10.1186/1745-6150-9-5] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Accepted: 03/26/2014] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs. RESULTS We develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both host and pathogen proteins involved in host-pathogen PPIs tend to have lower charge, and tend to be more hydrophilic. CONCLUSIONS Our stringent homology-based prediction approach provides a better strategy in predicting PPIs between eukaryotic hosts and prokaryotic pathogens than a conventional homology-based approach. The properties we have observed from the predicted H. sapiens-M. tuberculosis H37Rv PPI network are useful for understanding inter-species host-pathogen PPI networks and provide novel insights for host-pathogen interaction studies.
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Affiliation(s)
- Hufeng Zhou
- NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, Singapore, Singapore
- School of Computing, National University of Singapore, Singapore, Singapore
- Department of Medicine, Brigham and Women’s Hospital, Boston, USA
- Department of Microbiology and Immunobiology, Harvard University, Cambridge, USA
| | - Shangzhi Gao
- Department of Environmental Health, Harvard School of Public Health, Harvard University, Cambridge, USA
| | - Nam Ninh Nguyen
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Mengyuan Fan
- NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, Singapore, Singapore
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Jingjing Jin
- School of Computing, National University of Singapore, Singapore, Singapore
| | - Bing Liu
- Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Liang Zhao
- Bioinformatics Research Center, & School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
| | - Geng Xiong
- Department of Medicine, Brigham and Women’s Hospital, Boston, USA
| | - Min Tan
- Department of Medicine, Brigham and Women’s Hospital, Boston, USA
- Department of Microbiology and Immunobiology, Harvard University, Cambridge, USA
| | - Shijun Li
- Department of Medicine, Brigham and Women’s Hospital, Boston, USA
- Department of Microbiology and Immunobiology, Harvard University, Cambridge, USA
| | - Limsoon Wong
- School of Computing, National University of Singapore, Singapore, Singapore
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Emamjomeh A, Goliaei B, Zahiri J, Ebrahimpour R. Predicting protein–protein interactions between human and hepatitis C virus via an ensemble learning method. ACTA ACUST UNITED AC 2014; 10:3147-54. [DOI: 10.1039/c4mb00410h] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
We developed a novel method to predict human–HCV protein–protein interactions, the most comprehensive study of this type.
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Affiliation(s)
- Abbasali Emamjomeh
- Institute of Biochemistry and Biophysics (IBB)
- University of Tehran
- Tehran, Iran
| | - Bahram Goliaei
- Institute of Biochemistry and Biophysics (IBB)
- University of Tehran
- Tehran, Iran
| | - Javad Zahiri
- Institute of Biochemistry and Biophysics (IBB)
- University of Tehran
- Tehran, Iran
- Department of Mathematics
- K.N. Toosi University of Technology
| | - Reza Ebrahimpour
- Brain and Intelligent Systems Research Lab
- Department of Electrical and Computer Engineering
- Shahid Rajaee Teacher Training University
- Tehran, Iran
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Viral channel proteins in intracellular protein-protein communication: Vpu of HIV-1, E5 of HPV16 and p7 of HCV. BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2013; 1838:1113-21. [PMID: 24035804 DOI: 10.1016/j.bbamem.2013.08.017] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2013] [Revised: 08/06/2013] [Accepted: 08/09/2013] [Indexed: 01/20/2023]
Abstract
Viral channel forming proteins are known for their capability to make the lipid membrane of the host cell and its subcellular compartments permeable to ions and small compounds. There is increasing evidence that some of the representatives of this class of proteins are also strongly interacting with host proteins and the effectiveness of this interaction seems to be high. Interaction of viral channel proteins with host factors has been proposed by bioinformatics approaches and has also been identified experimentally. An overview of the interactions with host proteins is given for Vpu from HIV-1, E5 from HPV-16 and p7 from HCV. This article is part of a Special Issue entitled: Viral Membrane Proteins - Channels for Cellular Networking.
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Segura-Cabrera A, García-Pérez CA, Guo X, Rodríguez-Pérez MA. A viral-human interactome based on structural motif-domain interactions captures the human infectome. PLoS One 2013; 8:e71526. [PMID: 23951184 PMCID: PMC3738538 DOI: 10.1371/journal.pone.0071526] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Accepted: 06/28/2013] [Indexed: 11/23/2022] Open
Abstract
Protein interactions between a pathogen and its host are fundamental in the establishment of the pathogen and underline the infection mechanism. In the present work, we developed a single predictive model for building a host-viral interactome based on the identification of structural descriptors from motif-domain interactions of protein complexes deposited in the Protein Data Bank (PDB). The structural descriptors were used for searching, in a database of protein sequences of human and five clinically important viruses; therefore, viral and human proteins sharing a descriptor were predicted as interacting proteins. The analysis of the host-viral interactome allowed to identify a set of new interactions that further explain molecular mechanism associated with viral infections and showed that it was able to capture human proteins already associated to viral infections (human infectome) and non-infectious diseases (human diseasome). The analysis of human proteins targeted by viral proteins in the context of a human interactome showed that their neighbors are enriched in proteins reported with differential expression under infection and disease conditions. It is expected that the findings of this work will contribute to the development of systems biology for infectious diseases, and help guide the rational identification and prioritization of novel drug targets.
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Affiliation(s)
- Aldo Segura-Cabrera
- Laboratorio de Bioinformática, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, México.
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Rana J, Sreejith R, Gulati S, Bharti I, Jain S, Gupta S. Deciphering the host-pathogen protein interface in chikungunya virus-mediated sickness. Arch Virol 2013; 158:1159-72. [PMID: 23334837 DOI: 10.1007/s00705-013-1602-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2012] [Accepted: 12/02/2012] [Indexed: 12/20/2022]
Abstract
Successful infection with chikungunya virus (CHIKV) depends largely on the ability of this virus to manipulate cellular processes in its favour through specific interactions with several host factors. The knowledge of virus-host interactions is of particular value for understanding the interface through which therapeutic strategies could be applied. In the current study, the authors have employed a computational method to study the protein interactions between CHIKV and both its human host and its mosquito vector. In this structure-based study, 2028 human and 86 mosquito proteins were predicted to interact with those of CHIKV through 3918 and 112 unique interactions, respectively. This approach could predict 40 % of the experimentally confirmed CHIKV-host interactions along with several novel interactions, suggesting the involvement of CHIKV in intracellular cell signaling, programmed cell death, and transcriptional and translational regulation. The data corresponded to those obtained in earlier studies for HIV and dengue viruses using the same methodology. This study provides a conservative set of potential interactions that can be employed for future experimental studies with a view to understanding CHIKV biology.
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Affiliation(s)
- Jyoti Rana
- Department of Biotechnology, Center for Emerging Diseases, Jaypee Institute of Information Technology, A-10, Sector 62, Noida, 201 307 Uttar Pradesh, India
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Barh D, Gupta K, Jain N, Khatri G, León-Sicairos N, Canizalez-Roman A, Tiwari S, Verma A, Rahangdale S, Shah Hassan S, Rodrigues dos Santos A, Ali A, Carlos Guimarães L, Thiago Jucá Ramos R, Devarapalli P, Barve N, Bakhtiar M, Kumavath R, Ghosh P, Miyoshi A, Silva A, Kumar A, Narayan Misra A, Blum K, Baumbach J, Azevedo V. Conserved host–pathogen PPIs Globally conserved inter-species bacterial PPIs based conserved host-pathogen interactome derived novel target inC. pseudotuberculosis,C. diphtheriae,M. tuberculosis,C. ulcerans,Y. pestis, andE. colitargeted byPiper betelcompounds. Integr Biol (Camb) 2013; 5:495-509. [DOI: 10.1039/c2ib20206a] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Debmalya Barh
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
- Department of Biosciences and Biotechnology, School of Biotechnology, Fakir Mohan University, Jnan Bigyan Vihar, Balasore, Orissa, India
| | - Krishnakant Gupta
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
- School of Biotechnology, Devi Ahilya University, Khandwa Road Campus, Indore, MP, India
| | - Neha Jain
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
| | - Gourav Khatri
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
- School of Biotechnology, Devi Ahilya University, Khandwa Road Campus, Indore, MP, India
| | - Nidia León-Sicairos
- Unidad de investigacion, Facultad de Medicina, Universidad Autónoma de Sinaloa. Cedros y Sauces, Fraccionamiento Fresnos, Culiacán Sinaloa 80246, México
| | - Adrian Canizalez-Roman
- Unidad de investigacion, Facultad de Medicina, Universidad Autónoma de Sinaloa. Cedros y Sauces, Fraccionamiento Fresnos, Culiacán Sinaloa 80246, México
| | - Sandeep Tiwari
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
| | - Ankit Verma
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
- School of Biotechnology, Devi Ahilya University, Khandwa Road Campus, Indore, MP, India
| | - Sachin Rahangdale
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
- School of Biotechnology, Devi Ahilya University, Khandwa Road Campus, Indore, MP, India
| | - Syed Shah Hassan
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | | | - Amjad Ali
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Luis Carlos Guimarães
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | | | - Pratap Devarapalli
- Department of Genomic Science, School of Biological Sciences, Riverside Transit Campus, Central University of Kerala, Kasaragod, India
| | - Neha Barve
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
- School of Biotechnology, Devi Ahilya University, Khandwa Road Campus, Indore, MP, India
| | - Marriam Bakhtiar
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Ranjith Kumavath
- Department of Genomic Science, School of Biological Sciences, Riverside Transit Campus, Central University of Kerala, Kasaragod, India
| | - Preetam Ghosh
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
- Department of Computer Science and Center for the Study of Biological Complexity, Virginia Commonwealth University, 401 West Main Street, Room E4234, P.O. Box 843019, Richmond, Virginia 23284-3019, USA
| | - Anderson Miyoshi
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Artur Silva
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, PA, Brazil
| | - Anil Kumar
- School of Biotechnology, Devi Ahilya University, Khandwa Road Campus, Indore, MP, India
| | - Amarendra Narayan Misra
- Department of Biosciences and Biotechnology, School of Biotechnology, Fakir Mohan University, Jnan Bigyan Vihar, Balasore, Orissa, India
- Center for Life Sciences, School of Natural Sciences, Central University of Jharkhand, Ranchi, Jharkhand State, India
| | - Kenneth Blum
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal-721172, India. Fax: +91-944 955 0032; Tel: +91-944 955 0032
- University of Florida, College of Medicine, Gainesville, Florida, USA
- Global Integrated Services Unit University of Vermont Center for Clinical & Translational Science, College of Medicine, Burlington, VT, USA
- Dominion Diagnostics LLC, North Kingstown, Rhode Island, USA
| | - Jan Baumbach
- Computational Biology Group Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
| | - Vasco Azevedo
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
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Durmuş Tekir SD, Ülgen KÖ. Systems biology of pathogen-host interaction: networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era. Biotechnol J 2013; 8:85-96. [PMID: 23193100 PMCID: PMC7161785 DOI: 10.1002/biot.201200110] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Revised: 09/17/2012] [Accepted: 10/11/2012] [Indexed: 12/13/2022]
Abstract
Infectious diseases comprise some of the leading causes of death and disability worldwide. Interactions between pathogen and host proteins underlie the process of infection. Improved understanding of pathogen-host molecular interactions will increase our knowledge of the mechanisms involved in infection, and allow novel therapeutic solutions to be devised. Complete genome sequences for a number of pathogenic microorganisms, as well as the human host, has led to the revelation of their protein-protein interaction (PPI) networks. In this post-genomic era, pathogen-host interactions (PHIs) operating during infection can also be mapped. Detailed systematic analyses of PPI and PHI data together are required for a complete understanding of pathogenesis of infections. Here we review the striking results recently obtained during the construction and investigation of these networks. Emphasis is placed on studies producing large-scale interaction data by high-throughput experimental techniques.
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Affiliation(s)
| | - Kutlu Ö. Ülgen
- Department of Chemical Engineering, Boǧaziçi University, Istanbul, Turkey
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