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Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [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] [Indexed: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
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Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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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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Tayal S, Bhatnagar S. Role of molecular mimicry in the SARS-CoV-2-human interactome for pathogenesis of cardiovascular diseases: An update to ImitateDB. Comput Biol Chem 2023; 106:107919. [PMID: 37463554 DOI: 10.1016/j.compbiolchem.2023.107919] [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: 01/15/2023] [Revised: 06/13/2023] [Accepted: 07/06/2023] [Indexed: 07/20/2023]
Abstract
Mimicry of host proteins is a strategy employed by pathogens to hijack host functions. Domain and motif mimicry was explored in the experimental and predicted SARS-CoV-2-human interactome. The host first interactor proteins were also added to capture the continuum of the interactions. The domains and motifs of the proteins were annotated using NCBI CD Search and ScanProsite, respectively. Host and pathogen proteins with a common host interactor and similar domain/motif constitute a mimicry pair indicating global structural similarity (domain mimicry pair; DMP) or local sequence similarity (motif mimicry pair; MMP). 593 DMPs and 7,02,472 MMPs were determined. AAA, DEXDc and Macro domains were frequent among DMPs whereas glycosylation, myristoylation and RGD motifs were abundant among MMP. The proteins involved in mimicry were visualised as a SARS-CoV-2 mimicry interaction network. The host proteins were enriched in multiple CVD pathways indicating the role of mimicry in COVID-19 associated CVDs. Bridging nodes were identified as potential drug targets. Approved antihypertensive and anti-inflammatory drugs are proposed for repurposing against COVID-19 associated CVDs. The SARS-CoV-2 mimicry data has been updated in ImitateDB (http://imitatedb.sblab-nsit.net/SARSCoV2Mimicry). Determination of key mechanisms, proteins, pathways, drug targets and repurposing candidates is critical for developing therapeutics for SARS CoV-2 associated CVDs.
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Affiliation(s)
- Sonali Tayal
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi 110078, India
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi 110078, India.
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4
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Nithya C, Kiran M, Nagarajaram HA. Dissection of hubs and bottlenecks in a protein-protein interaction network. Comput Biol Chem 2023; 102:107802. [PMID: 36603332 DOI: 10.1016/j.compbiolchem.2022.107802] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/20/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
Analysis of degree centrality in conjunction with betweenness centrality of proteins in a human protein-protein interaction network revealed three categories of centrally important proteins: a) proteins with high degree and betweenness (hub-bottlenecks denoted as MX), b) proteins with high betweenness and low degree (non-hub-bottlenecks/pure bottlenecks denoted as PB) and c) proteins with high degree and low betweenness (hub-non-bottlenecks/pure hubs denoted as PH). When subjected to a detailed statistical analysis of their molecular-level properties, the proteins belonging to each of these categories were found to be associated with distinct canonical molecular properties, i.e., "molecular markers". The MX proteins are a) conformationally versatile, mainly comprising of essential proteins, b) the targets for interactions by the proteins of viral and bacterial pathogens, c) evolutionally constrained, involved in multiple pathways, enriched with disease genes and d) involved in the functions such as protein stabilization, phosphorylation, and mRNA slicing processes. PB proteins are a) enriched with extracellular and cancer-related proteins, b) enriched with the approved drug targets and c) involved in cell-cell signaling processes. Finally, PH are a) structurally versatile, b) enriched with essential proteins primarily involved in housekeeping processes (transcription and replication). The fact that the proteins belonging to these three categories form three distinct sets in terms of their molecular properties reveals the existence of trichotomy among hubs and bottlenecks, and this knowledge is of paramount importance while prioritizing protein targets for further studies such as drug design and disease association studies based on their network centrality values.
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Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana 500046, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana 500046, India
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5
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A binary interaction map between turnip mosaic virus and Arabidopsis thaliana proteomes. Commun Biol 2023; 6:28. [PMID: 36631662 PMCID: PMC9834402 DOI: 10.1038/s42003-023-04427-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 01/05/2023] [Indexed: 01/13/2023] Open
Abstract
Viruses are obligate intracellular parasites that have co-evolved with their hosts to establish an intricate network of protein-protein interactions. Here, we followed a high-throughput yeast two-hybrid screening to identify 378 novel protein-protein interactions between turnip mosaic virus (TuMV) and its natural host Arabidopsis thaliana. We identified the RNA-dependent RNA polymerase NIb as the viral protein with the largest number of contacts, including key salicylic acid-dependent transcription regulators. We verified a subset of 25 interactions in planta by bimolecular fluorescence complementation assays. We then constructed and analyzed a network comprising 399 TuMV-A. thaliana interactions together with intravirus and intrahost connections. In particular, we found that the host proteins targeted by TuMV are enriched in different aspects of plant responses to infections, are more connected and have an increased capacity to spread information throughout the cell proteome, display higher expression levels, and have been subject to stronger purifying selection than expected by chance. The proviral or antiviral role of ten host proteins was validated by characterizing the infection dynamics in the corresponding mutant plants, supporting a proviral role for the transcriptional regulator TGA1. Comparison with similar studies with animal viruses, highlights shared fundamental features in their mode of action.
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Jiang Y, Zhang JX, Liu R. Systematic comparison of differential expression networks in MTB mono-, HIV mono- and MTB/HIV co-infections for drug repurposing. PLoS Comput Biol 2022; 18:e1010744. [PMID: 36534703 PMCID: PMC9810203 DOI: 10.1371/journal.pcbi.1010744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 01/03/2023] [Accepted: 11/17/2022] [Indexed: 12/24/2022] Open
Abstract
The synergy between human immunodeficiency virus (HIV) and Mycobacterium tuberculosis (MTB) could accelerate the deterioration of immunological functions. Previous studies have explored the pathogenic mechanisms of HIV mono-infection (HMI), MTB mono-infection (MMI) and MTB/HIV co-infection (MHCI), but their similarities and specificities remain to be profoundly investigated. We thus designed a computational framework named IDEN to identify gene pairs related to these states, which were then compared from different perspectives. MMI-related genes showed the highest enrichment level on a greater number of chromosomes. Genes shared by more states tended to be more evolutionarily conserved, posttranslationally modified and topologically important. At the expression level, HMI-specific gene pairs yielded higher correlations, while the overlapping pairs involved in MHCI had significantly lower correlations. The correlation changes of common gene pairs showed that MHCI shared more similarities with MMI. Moreover, MMI- and MHCI-related genes were enriched in more identical pathways and biological processes, further illustrating that MTB may play a dominant role in co-infection. Hub genes specific to each state could promote pathogen infections, while those shared by two states could enhance immune responses. Finally, we improved the network proximity measure for drug repurposing by considering the importance of gene pairs, and approximately ten drug candidates were identified for each disease state.
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Affiliation(s)
- Yao Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
| | - Jia-Xuan Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
| | - Rong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China
- * E-mail:
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Shuler G, Hagai T. Rapidly evolving viral motifs mostly target biophysically constrained binding pockets of host proteins. Cell Rep 2022; 40:111212. [PMID: 35977510 DOI: 10.1016/j.celrep.2022.111212] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/11/2022] [Accepted: 07/22/2022] [Indexed: 11/28/2022] Open
Abstract
Evolutionary changes in host-virus interactions can alter the course of infection, but the biophysical and regulatory constraints that shape interface evolution remain largely unexplored. Here, we focus on viral mimicry of host-like motifs that allow binding to host domains and modulation of cellular pathways. We observe that motifs from unrelated viruses preferentially target conserved, widely expressed, and highly connected host proteins, enriched with regulatory and essential functions. The interface residues within these host domains are more conserved and bind a larger number of cellular proteins than similar motif-binding domains that are not known to interact with viruses. In contrast, rapidly evolving viral-binding human proteins form few interactions with other cellular proteins and display high tissue specificity, and their interfaces have few inter-residue contacts. Our results distinguish between conserved and rapidly evolving host-virus interfaces and show how various factors limit host capacity to evolve, allowing for efficient viral subversion of host machineries.
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Affiliation(s)
- Gal Shuler
- Shmunis School of Biomedicine and Cancer Research, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tzachi Hagai
- Shmunis School of Biomedicine and Cancer Research, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
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8
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Eskandarzade N, Ghorbani A, Samarfard S, Diaz J, Guzzi PH, Fariborzi N, Tahmasebi A, Izadpanah K. Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs. Comput Biol Med 2022; 146:105575. [PMID: 35533462 PMCID: PMC9055686 DOI: 10.1016/j.compbiomed.2022.105575] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 04/16/2022] [Accepted: 04/27/2022] [Indexed: 01/08/2023]
Abstract
SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.
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Affiliation(s)
- Neda Eskandarzade
- Department of Basic Sciences, School of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Abozar Ghorbani
- Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute (NSTRI), Karaj, Iran,Corresponding author
| | - Samira Samarfard
- Berrimah Veterinary Laboratory, Department of Primary Industry and Resources, Berrimah, NT, 0828, Australia
| | - Jose Diaz
- Laboratorio de Dinámica de Redes Genéticas, Centro de Investigación en Dinámica Celular, Universidad Autónoma del Estado de Morelos, Cuernavaca, Mexico
| | - Pietro H. Guzzi
- Department of Medical and Surgical Sciences, Laboratory of Bioinformatics Unit, Italy
| | - Niloofar Fariborzi
- Department of Medical Entomology and Vector Control, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ahmad Tahmasebi
- Institute of Biotechnology, College of Agriculture, Shiraz University, Shiraz, Iran
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9
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Garg A, Dabburu GR, Singhal N, Kumar M. Investigating the disordered regions (MoRFs, SLiMs and LCRs) and functions of mimicry proteins/peptides in silico. PLoS One 2022; 17:e0265657. [PMID: 35421114 PMCID: PMC9009644 DOI: 10.1371/journal.pone.0265657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/04/2022] [Indexed: 11/24/2022] Open
Abstract
Microbial mimicry of the host proteins/peptides can elicit host auto-reactive T- or B-cells resulting in autoimmune disease(s). Since intrinsically disordered protein regions (IDPRs) are involved in several host cell signaling and PPI networks, molecular mimicry of the IDPRs can help the pathogens in substituting their own proteins in the host cell-signaling and PPI networks and, ultimately hijacking the host cellular machinery. Thus, the present study was conducted to discern the structural disorder and intrinsically disordered protein regions (IDPRs) like, molecular recognition features (MoRFs), short linear motifs (SLiMs), and low complexity regions (LCRs) in the experimentally verified mimicry proteins and peptides (mimitopes) of bacteria, viruses and host. Also, functional characteristics of the mimicry proteins were studied in silico. Our results indicated that 78% of the bacterial host mimicry proteins and 45% of the bacterial host mimitopes were moderately/highly disordered while, 73% of the viral host mimicry proteins and 31% of the viral host mimitopes were moderately/highly disordered. Among the pathogens, 27% of the bacterial mimicry proteins and 13% of the bacterial mimitopes were moderately/highly disordered while, 53% of the viral mimicry proteins and 21% of the viral mimitopes were moderately/highly disordered. Though IDPR were frequent in host, bacterial and viral mimicry proteins, only a few mimitopes overlapped with the IDPRs like, MoRFs, SLiMs and LCRs. This suggests that most of the microbes cannot use molecular mimicry to modulate the host PPIs and hijack the host cell machinery. Functional analyses indicated that most of the pathogens exhibited mimicry with the host proteins involved in ion binding and signaling pathways. This is the first report on the disordered regions and functional aspects of experimentally proven host and microbial mimicry proteins.
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Affiliation(s)
- Anjali Garg
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
| | - Govinda Rao Dabburu
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
| | - Neelja Singhal
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
- * E-mail: (MK); (NS)
| | - Manish Kumar
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
- * E-mail: (MK); (NS)
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Highly Potent Host-Specific Small-Molecule Inhibitor of Paramyxovirus and Pneumovirus Replication with High Resistance Barrier. mBio 2021; 12:e0262121. [PMID: 34724816 PMCID: PMC8561388 DOI: 10.1128/mbio.02621-21] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Multiple enveloped RNA viruses of the family Paramyxoviridae and Pneumoviridae, like measles virus (MeV), Nipah virus (NiV), canine distemper virus (CDV), or respiratory syncytial virus (RSV), are of high clinical relevance. Each year a huge number of lives are lost as a result of these viral infections. Worldwide, MeV infection alone is responsible for over a hundred thousand deaths each year despite available vaccine. Therefore, there is an urgent need for treatment options to counteract these viral infections. The development of antiviral drugs in general stands as a huge challenge due to the rapid emergence of viral escape mutants. Here, we disclose the discovery of a small-molecule antiviral, compound 1 (ZHAWOC9045), active against several pneumo-/paramyxoviruses, including MeV, NiV, CDV, RSV, and parainfluenza virus type 5 (PIV-5). A series of mechanistic characterizations revealed that compound 1 targets a host factor which is indispensable for viral genome replication. Drug resistance profiling against a paramyxovirus model (CDV) demonstrated no detectable adaptation despite prolonged time of investigation, thereby mitigating the rapid emergence of escape variants. Furthermore, a thorough structure-activity relationship analysis of compound 1 led to the invention of 100-times-more potent-derivatives, e.g., compound 2 (ZHAWOC21026). Collectively, we present in this study an attractive host-directed pneumoviral/paramyxoviral replication inhibitor with potential therapeutic application.
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11
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Das JK, Roy S, Guzzi PH. Analyzing host-viral interactome of SARS-CoV-2 for identifying vulnerable host proteins during COVID-19 pathogenesis. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2021; 93:104921. [PMID: 34004362 PMCID: PMC8123524 DOI: 10.1016/j.meegid.2021.104921] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/04/2021] [Accepted: 05/07/2021] [Indexed: 02/07/2023]
Abstract
The development of therapeutic targets for COVID-19 relies on understanding the molecular mechanism of pathogenesis. Identifying genes or proteins involved in the infection mechanism is the key to shedding light on the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced protein and genetic interactions. We integrated available results and obtained a host protein-protein interaction network composed of 1432 human proteins. Next, we performed network centrality analysis to identify critical proteins in the derived network. Finally, we performed a functional enrichment analysis of central proteins. We observed that the identified proteins are primarily associated with several crucial pathways, including cellular process, signaling transduction, neurodegenerative diseases. We focused on the proteins that are involved in human respiratory tract diseases. We highlighted many potential therapeutic targets, including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, and HSPA1A, which are central and also associated with multiple diseases.
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Affiliation(s)
- Jayanta Kumar Das
- Department of Pediatrics, School of Medicine, Johns Hopkins University, MD, USA
| | - Swarup Roy
- Network Reconstruction & Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, Gangtok, India,Corresponding authors
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy,Corresponding authors
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12
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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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13
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Das JK, Chakraborty S, Roy S. A scheme for inferring viral-host associations based on codon usage patterns identifies the most affected signaling pathways during COVID-19. J Biomed Inform 2021; 118:103801. [PMID: 33965637 PMCID: PMC8102073 DOI: 10.1016/j.jbi.2021.103801] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 05/02/2021] [Accepted: 05/03/2021] [Indexed: 12/16/2022]
Abstract
Understanding the molecular mechanism of COVID-19 pathogenesis helps in the rapid therapeutic target identification. Usually, viral protein targets host proteins in an organized fashion. The expression of any viral gene depends mostly on the host translational machinery. Recent studies report the great significance of codon usage biases in establishing host-viral protein–protein interactions (PPI). Exploring the codon usage patterns between a pair of co-evolved host and viral proteins may present novel insight into the host-viral protein interactomes during disease pathogenesis. Leveraging the similarity in codon usage patterns, we propose a computational scheme to recreate the host-viral protein–protein interaction network. We use host proteins from seventeen (17) essential signaling pathways for our current work towards understanding the possible targeting mechanism of SARS-CoV-2 proteins. We infer both negatively and positively interacting edges in the network. Further, extensive analysis is performed to understand the host PPI network topologically and the attacking behavior of the viral proteins. Our study reveals that viral proteins mostly utilize codons, rare in the targeted host proteins (negatively correlated interaction). Among them, non-structural proteins, NSP3 and structural protein, Spike (S), are the most influential proteins in interacting with multiple host proteins. While ranking the most affected pathways, MAPK pathways observe to be the worst affected during the SARS-CoV-2 infection. Several proteins participating in multiple pathways are highly central in host PPI and mostly targeted by multiple viral proteins. We observe many potential targets (host proteins) from the affected pathways associated with the various drug molecules, including Arsenic trioxide, Dexamethasone, Hydroxychloroquine, Ritonavir, and Interferon beta, which are either under clinical trial or in use during COVID-19.
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Affiliation(s)
- Jayanta Kumar Das
- Department of Pediatrics, Johns Hopkins University, School of Medicine, MD, USA
| | | | - Swarup Roy
- Network Reconstruction & Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, Gangtok, India.
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14
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Yapici-Eser H, Koroglu YE, Oztop-Cakmak O, Keskin O, Gursoy A, Gursoy-Ozdemir Y. Neuropsychiatric Symptoms of COVID-19 Explained by SARS-CoV-2 Proteins' Mimicry of Human Protein Interactions. Front Hum Neurosci 2021; 15:656313. [PMID: 33833673 PMCID: PMC8021734 DOI: 10.3389/fnhum.2021.656313] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 02/23/2021] [Indexed: 12/16/2022] Open
Abstract
The first clinical symptoms focused on the presentation of coronavirus disease 2019 (COVID-19) have been respiratory failure, however, accumulating evidence also points to its presentation with neuropsychiatric symptoms, the exact mechanisms of which are not well known. By using a computational methodology, we aimed to explain the molecular paths of COVID-19 associated neuropsychiatric symptoms, based on the mimicry of the human protein interactions with SARS-CoV-2 proteins. Methods: Available 11 of the 29 SARS-CoV-2 proteins' structures have been extracted from Protein Data Bank. HMI-PRED (Host-Microbe Interaction PREDiction), a recently developed web server for structural PREDiction of protein-protein interactions (PPIs) between host and any microbial species, was used to find the "interface mimicry" through which the microbial proteins hijack host binding surfaces. Classification of the found interactions was conducted using the PANTHER Classification System. Results: Predicted Human-SARS-CoV-2 protein interactions have been extensively compared with the literature. Based on the analysis of the molecular functions, cellular localizations and pathways related to human proteins, SARS-CoV-2 proteins are found to possibly interact with human proteins linked to synaptic vesicle trafficking, endocytosis, axonal transport, neurotransmission, growth factors, mitochondrial and blood-brain barrier elements, in addition to its peripheral interactions with proteins linked to thrombosis, inflammation and metabolic control. Conclusion: SARS-CoV-2-human protein interactions may lead to the development of delirium, psychosis, seizures, encephalitis, stroke, sensory impairments, peripheral nerve diseases, and autoimmune disorders. Our findings are also supported by the previous in vivo and in vitro studies from other viruses. Further in vivo and in vitro studies using the proteins that are pointed here, could pave new targets both for avoiding and reversing neuropsychiatric presentations.
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Affiliation(s)
- Hale Yapici-Eser
- Department of Psychiatry, School of Medicine, Koç University, Istanbul, Turkey
- Research Center for Translational Medicine, Koç University, Istanbul, Turkey
| | - Yunus Emre Koroglu
- Research Center for Translational Medicine, Koç University, Istanbul, Turkey
- Graduate School of Sciences and Engineering, College of Engineering, Koç University, Istanbul, Turkey
| | - Ozgur Oztop-Cakmak
- Research Center for Translational Medicine, Koç University, Istanbul, Turkey
- Department of Neurology, School of Medicine, Koç University, Istanbul, Turkey
| | - Ozlem Keskin
- Research Center for Translational Medicine, Koç University, Istanbul, Turkey
- College of Engineering, Chemical and Biological Engineering, Koç University, Istanbul, Turkey
| | - Attila Gursoy
- Research Center for Translational Medicine, Koç University, Istanbul, Turkey
- Department of Computer Science and Engineering, College of Engineering, Koç University, Istanbul, Turkey
| | - Yasemin Gursoy-Ozdemir
- Research Center for Translational Medicine, Koç University, Istanbul, Turkey
- Department of Neurology, School of Medicine, Koç University, Istanbul, Turkey
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15
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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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16
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Acharya D, Dutta TK. Elucidating the network features and evolutionary attributes of intra- and interspecific protein-protein interactions between human and pathogenic bacteria. Sci Rep 2021; 11:190. [PMID: 33420198 PMCID: PMC7794237 DOI: 10.1038/s41598-020-80549-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 12/09/2020] [Indexed: 01/08/2023] Open
Abstract
Host–pathogen interaction is one of the most powerful determinants involved in coevolutionary processes covering a broad range of biological phenomena at molecular, cellular, organismal and/or population level. The present study explored host–pathogen interaction from the perspective of human–bacteria protein–protein interaction based on large-scale interspecific and intraspecific interactome data for human and three pathogenic bacterial species, Bacillus anthracis, Francisella tularensis and Yersinia pestis. The network features revealed a preferential enrichment of intraspecific hubs and bottlenecks for both human and bacterial pathogens in the interspecific human–bacteria interaction. Analyses unveiled that these bacterial pathogens interact mostly with human party-hubs that may enable them to affect desired functional modules, leading to pathogenesis. Structural features of pathogen-interacting human proteins indicated an abundance of protein domains, providing opportunities for interspecific domain-domain interactions. Moreover, these interactions do not always occur with high-affinity, as we observed that bacteria-interacting human proteins are rich in protein-disorder content, which correlates positively with the number of interacting pathogen proteins, facilitating low-affinity interspecific interactions. Furthermore, functional analyses of pathogen-interacting human proteins revealed an enrichment in regulation of processes like metabolism, immune system, cellular localization and transport apart from divulging functional competence to bind enzyme/protein, nucleic acids and cell adhesion molecules, necessary for host-microbial cross-talk.
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Affiliation(s)
- Debarun Acharya
- Department of Microbiology, Bose Institute, P-1/12, CIT Scheme VII M, Kolkata, West Bengal, 700 054, India
| | - Tapan K Dutta
- Department of Microbiology, Bose Institute, P-1/12, CIT Scheme VII M, Kolkata, West Bengal, 700 054, India.
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17
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Xiang Y, Zou Q, Zhao L. VPTMdb: a viral posttranslational modification database. Brief Bioinform 2020; 22:5935500. [PMID: 33094321 DOI: 10.1093/bib/bbaa251] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 09/03/2020] [Accepted: 09/07/2020] [Indexed: 11/14/2022] Open
Abstract
In viruses, posttranslational modifications (PTMs) are essential for their life cycle. Recognizing viral PTMs is very important for a better understanding of the mechanism of viral infections and finding potential drug targets. However, few studies have investigated the roles of viral PTMs in virus-human interactions using comprehensive viral PTM datasets. To fill this gap, we developed the first comprehensive viral posttranslational modification database (VPTMdb) for collecting systematic information of PTMs in human viruses and infected host cells. The VPTMdb contains 1240 unique viral PTM sites with 8 modification types from 43 viruses (818 experimentally verified PTM sites manually extracted from 150 publications and 422 PTMs extracted from SwissProt) as well as 13 650 infected cells' PTMs extracted from seven global proteomics experiments in six human viruses. The investigation of viral PTM sequences motifs showed that most viral PTMs have the consensus motifs with human proteins in phosphorylation and five cellular kinase families phosphorylate more than 10 viral species. The analysis of protein disordered regions presented that more than 50% glycosylation sites of double-strand DNA viruses are in the disordered regions, whereas single-strand RNA and retroviruses prefer ordered regions. Domain-domain interaction analysis indicating potential roles of viral PTMs play in infections. The findings should make an important contribution to the field of virus-human interaction. Moreover, we created a novel sequence-based classifier named VPTMpre to help users predict viral protein phosphorylation sites. VPTMdb online web server (http://vptmdb.com:8787/VPTMdb/) was implemented for users to download viral PTM data and predict phosphorylation sites of interest.
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Affiliation(s)
- Yujia Xiang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences
| | - Quan Zou
- University of Electronic Science and Technology of China
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18
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Abstract
Antiviral drugs have traditionally been developed by directly targeting essential viral components. However, this strategy often fails due to the rapid generation of drug-resistant viruses. Recent genome-wide approaches, such as those employing small interfering RNA (siRNA) or clustered regularly interspaced short palindromic repeats (CRISPR) or those using small molecule chemical inhibitors targeting the cellular "kinome," have been used successfully to identify cellular factors that can support virus replication. Since some of these cellular factors are critical for virus replication, but are dispensable for the host, they can serve as novel targets for antiviral drug development. In addition, potentiation of immune responses, regulation of cytokine storms, and modulation of epigenetic changes upon virus infections are also feasible approaches to control infections. Because it is less likely that viruses will mutate to replace missing cellular functions, the chance of generating drug-resistant mutants with host-targeted inhibitor approaches is minimized. However, drug resistance against some host-directed agents can, in fact, occur under certain circumstances, such as long-term selection pressure of a host-directed antiviral agent that can allow the virus the opportunity to adapt to use an alternate host factor or to alter its affinity toward the target that confers resistance. This review describes novel approaches for antiviral drug development with a focus on host-directed therapies and the potential mechanisms that may account for the acquisition of antiviral drug resistance against host-directed agents.
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19
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Sun J, Shi Q, Chen X, Liu R. Decoding the similarities and specific differences between latent and active tuberculosis infections based on consistently differential expression networks. Brief Bioinform 2019; 21:2084-2098. [PMID: 31724702 DOI: 10.1093/bib/bbz127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Revised: 08/21/2019] [Accepted: 09/06/2019] [Indexed: 11/14/2022] Open
Abstract
Although intensive efforts have been devoted to investigating latent tuberculosis (LTB) and active tuberculosis (PTB) infections, the similarities and differences in the host responses to these two closely associated stages remain elusive, probably due to the difficulty in identifying informative genes related to LTB using traditional methods. Herein, we developed a framework known as the consistently differential expression network to identify tuberculosis (TB)-related gene pairs by combining microarray profiles and protein-protein interactions. We thus obtained 774 and 693 pairs corresponding to the PTB and LTB stages, respectively. The PTB-specific genes showed higher expression values and fold-changes than the LTB-specific genes. Furthermore, the PTB-related pairs generally had higher expression correlations and would be more activated compared to their LTB-related counterparts. The module analysis implied that the detected gene pairs tended to cluster in the topological and functional modules. Functional analysis indicated that the LTB- and PTB-specific genes were enriched in different pathways and had remarkably different locations in the NF-κB signaling pathway. Finally, we showed that the identified genes and gene pairs had the potential to distinguish TB patients in different disease stages and could be considered as drug targets for the specific treatment of patients with LTB or PTB.
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Affiliation(s)
- Jun Sun
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Qianqian Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Xi Chen
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.,College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China
| | - Rong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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20
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Lasso G, Mayer SV, Winkelmann ER, Chu T, Elliot O, Patino-Galindo JA, Park K, Rabadan R, Honig B, Shapira SD. A Structure-Informed Atlas of Human-Virus Interactions. Cell 2019; 178:1526-1541.e16. [PMID: 31474372 PMCID: PMC6736651 DOI: 10.1016/j.cell.2019.08.005] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/17/2019] [Accepted: 08/02/2019] [Indexed: 12/19/2022]
Abstract
While knowledge of protein-protein interactions (PPIs) is critical for understanding virus-host relationships, limitations on the scalability of high-throughput methods have hampered their identification beyond a number of well-studied viruses. Here, we implement an in silico computational framework (pathogen host interactome prediction using structure similarity [P-HIPSTer]) that employs structural information to predict ∼282,000 pan viral-human PPIs with an experimental validation rate of ∼76%. In addition to rediscovering known biology, P-HIPSTer has yielded a series of new findings: the discovery of shared and unique machinery employed across human-infecting viruses, a likely role for ZIKV-ESR1 interactions in modulating viral replication, the identification of PPIs that discriminate between human papilloma viruses (HPVs) with high and low oncogenic potential, and a structure-enabled history of evolutionary selective pressure imposed on the human proteome. Further, P-HIPSTer enables discovery of previously unappreciated cellular circuits that act on human-infecting viruses and provides insight into experimentally intractable viruses.
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Affiliation(s)
- Gorka Lasso
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Sandra V Mayer
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Evandro R Winkelmann
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA
| | - Tim Chu
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
| | - Oliver Elliot
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
| | | | - Kernyu Park
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
| | - Raul Rabadan
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University Medical Center, New York, NY, USA; Howard Hughes Medical Institute, Columbia University Medical Center, New York, NY, USA.
| | - Sagi D Shapira
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA.
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21
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Huang D, Wen W, Liu X, Li Y, Zhang JZH. Computational analysis of hot spots and binding mechanism in the PD-1/PD-L1 interaction. RSC Adv 2019; 9:14944-14956. [PMID: 35516311 PMCID: PMC9064197 DOI: 10.1039/c9ra01369e] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 05/05/2019] [Indexed: 12/14/2022] Open
Abstract
Programmed cell death protein-1 (PD-1) is an important immunological checkpoint and plays a vital role in maintaining the peripheral tolerance of the human body by interacting with its ligand PD-L1. The overexpression of PD-L1 in tumor cells induces local immune suppression and helps the tumor cells to evade the endogenous anti-tumor immunity. Developing monoclonal antibodies against the PD-1/PD-L1 protein–protein interaction to block the PD-1/PD-L1 signaling pathway has demonstrated superior anti-tumor efficacy in a variety of solid tumors and has made a profound impact on the field of cancer immunotherapy in recent years. Although the X-ray crystal structure of the PD-1/PD-L1 complex has been solved, the detailed binding mechanism of the PD-1/PD-L1 interaction is not fully understood from a theoretical point of view. In this study, we performed computational alanine scanning on the PD-1/PD-L1 complex to quantitatively identify the hot spots in the PD-1/PD-L1 interaction and characterize its binding mechanisms at the atomic level. To the best of our knowledge, this is the first time that theoretical calculations have been used to systematically and quantitatively predict the hot spots in the PD-1/PD-L1 interaction. We hope that the predicted hot spots and the energy profile of the PD-1/PD-L1 interaction presented in this work can provide guidance for the design of peptide and small molecule drugs targeting PD-1 or PD-L1. The hot spots quantitatively predicted by the recently developed MM/GBSA/IE method reveal a hydrophobic core in the PD-1/PD-L1 interaction.![]()
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Affiliation(s)
- Dading Huang
- State Key Laboratory for Precision Spectroscopy
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development
- School of Chemistry and Molecular Engineering
- East China Normal University
- Shanghai 200062
| | - Wei Wen
- State Key Laboratory for Precision Spectroscopy
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development
- School of Chemistry and Molecular Engineering
- East China Normal University
- Shanghai 200062
| | - Xiao Liu
- State Key Laboratory for Precision Spectroscopy
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development
- School of Chemistry and Molecular Engineering
- East China Normal University
- Shanghai 200062
| | - Yang Li
- State Key Laboratory for Precision Spectroscopy
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development
- School of Chemistry and Molecular Engineering
- East China Normal University
- Shanghai 200062
| | - John Z. H. Zhang
- State Key Laboratory for Precision Spectroscopy
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development
- School of Chemistry and Molecular Engineering
- East China Normal University
- Shanghai 200062
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22
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Huang D, Qi Y, Song J, Zhang JZH. Calculation of hot spots for protein–protein interaction in p53/PMI‐MDM2/MDMX complexes. J Comput Chem 2018; 40:1045-1056. [DOI: 10.1002/jcc.25592] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 08/04/2018] [Accepted: 08/23/2018] [Indexed: 12/24/2022]
Affiliation(s)
- Dading Huang
- School of Physics and Material Science, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200062 China
| | - Yifei Qi
- School of Physics and Material Science, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200062 China
- NYU‐ECNU Center for Computational Chemistry at NYU Shanghai Shanghai 200062 China
| | - Jianing Song
- NYU‐ECNU Center for Computational Chemistry at NYU Shanghai Shanghai 200062 China
| | - John Z. H. Zhang
- School of Physics and Material Science, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200062 China
- NYU‐ECNU Center for Computational Chemistry at NYU Shanghai Shanghai 200062 China
- Department of ChemistryNew York University New York New York, 10003
- Collaborative Innovation Center of Extreme OpticsShanxi University Taiyuan Shanxi, 030006 China
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23
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Chen J, Sun J, Liu X, Liu F, Liu R, Wang J. Structure-based prediction of West Nile virus-human protein-protein interactions. J Biomol Struct Dyn 2018; 37:2310-2321. [PMID: 30044201 DOI: 10.1080/07391102.2018.1479659] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In recent years, West Nile virus (WNV) has posed a great threat to global human health due to its explosive spread. Studying the protein-protein interactions (PPIs) between WNV and human is beneficial for understanding the pathogenesis of WNV and the immune response mechanism of human against WNV infection at the molecular level. In this study, we identified the human target proteins which interact with WNV based on protein structure similarity, and then the interacting pairs were filtered by the subcellular co-localization information. As a result, a network of 3346 interactions was constructed, involving 6 WNV proteins and 1970 human target proteins. To our knowledge, this is the first predicted interactome for WNV-human. By analyzing the topological properties and evolutionary rates of the human target proteins, it was demonstrated that these proteins tend to be the hub and bottleneck proteins in the human PPI network and are more conserved than the non-target ones. Triplet analysis showed that the target proteins are adjacent to each other in the human PPI network, suggesting that these proteins may have similar biological functions. Further, the functional enrichment analysis indicated that the target proteins are mainly involved in virus process, transcription regulation, cell adhesion, and so on. In addition, the common and specific targets were identified and compared based on the networks between WNV-human and Dengue virus II (DENV2)-human. Finally, by combining topological features and existing drug target information, we identified 30 potential anti-WNV human targets, among which 11 ones were reported to be associated with WNV infection. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Jing Chen
- a Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics , Huazhong Agricultural University , Wuhan , People's Republic of China
| | - Jun Sun
- a Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics , Huazhong Agricultural University , Wuhan , People's Republic of China
| | - Xiangming Liu
- b Gongqing Institute of Science and Technology , Gongqing , People's Republic of China
| | - Feng Liu
- a Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics , Huazhong Agricultural University , Wuhan , People's Republic of China
| | - Rong Liu
- a Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics , Huazhong Agricultural University , Wuhan , People's Republic of China
| | - Jia Wang
- a Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics , Huazhong Agricultural University , Wuhan , People's Republic of China
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24
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Gene expression variability across cells and species shapes innate immunity. Nature 2018; 563:197-202. [PMID: 30356220 PMCID: PMC6347972 DOI: 10.1038/s41586-018-0657-2] [Citation(s) in RCA: 125] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 08/17/2018] [Indexed: 11/09/2022]
Abstract
As the first line of defence against pathogens, cells mount an innate immune response, which varies widely from cell to cell. The response must be potent but carefully controlled to avoid self-damage. How these constraints have shaped the evolution of innate immunity remains poorly understood. Here we characterize the innate immune response's transcriptional divergence between species and variability in expression among cells. Using bulk and single-cell transcriptomics in fibroblasts and mononuclear phagocytes from different species, challenged with immune stimuli, we map the architecture of the innate immune response. Transcriptionally diverging genes, including those that encode cytokines and chemokines, vary across cells and have distinct promoter structures. Conversely, genes that are involved in the regulation of this response, such as those that encode transcription factors and kinases, are conserved between species and display low cell-to-cell variability in expression. We suggest that this expression pattern, which is observed across species and conditions, has evolved as a mechanism for fine-tuned regulation to achieve an effective but balanced response.
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25
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Sun J, Yang LL, Chen X, Kong DX, Liu R. Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions. J Proteome Res 2018; 17:3810-3823. [PMID: 30269499 DOI: 10.1021/acs.jproteome.8b00497] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.
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26
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Chasapis CT. Interactions between metal binding viral proteins and human targets as revealed by network-based bioinformatics. J Inorg Biochem 2018; 186:157-161. [DOI: 10.1016/j.jinorgbio.2018.06.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/24/2018] [Accepted: 06/14/2018] [Indexed: 12/13/2022]
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27
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Goodacre N, Devkota P, Bae E, Wuchty S, Uetz P. Protein-protein interactions of human viruses. Semin Cell Dev Biol 2018; 99:31-39. [PMID: 30031213 PMCID: PMC7102568 DOI: 10.1016/j.semcdb.2018.07.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 04/02/2018] [Accepted: 07/17/2018] [Indexed: 12/16/2022]
Abstract
Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.
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Affiliation(s)
- Norman Goodacre
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Prajwal Devkota
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, 33146, USA
| | - Eunhae Bae
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Stefan Wuchty
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, 33146, USA; Center for Computational Science, Univ. of Miami, Coral Gables, FL, 33146, USA; Dept. of Biology, Univ. of Miami, Coral Gables, FL, 33146, USA; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.
| | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, 23284, USA.
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Li H, Zhou Y, Zhang Z. Network Analysis Reveals a Common Host-Pathogen Interaction Pattern in Arabidopsis Immune Responses. FRONTIERS IN PLANT SCIENCE 2017; 8:893. [PMID: 28611808 PMCID: PMC5446985 DOI: 10.3389/fpls.2017.00893] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 05/12/2017] [Indexed: 05/28/2023]
Abstract
Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein-protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs). We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant-pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.
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Affiliation(s)
| | - Yuan Zhou
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural UniversityBeijing, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural UniversityBeijing, China
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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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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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32
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Enard D, Cai L, Gwennap C, Petrov DA. Viruses are a dominant driver of protein adaptation in mammals. eLife 2016; 5. [PMID: 27187613 PMCID: PMC4869911 DOI: 10.7554/elife.12469] [Citation(s) in RCA: 172] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 04/04/2016] [Indexed: 12/12/2022] Open
Abstract
Viruses interact with hundreds to thousands of proteins in mammals, yet adaptation against viruses has only been studied in a few proteins specialized in antiviral defense. Whether adaptation to viruses typically involves only specialized antiviral proteins or affects a broad array of virus-interacting proteins is unknown. Here, we analyze adaptation in ~1300 virus-interacting proteins manually curated from a set of 9900 proteins conserved in all sequenced mammalian genomes. We show that viruses (i) use the more evolutionarily constrained proteins within the cellular functions they interact with and that (ii) despite this high constraint, virus-interacting proteins account for a high proportion of all protein adaptation in humans and other mammals. Adaptation is elevated in virus-interacting proteins across all functional categories, including both immune and non-immune functions. We conservatively estimate that viruses have driven close to 30% of all adaptive amino acid changes in the part of the human proteome conserved within mammals. Our results suggest that viruses are one of the most dominant drivers of evolutionary change across mammalian and human proteomes.
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Affiliation(s)
- David Enard
- Department of Biology, Stanford University, Stanford, United States
| | - Le Cai
- Department of Biology, Stanford University, Stanford, United States
| | - Carina Gwennap
- Department of Biology, Stanford University, Stanford, United States
| | - Dmitri A Petrov
- Department of Biology, Stanford University, Stanford, United States
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Kiran M, Nagarajaram HA. Interaction and localization diversities of global and local hubs in human protein–protein interaction networks. MOLECULAR BIOSYSTEMS 2016; 12:2875-82. [DOI: 10.1039/c6mb00104a] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Hubs, the highly connected nodes in protein–protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells.
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Affiliation(s)
- M. Kiran
- Laboratory of Computational Biology
- Centre for DNA Fingerprinting and Diagnostics
- Gruhakalpa
- Hyderabad 500 001
- India
| | - H. A. Nagarajaram
- Laboratory of Computational Biology
- Centre for DNA Fingerprinting and Diagnostics
- Gruhakalpa
- Hyderabad 500 001
- India
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