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Rosilan NF, Waiho K, Fazhan H, Sung YY, Zakaria NH, Afiqah-Aleng N, Mohamed-Hussein ZA. Current trends of host-pathogen relationship in shrimp infectious disease via computational protein-protein interaction: A bibliometric analysis. FISH & SHELLFISH IMMUNOLOGY 2023; 142:109171. [PMID: 37858788 DOI: 10.1016/j.fsi.2023.109171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/12/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023]
Abstract
Protein-protein interactions (PPIs) are essential for understanding cell physiology in normal and pathological conditions, as they might involve in all cellular processes. PPIs have been widely used to elucidate the pathobiology of human and plant diseases. Therefore, they can also be used to unveil the pathobiology of infectious diseases in shrimp, which is one of the high-risk factors influencing the success or failure of shrimp production. PPI network analysis, specifically host-pathogen PPI (HP-PPI), provides insights into the molecular interactions between the shrimp and pathogens. This review quantitatively analyzed the research trends within this field through bibliometric analysis using specific keywords, countries, authors, organizations, journals, and documents. This analysis has screened 206 records from the Scopus database for determining eligibility, resulting in 179 papers that were retrieved for bibliometric analysis. The analysis revealed that China and Thailand were the driving forces behind this specific field of research and frequently collaborated with the United States. Aquaculture and Diseases of Aquatic Organisms were the prominent sources for publications in this field. The main keywords identified included "white spot syndrome virus," "WSSV," and "shrimp." We discovered that studies on HP-PPI are currently quite scarce. As a result, we further discussed the significance of HP-PPI by highlighting various approaches that have been previously adopted. These findings not only emphasize the importance of HP-PPI but also pave the way for future researchers to explore the pathogenesis of infectious diseases in shrimp. By doing so, preventative measures and enhanced treatment strategies can be identified.
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Affiliation(s)
- Nur Fathiah Rosilan
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
| | - Khor Waiho
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia; Centre for Chemical Biology, Universiti Sains Malaysia, Minden, 11900, Penang, Malaysia; Department of Aquaculture, Faculty of Fisheries, Kasetsart University, 10900, Bangkok, Thailand
| | - Hanafiah Fazhan
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia; Centre for Chemical Biology, Universiti Sains Malaysia, Minden, 11900, Penang, Malaysia; Department of Aquaculture, Faculty of Fisheries, Kasetsart University, 10900, Bangkok, Thailand
| | - Yeong Yik Sung
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
| | - Nor Hafizah Zakaria
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia.
| | - Nor Afiqah-Aleng
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia.
| | - Zeti-Azura Mohamed-Hussein
- UKM Medical Molecular Biology Institute, UKM Medical Centre, Jalan Yaacob Latiff, 56000, Cheras, Kuala Lumpur, Malaysia; Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
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Singhvi N, Talwar C, Mahanta U, Kaur J, Mondal K, Ahmad N, Tyagi I, Sharma G, Gupta V. Comparative genomics and integrated system biology approach unveiled undirected phylogeny patterns, mutational hotspots, functional patterns, and molecule repurposing for monkeypox virus. Funct Integr Genomics 2023; 23:231. [PMID: 37432480 DOI: 10.1007/s10142-023-01168-z] [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: 02/20/2023] [Revised: 06/08/2023] [Accepted: 07/03/2023] [Indexed: 07/12/2023]
Abstract
Monkeypox is a viral zoonosis with symptoms that are reminiscent of those experienced in previous smallpox cases. The GSAID database (Global Initiative on Sharing Avian Influenza Data) was used to assess 630 genomes of MPXV. The phylogenetic study revealed six primary clades, as well as a smaller percentage in radiating clades. Individual clades that make up various nationalities may have formed as a result of a particular SNP hotspot type that mutated in a specific population. The most significant mutation based on a mutational hotspot analysis was found at G3729A and G5143A. The gene ORF138, which encodes the Ankyrin repeat (ANK) protein, was found to have the most mutations. This protein mediates molecular recognition via protein-protein interactions. It was shown that 243 host proteins interacted with 10 monkeypox proteins identified as the hub proteins E3, SPI2, C5, K7, E8, G6, N2, B14, CRMB, and A41 through 262 direct connections. The interaction with chemokine system-related proteins provides further evidence that the monkeypox virus suppresses human proteins to facilitate its survival against innate immunity. Several FDA-approved molecules were evaluated as possible inhibitors of F13, a significant envelope protein on the membrane of extracellular versions of the virus. A total of 2500 putative ligands were individually docked with the F13 protein. The interaction between the F13 protein and these molecules may help prevent the monkeypox virus from spreading. After being confirmed by experiments, these putative inhibitors could have an impact on the activity of these proteins and be used in monkeypox treatments.
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Affiliation(s)
- Nirjara Singhvi
- Department of Zoology, School of Allied Sciences, Dev Bhoomi Uttarakhand University, Dehradun, 248007, India
| | - Chandni Talwar
- Department of Zoology, University of Delhi, Delhi, India, 110007
| | - Utkarsha Mahanta
- Institute of Bioinformatics and Applied Biotechnology (IBAB), Bengaluru, Karnataka, 560100, India
- Department of Biotechnology, Indian Institute of Technology Hyderabad, Telangana, 502284, India
| | - Jasvinder Kaur
- Department of Zoology, Gargi College, University of Delhi, New Delhi, 110049, India
| | - Krishnendu Mondal
- Ministry of Environment, Forest and Climate Change, Integrated Regional Office, Dehradun, 248001, India
| | - Nabeel Ahmad
- Department of Biotechnology, School of Allied Sciences, Dev Bhoomi Uttarakhand University, Dehradun, 248007, India
| | - Inderjeet Tyagi
- Centre of DNA Taxonomy, Molecular Systematics Division, Zoological Survey of India,, Kolkata, 700053, India
| | - Gaurav Sharma
- Institute of Bioinformatics and Applied Biotechnology (IBAB), Bengaluru, Karnataka, 560100, India
- Department of Biotechnology, Indian Institute of Technology Hyderabad, Telangana, 502284, India
| | - Vipin Gupta
- Ministry of Environment, Forest and Climate Change, Integrated Regional Office, Dehradun, 248001, India.
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Palomba M, Rughetti A, Mignogna G, Castrignanò T, Rahimi H, Masuelli L, Napoletano C, Pinna V, Giorgi A, Santoro M, Schininà ME, Maras B, Mattiucci S. Proteomic characterization of extracellular vesicles released by third stage larvae of the zoonotic parasite Anisakis pegreffii (Nematoda: Anisakidae). Front Cell Infect Microbiol 2023; 13:1079991. [PMID: 37009516 PMCID: PMC10050594 DOI: 10.3389/fcimb.2023.1079991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionAnisakis pegreffii is a sibling species within the A. simplex (s.l.) complex requiring marine homeothermic (mainly cetaceans) and heterothermic (crustaceans, fish, and cephalopods) organisms to complete its life cycle. It is also a zoonotic species, able to accidentally infect humans (anisakiasis). To investigate the molecular signals involved in this host-parasite interaction and pathogenesis, the proteomic composition of the extracellular vesicles (EVs) released by the third-stage larvae (L3) of A. pegreffii, was characterized.MethodsGenetically identified L3 of A. pegreffii were maintained for 24 h at 37°C and EVs were isolated by serial centrifugation and ultracentrifugation of culture media. Proteomic analysis was performed by Shotgun Analysis.Results and discussionEVs showed spherical shaped structure (size 65-295 nm). Proteomic results were blasted against the A. pegreffii specific transcriptomic database, and 153 unique proteins were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis predicted several proteins belonging to distinct metabolic pathways. The similarity search employing selected parasitic nematodes database revealed that proteins associated with A. pegreffii EVs might be involved in parasite survival and adaptation, as well as in pathogenic processes. Further, a possible link between the A. pegreffii EVs proteins versus those of human and cetaceans’ hosts, were predicted by using HPIDB database. The results, herein described, expand knowledge concerning the proteins possibly implied in the host-parasite interactions between this parasite and its natural and accidental hosts.
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Affiliation(s)
- Marialetizia Palomba
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Aurelia Rughetti
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Giuseppina Mignogna
- Department of Biochemistry Science, Sapienza University of Rome, Rome, Italy
| | - Tiziana Castrignanò
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Hassan Rahimi
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Laura Masuelli
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Chiara Napoletano
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Valentina Pinna
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, Italy
| | - Alessandra Giorgi
- Department of Biochemistry Science, Sapienza University of Rome, Rome, Italy
| | - Mario Santoro
- Department of Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Naples, Italy
| | | | - Bruno Maras
- Department of Biochemistry Science, Sapienza University of Rome, Rome, Italy
| | - Simonetta Mattiucci
- Department of Public Health and Infectious Diseases, Section of Parasitology, Sapienza University of Rome, Rome, Italy
- *Correspondence: Simonetta Mattiucci,
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Zaldívar-López S, Herrera-Uribe J, Bautista R, Jiménez Á, Moreno Á, Claros MG, Garrido JJ. Salmonella Typhimurium induces genome-wide expression and phosphorylation changes that modulate immune response, intracellular survival and vesicle transport in infected neutrophils. DEVELOPMENTAL AND COMPARATIVE IMMUNOLOGY 2023; 140:104597. [PMID: 36450302 DOI: 10.1016/j.dci.2022.104597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/16/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
Salmonella Typhimurium is a food-borne pathogen that causes salmonellosis. When in contact with the host, neutrophils are rapidly recruited to act as first line of defense. To better understand the pathogenesis of this infection, we used an in vitro model of neutrophil infection to perform dual RNA-sequencing (both host and pathogen). In addition, and given that many pathogens interfere with kinase-mediated phosphorylation in host signaling, we performed a phosphoproteomic analysis. The immune response was overall diminished in infected neutrophils, mainly JAK/STAT and toll-like receptor signaling pathways. We found decreased expression of proinflammatory cytokine receptor genes and predicted downregulation of the mitogen-activated protein (MAPK) signaling pathway. Also, Salmonella infection inhibited interferons I and II signaling pathways by upregulation of SOCS3 and subsequent downregulation of STAT1 and STAT2. Additionally, phosphorylation of PSMC2 and PSMC4, proteasome regulatory proteins, was decreased in infected neutrophils. Cell viability and survival was increased by p53 signaling, cell cycle arrest and NFkB-proteasome pathways activation. Combined analysis of RNA-seq and phosphoproteomics also revealed inhibited vesicle transport mechanisms mediated by dynein/dynactin and exocyst complexes, involved in ER-to-Golgi transport and centripetal movement of lysosomes and endosomes. Among the overexpressed virulence genes from Salmonella we found potential effectors responsible of these dysregulations, such as spiC, sopD2, sifA or pipB2, all of them involved in intracellular replication. Our results suggest that Salmonella induces (through overexpression of virulence factors) transcriptional and phosphorylation changes that increases neutrophil survival and shuts down immune response to minimize host response, and impairing intracellular vesicle transport likely to keep nutrients for replication and Salmonella-containing vacuole formation and maintenance.
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Affiliation(s)
- Sara Zaldívar-López
- Grupo de Inmunogenómica y Patogénesis Molecular, UIC Zoonosis y Enfermedades Emergentes ENZOEM, Departamento de Genética, Universidad de Córdoba, Córdoba, Spain; Maimónides Biomedical Research Institute of Córdoba (IMIBIC), GA-14 Research Group, Córdoba, Spain.
| | - Juber Herrera-Uribe
- Grupo de Inmunogenómica y Patogénesis Molecular, UIC Zoonosis y Enfermedades Emergentes ENZOEM, Departamento de Genética, Universidad de Córdoba, Córdoba, Spain
| | - Rocío Bautista
- Plataforma Andaluza de Bioinformática, Universidad de Málaga, Málaga, Spain
| | - Ángeles Jiménez
- Grupo de Inmunogenómica y Patogénesis Molecular, UIC Zoonosis y Enfermedades Emergentes ENZOEM, Departamento de Genética, Universidad de Córdoba, Córdoba, Spain
| | - Ángela Moreno
- Grupo de Inmunogenómica y Patogénesis Molecular, UIC Zoonosis y Enfermedades Emergentes ENZOEM, Departamento de Genética, Universidad de Córdoba, Córdoba, Spain
| | - M Gonzalo Claros
- Plataforma Andaluza de Bioinformática, Universidad de Málaga, Málaga, Spain; Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, Málaga, Spain
| | - Juan J Garrido
- Grupo de Inmunogenómica y Patogénesis Molecular, UIC Zoonosis y Enfermedades Emergentes ENZOEM, Departamento de Genética, Universidad de Córdoba, Córdoba, Spain; Maimónides Biomedical Research Institute of Córdoba (IMIBIC), GA-14 Research Group, Córdoba, Spain
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Karan B, Mahapatra S, Sahu SS, Pandey DM, Chakravarty S. Computational models for prediction of protein-protein interaction in rice and Magnaporthe grisea. FRONTIERS IN PLANT SCIENCE 2023; 13:1046209. [PMID: 36816487 PMCID: PMC9929577 DOI: 10.3389/fpls.2022.1046209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 12/28/2022] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease. METHODS In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale. RESULTS AND DISCUSSION A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.
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Affiliation(s)
- Biswajit Karan
- Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, India
| | - Satyajit Mahapatra
- Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, India
| | - Sitanshu Sekhar Sahu
- Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, India
| | - Dev Mani Pandey
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Ranchi, India
| | - Sumit Chakravarty
- Department of Electrical and Computer Engineering, Kennesaw State University, Kennesaw, GA, United States
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Swain A, Pan A. Protein Therapeutic Target Candidates Against Acinetobacter baumannii, a Pathogen of Concern to Planetary Health: A Network-Based Integrative Omics Drug Discovery Approach. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:62-74. [PMID: 36735546 DOI: 10.1089/omi.2022.0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Acinetobacter baumannii, an opportunistic gram-negative pathogen responsible for several nosocomial infections, has developed resistance to various antibiotics. Proteins involved in the two-component system (TCS), virulence, and antibiotic resistance (AR), help this pathogen in regulating antibiotic susceptibility and virulence mechanisms. The present study reports a network-based integrative omics approach to drug discovery to identify key regulatory proteins as therapeutic candidates against A. baumannii. We collected data on the TCS, virulence, and AR proteins from various databases (P2CS, VFDB, ARDB, and PAIDB), which were subjected to network, host-pathogen, and gene expression data analysis. Network analysis identified 43 hubs, and 10 proteins were found to be interacting with human proteins associated with vital pathways. Of the 53 (43 + 10) pathogen proteins, 46 had no orthologs in the human host. Twelve proteins, namely, RpfC, Wzc, OmpR, EnvZ, BfmS, PilG, histidine kinase, ABC 3 transport family protein, outer membrane porin OprD family, CsuD, Pgm, and LpxA, were differentially expressed in the resistant strain. We propose these proteins as key regulators that warrant evaluation as therapeutic target candidates in the future. Furthermore, structure prediction of ABC 3 transport family protein was performed as a case study. The findings from this study are poised to facilitate and inform drug discovery and development against A. baumannii.
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Affiliation(s)
- Aishwarya Swain
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India
| | - Archana Pan
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India
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Ozdemir ES, Nussinov R. Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions. Front Oncol 2023; 13:1061595. [PMID: 36910650 PMCID: PMC9997845 DOI: 10.3389/fonc.2023.1061595] [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: 10/04/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.
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Affiliation(s)
- Emine Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Durairaj J, de Ridder D, van Dijk AD. Beyond sequence: Structure-based machine learning. Comput Struct Biotechnol J 2022; 21:630-643. [PMID: 36659927 PMCID: PMC9826903 DOI: 10.1016/j.csbj.2022.12.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/31/2022] Open
Abstract
Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.
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Affiliation(s)
- Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Aalt D.J. van Dijk
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
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Kumar N, Mishra B, Mukhtar MS. A pipeline of integrating transcriptome and interactome to elucidate central nodes in host-pathogens interactions. STAR Protoc 2022; 3:101608. [PMID: 35990739 PMCID: PMC9386103 DOI: 10.1016/j.xpro.2022.101608] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Investigating the complexity of host-pathogen interactions is challenging. Here, we outline a pipeline to identify important proteins and signaling molecules in human-viral interactomes. Firstly, we curate a comprehensive human interactome. Subsequently, we infer viral targets and transcriptome-specific human interactomes (VTTSHI) for papillomavirus and herpes viruses by integrating viral targets and transcriptome data. Finally, we reveal the common and shared nodes and pathways in viral pathogenesis following network topology and pathway enrichment analyses. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2020). Protocol for integrative analysis of transcriptome and proteome network data Network subgroup enrichment of two host-pathogen interaction networks Preprocessing of data and heterogeneous network integration
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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Valiente G. The Landscape of Virus-Host Protein–Protein Interaction Databases. Front Microbiol 2022; 13:827742. [PMID: 35910656 PMCID: PMC9335289 DOI: 10.3389/fmicb.2022.827742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/17/2022] [Indexed: 11/25/2022] Open
Abstract
Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.
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11
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Le TD, Nguyen PD, Korkin D, Thieu T. PHILM2Web: A high-throughput database of macromolecular host–pathogen interactions on the Web. Database (Oxford) 2022; 2022:6625823. [PMID: 35776535 PMCID: PMC9248916 DOI: 10.1093/database/baac042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 04/27/2022] [Accepted: 05/31/2022] [Indexed: 12/02/2022]
Abstract
During infection, the pathogen’s entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host–pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein–protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen–Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live
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Affiliation(s)
- Tuan-Dung Le
- Department of Computer Science, Oklahoma State University , Stillwater, OK, USA
| | - Phuong D Nguyen
- Department of Biochemistry and Molecular Biology, Oklahoma State University , Stillwater, OK, USA
| | - Dmitry Korkin
- Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute , Worcester, MA, USA
| | - Thanh Thieu
- Machine Learning Department, Moffitt Cancer Center and Research Institute , Tampa, FL, USA
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Hephzibah Cathryn R, Udhaya Kumar S, Younes S, Zayed H, George Priya Doss C. A review of bioinformatics tools and web servers in different microarray platforms used in cancer research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:85-164. [PMID: 35871897 DOI: 10.1016/bs.apcsb.2022.05.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.
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Affiliation(s)
- R Hephzibah Cathryn
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - S Udhaya Kumar
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Salma Younes
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - Hatem Zayed
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
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Integrated analysis of microbe-host interactions in Crohn’s disease reveals potential mechanisms of microbial proteins on host gene expression. iScience 2022; 25:103963. [PMID: 35479407 PMCID: PMC9035720 DOI: 10.1016/j.isci.2022.103963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 12/11/2021] [Accepted: 02/18/2022] [Indexed: 12/15/2022] Open
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Stryiński R, Mateos J, Carrera M, Jastrzębski JP, Bogacka I, Łopieńska-Biernat E. Tandem Mass Tagging (TMT) Reveals Tissue-Specific Proteome of L4 Larvae of Anisakis simplex s. s.: Enzymes of Energy and/or Carbohydrate Metabolism as Potential Drug Targets in Anisakiasis. Int J Mol Sci 2022; 23:ijms23084336. [PMID: 35457153 PMCID: PMC9027741 DOI: 10.3390/ijms23084336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 02/04/2023] Open
Abstract
Anisakis simplex s. s. is a parasitic nematode of marine mammals and causative agent of anisakiasis in humans. The cuticle and intestine of the larvae are the tissues most responsible for direct and indirect contact, respectively, of the parasite with the host. At the L4 larval stage, tissues, such as the cuticle and intestine, are fully developed and functional, in contrast to the L3 stage. As such, this work provides for the first time the tissue-specific proteome of A. simplex s. s. larvae in the L4 stage. Statistical analysis (FC ≥ 2; p-value ≤ 0.01) showed that 107 proteins were differentially regulated (DRPs) between the cuticle and the rest of the larval body. In the comparison between the intestine and the rest of the larval body at the L4 stage, 123 proteins were identified as DRPs. Comparison of the individual tissues examined revealed a total of 272 DRPs, with 133 proteins more abundant in the cuticle and 139 proteins more abundant in the intestine. Detailed functional analysis of the identified proteins was performed using bioinformatics tools. Glycolysis and the tricarboxylic acid cycle were the most enriched metabolic pathways by cuticular and intestinal proteins, respectively, in the L4 stage of A. simplex s. s. The presence of two proteins, folliculin (FLCN) and oxoglutarate dehydrogenase (OGDH), was confirmed by Western blot, and their tertiary structure was predicted and compared with other species. In addition, host–pathogen interactions were identified, and potential new allergens were predicted. The result of this manuscript shows the largest number of protein identifications to our knowledge using proteomics tools for different tissues of L4 larvae of A. simplex s. s. The identified tissue-specific proteins could serve as targets for new drugs against anisakiasis.
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Affiliation(s)
- Robert Stryiński
- Department of Biochemistry, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
- Correspondence: (R.S.); (M.C.); (E.Ł.-B.)
| | - Jesús Mateos
- Clinical Pharmacology Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, 15-706 A Coruña, Spain;
| | - Mónica Carrera
- Department of Food Technology, Marine Research Institute (IIM), Spanish National Research Council (CSIC), 36-208 Vigo, Spain
- Correspondence: (R.S.); (M.C.); (E.Ł.-B.)
| | - Jan Paweł Jastrzębski
- Department of Plant Physiology, Genetics and Biotechnology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland;
| | - Iwona Bogacka
- Department of Animal Anatomy and Physiology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland;
| | - Elżbieta Łopieńska-Biernat
- Department of Biochemistry, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
- Correspondence: (R.S.); (M.C.); (E.Ł.-B.)
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Deciphering the Host-Pathogen Interactome of the Wheat-Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat. Int J Mol Sci 2022; 23:ijms23052589. [PMID: 35269732 PMCID: PMC8910311 DOI: 10.3390/ijms23052589] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 02/09/2022] [Indexed: 02/05/2023] Open
Abstract
Common bunt, caused by two fungal species, Tilletia caries and Tilletia laevis, is one of the most potentially destructive diseases of wheat. Despite the availability of synthetic chemicals against the disease, organic agriculture relies greatly on resistant cultivars. Using two computational approaches—interolog and domain-based methods—a total of approximately 58 M and 56 M probable PPIs were predicted in T. aestivum–T. caries and T. aestivum–T. laevis interactomes, respectively. We also identified 648 and 575 effectors in the interactions from T. caries and T. laevis, respectively. The major host hubs belonged to the serine/threonine protein kinase, hsp70, and mitogen-activated protein kinase families, which are actively involved in plant immune signaling during stress conditions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the host proteins revealed significant GO terms (O-methyltransferase activity, regulation of response to stimulus, and plastid envelope) and pathways (NF-kappa B signaling and the MAPK signaling pathway) related to plant defense against pathogens. Subcellular localization suggested that most of the pathogen proteins target the host in the plastid. Furthermore, a comparison between unique T. caries and T. laevis proteins was carried out. We also identified novel host candidates that are resistant to disease. Additionally, the host proteins that serve as transcription factors were also predicted.
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Proteomic Profiling and In Silico Characterization of the Secretome of Anisakis simplex Sensu Stricto L3 Larvae. Pathogens 2022; 11:pathogens11020246. [PMID: 35215189 PMCID: PMC8879239 DOI: 10.3390/pathogens11020246] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/09/2022] [Accepted: 02/10/2022] [Indexed: 02/04/2023] Open
Abstract
Anisakis simplex sensu stricto (s.s.) L3 larvae are one of the major etiological factors of human anisakiasis, which is one of the most important foodborne parasitic diseases. Nevertheless, to date, Anisakis secretome proteins, with important functions in nematode pathogenicity and host-parasite interactions, have not been extensively explored. Therefore, the aim of this study was to identify and characterize the excretory-secretory (ES) proteins of A. simplex L3 larvae. ES proteins of A. simplex were subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, and the identified proteins were then analyzed using bioinformatics tools. A total of 158 proteins were detected. Detailed bioinformatic characterization of ES proteins was performed, including Gene Ontology (GO) analysis, identification of enzymes, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, protein family classification, secretory pathway prediction, and detection of essential proteins. Furthermore, of all detected ES proteins, 1 was identified as an allergen, which was Ani s 4, and 18 were potential allergens, most of which were homologs of nematode and arthropod allergens. Nine potential pathogenicity-related proteins were predicted, which were predominantly homologs of chaperones. In addition, predicted host-parasite interactions between the Anisakis ES proteins and both human and fish proteins were identified. In conclusion, this study represents the first global analysis of Anisakis ES proteins. The findings provide a better understanding of survival and invasion strategies of A. simplex L3 larvae.
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Lata KS, Kumar S, Vindal V, Patel S, Das J. A core and pan gene map of Leptospira genus and its interactions with human host. Microb Pathog 2021; 162:105347. [PMID: 34871726 DOI: 10.1016/j.micpath.2021.105347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 10/18/2021] [Accepted: 12/01/2021] [Indexed: 01/08/2023]
Abstract
Leptospira species are the etiological agent of an emerging zoonotic disease known as "Leptospirosis" that substantially affects both human health and economy across the globe. Despite the global importance of the disease, pathogenetic features, host-adaptation and proper diagnosis of this bacteria remains lacking. To accomplish these gaps, pan-genome of Leptospira genus was explored in the present study. The pan-genome of Leptospira genus was comprised of core (692) and accessory parts (softcore:1804, shell:6432, cloud:16,600). The functional analysis revealed the abundancy of "Translation, ribosomal structure and biogenesis" COG class in core-genes; whereas in accessory parts, genes involved in signal transduction was the most abundant. Furthermore, pathogen-host interaction (PHI) analysis of core and accessory proteins with human proteins showed the presence of a total of 599 and 510 interactions, respectively. There were eight hubs in core PHI network and five hubs in PHI network of accessory proteins. The human's proteins involved in these interactions were found functionally enriched in metabolic processes, responses to stimulus and immune system processes. Further, pan-genome based phylogeny separated the Leptospira genus in three major clades (belonging to P1, P2 and S) which relates with their pathogenicity level. Additionally, pathogenic and saprophytic clade specific genes of Leptospira have also been identified and functionally annotated for COG, KEGG and virulence factors. The results revealed the presence of 102 pathogenic and 215 saprophytic group specific gene clusters. The COG functional annotation of pathogen specific genes showed that defence mechanism followed by signal transduction mechanisms category were most significantly enriched COG categories; whereas in saprophytic group, signal transduction mechanisms was the most abundant COG, suggesting their role in adaptation and hence important for microbe's evolution and survival. In conclusion, this study provides a new insight of genomic features of Leptospira genus which may further be implemented for development of better control actions of the disease.
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Affiliation(s)
- Kumari Snehkant Lata
- Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University, Ahmedabad, 380009, India
| | - Swapnil Kumar
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India
| | - Vaibhav Vindal
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India
| | - Saumya Patel
- Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University, Ahmedabad, 380009, India.
| | - Jayashankar Das
- Centre for Genomics and Biomedical Informatics, IMS and SUM Hospital, Siksha "O" Anusandhan University (Deemed to Be University), Bhubaneswar, 751003, India.
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Kalra R, Tiwari D, Dkhar HK, Bhagyaraj E, Kumar R, Bhardwaj A, Gupta P. Host factors subverted by Mycobacterium tuberculosis: Potential targets for host directed therapy. Int Rev Immunol 2021; 42:43-70. [PMID: 34678117 DOI: 10.1080/08830185.2021.1990277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Despite new approaches in the diagnosis and treatment of tuberculosis (TB), it continues to be a major health burden. Several immunotherapies that potentiate the immune response have come up as adjuncts to drug therapies against drug resistant TB strains; however, there needs to be an urgent appraisal of host specific drug targets for improving their clinical management and to curtail disease progression. Presently, various host directed therapies (HDTs) exist (repurposed drugs, nutraceuticals, monoclonal antibodies and immunomodulatory agents), but these mostly address molecules that combat disease progression. AREAS COVERED The current review discusses major Mycobacterium tuberculosis (M. tuberculosis) survival paradigms inside the host and presents a plethora of host targets subverted by M. tuberculosis which can be further explored for future HDTs. The host factors unique to M. tuberculosis infection (in humans) have also been identified through an in-silico interaction mapping. EXPERT OPINION HDTs could become the next-generation adjunct therapies in order to counter antimicrobial resistance and virulence, as well as to reduce the duration of existing TB treatments. However, current scientific efforts are largely directed toward combatants rather than host molecules co-opted by M. tuberculosis for its survival. This might drive the immune system to a hyper-inflammatory condition; therefore, we emphasize that host factors subverted by M. tuberculosis, and their subsequent neutralization, must be considered for development of better HDTs.
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Affiliation(s)
- Rashi Kalra
- Department of Molecular Biology, CSIR-Institute of Microbial Technology, Chandigarh-160036, India
| | - Drishti Tiwari
- Department of Molecular Biology, CSIR-Institute of Microbial Technology, Chandigarh-160036, India
| | - Hedwin Kitdorlang Dkhar
- Department of Molecular Biology, CSIR-Institute of Microbial Technology, Chandigarh-160036, India
| | - Ella Bhagyaraj
- Department of Molecular Biology, CSIR-Institute of Microbial Technology, Chandigarh-160036, India
| | - Rakesh Kumar
- Bioinformatics Center, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Anshu Bhardwaj
- Bioinformatics Center, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Pawan Gupta
- Department of Molecular Biology, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
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Dong AY, Wang Z, Huang JJ, Song BA, Hao GF. Bioinformatic tools support decision-making in plant disease management. TRENDS IN PLANT SCIENCE 2021; 26:953-967. [PMID: 34039514 DOI: 10.1016/j.tplants.2021.05.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 02/10/2021] [Accepted: 05/01/2021] [Indexed: 06/12/2023]
Abstract
Food loss due to pathogens is a major concern in agriculture, requiring the need for advanced disease detection and prevention measures to minimize pathogen damage to plants. Novel bioinformatic tools have opened doors for the low-cost rapid identification of pathogens and prevention of disease. The number of these tools is growing fast and a comprehensive and comparative summary of these resources is currently lacking. Here, we review all current bioinformatic tools used to identify the mechanisms of pathogen pathogenicity, plant resistance protein identification, and the detection and treatment of plant disease. We compare functionality, data volume, data sources, performance, and applicability of all tools to provide a comprehensive toolbox for researchers in plant disease management.
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Affiliation(s)
- An-Yu Dong
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Zheng Wang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Jun-Jie Huang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Bao-An Song
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Ge-Fei Hao
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China.
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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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Comparative Genomics and Integrated Network Approach Unveiled Undirected Phylogeny Patterns, Co-mutational Hot Spots, Functional Cross Talk, and Regulatory Interactions in SARS-CoV-2. mSystems 2021; 6:6/1/e00030-21. [PMID: 33622851 PMCID: PMC8573956 DOI: 10.1128/msystems.00030-21] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has resulted in 92 million cases in a span of 1 year. The study focuses on understanding population-specific variations attributing its high rate of infections in specific geographical regions particularly in the United States. Rigorous phylogenomic network analysis of complete SARS-CoV-2 genomes (245) inferred five central clades named a (ancestral), b, c, d, and e (subtypes e1 and e2). Clade d and subclade e2 were found exclusively comprised of U.S. strains. Clades were distinguished by 10 co-mutational combinations in Nsp3, ORF8, Nsp13, S, Nsp12, Nsp2, and Nsp6. Our analysis revealed that only 67.46% of single nucleotide polymorphism (SNP) mutations were at the amino acid level. T1103P mutation in Nsp3 was predicted to increase protein stability in 238 strains except for 6 strains which were marked as ancestral type, whereas co-mutation (P409L and Y446C) in Nsp13 were found in 64 genomes from the United States highlighting its 100% co-occurrence. Docking highlighted mutation (D614G) caused reduction in binding of spike proteins with angiotensin-converting enzyme 2 (ACE2), but it also showed better interaction with the TMPRSS2 receptor contributing to high transmissibility among U.S. strains. We also found host proteins, MYO5A, MYO5B, and MYO5C, that had maximum interaction with viral proteins (nucleocapsid [N], spike [S], and membrane [M] proteins). Thus, blocking the internalization pathway by inhibiting MYO5 proteins which could be an effective target for coronavirus disease 2019 (COVID-19) treatment. The functional annotations of the host-pathogen interaction (HPI) network were found to be closely associated with hypoxia and thrombotic conditions, confirming the vulnerability and severity of infection. We also screened CpG islands in Nsp1 and N conferring the ability of SARS-CoV-2 to enter and trigger zinc antiviral protein (ZAP) activity inside the host cell. IMPORTANCE In the current study, we presented a global view of mutational pattern observed in SARS-CoV-2 virus transmission. This provided a who-infect-whom geographical model since the early pandemic. This is hitherto the most comprehensive comparative genomics analysis of full-length genomes for co-mutations at different geographical regions especially in U.S. strains. Compositional structural biology results suggested that mutations have a balance of opposing forces affecting pathogenicity suggesting that only a few mutations are effective at the translation level. Novel HPI analysis and CpG predictions elucidate the proof of concept of hypoxia and thrombotic conditions in several patients. Thus, the current study focuses the understanding of population-specific variations attributing a high rate of SARS-CoV-2 infections in specific geographical regions which may eventually be vital for the most severely affected countries and regions for sharp development of custom-made vindication strategies.
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Saha D, Kundu S. A Molecular Interaction Map of Klebsiella pneumoniae and Its Human Host Reveals Potential Mechanisms of Host Cell Subversion. Front Microbiol 2021; 12:613067. [PMID: 33679637 PMCID: PMC7930833 DOI: 10.3389/fmicb.2021.613067] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/11/2021] [Indexed: 12/13/2022] Open
Abstract
Klebsiella pneumoniae is a leading cause of pneumonia and septicemia across the world. The rapid emergence of multidrug-resistant K. pneumoniae strains necessitates the discovery of effective drugs against this notorious pathogen. However, there is a dearth of knowledge on the mechanisms by which this deadly pathogen subverts host cellular machinery. To fill this knowledge gap, our study attempts to identify the potential mechanisms of host cell subversion by building a K. pneumoniae-human interactome based on rigorous computational methodology. The putative host targets inferred from the predicted interactome were found to be functionally enriched in the host's immune surveillance system and allied functions like apoptosis, hypoxia, etc. A multifunctionality-based scoring system revealed P53 as the most multifunctional protein among host targets accompanied by HIF1A and STAT1. Moreover, mining of host protein-protein interaction (PPI) network revealed that host targets interact among themselves to form a network (TTPPI), where P53 and CDC5L occupy a central position. The TTPPI is composed of several inter complex interactions which indicate that K. pneumoniae might disrupt functional coordination between these protein complexes through targeting of P53 and CDC5L. Furthermore, we identified four pivotal K. pneumoniae-targeted transcription factors (TTFs) that are part of TTPPI and are involved in generating host's transcriptional response to K. pneumoniae-mediated sepsis. In a nutshell, our study identifies some of the pivotal molecular targets of K. pneumoniae which primarily correlate to the physiological response of host during K. pneumoniae-mediated sepsis.
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Affiliation(s)
- Deeya Saha
- Department of Biophysics, Molecular Biology and Bioinformatics, Faculty of Science, University of Calcutta, Kolkata, India
| | - Sudip Kundu
- Department of Biophysics, Molecular Biology and Bioinformatics, Faculty of Science, University of Calcutta, Kolkata, India
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Internetwork connectivity of molecular networks across species of life. Sci Rep 2021; 11:1168. [PMID: 33441907 PMCID: PMC7806680 DOI: 10.1038/s41598-020-80745-9] [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: 09/18/2020] [Accepted: 12/23/2020] [Indexed: 01/29/2023] Open
Abstract
Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene and protein-protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN-PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.
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Verma A, Sharda S, Rathi B, Somvanshi P, Pandey BD. Elucidating potential molecular signatures through host-microbe interactions for reactive arthritis and inflammatory bowel disease using combinatorial approach. Sci Rep 2020; 10:15131. [PMID: 32934294 PMCID: PMC7492238 DOI: 10.1038/s41598-020-71674-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 07/06/2020] [Indexed: 02/08/2023] Open
Abstract
Reactive Arthritis (ReA), a rare seronegative inflammatory arthritis, lacks exquisite classification under rheumatic autoimmunity. ReA is solely established using differential clinical diagnosis of the patient cohorts, where pathogenic triggers linked to enteric and urogenital microorganisms e.g. Salmonella, Shigella, Yersinia, Campylobacter, Chlamydia have been reported. Inflammatory Bowel Disease (IBD), an idiopathic enteric disorder co-evolved and attuned to present gut microbiome dysbiosis, can be correlated to the genesis of enteropathic arthropathies like ReA. Gut microbes symbolically modulate immune system homeostasis and are elementary for varied disease patterns in autoimmune disorders. The gut-microbiota axis structured on the core host-microbe interactions execute an imperative role in discerning the etiopathogenesis of ReA and IBD. This study predicts the molecular signatures for ReA with co-evolved IBD through the enveloped host-microbe interactions and microbe-microbe 'interspecies communication', using synonymous gene expression data for selective microbes. We have utilized a combinatorial approach that have concomitant in-silico work-pipeline and experimental validation to corroborate the findings. In-silico analysis involving text mining, metabolic network reconstruction, simulation, filtering, host-microbe interaction, docking and molecular mimicry studies results in robust drug target/s and biomarker/s for co-evolved IBD and ReA. Cross validation of the target/s or biomarker/s was done by targeted gene expression analysis following a non-probabilistic convenience sampling. Studies were performed to substantiate the host-microbe disease network consisting of protein-marker-symptom/disease-pathway-drug associations resulting in possible identification of vital drug targets, biomarkers, pathways and inhibitors for IBD and ReA.Our study identified Na(+)/H(+) anti-porter (NHAA) and Kynureninase (KYNU) to be robust early and essential host-microbe interacting targets for IBD co-evolved ReA. Other vital host-microbe interacting genes, proteins, pathways and drugs include Adenosine Deaminase (ADA), Superoxide Dismutase 2 (SOD2), Catalase (CAT), Angiotensin I Converting Enzyme (ACE), carbon metabolism (folate biosynthesis) and methotrexate. These can serve as potential prognostic/theranostic biomarkers and signatures that can be extrapolated to stratify ReA and related autoimmunity patient cohorts for further pilot studies.
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Affiliation(s)
- Anukriti Verma
- Amity Institute of Biotechnology, J-3 Block, Amity University Campus, Sector-125, Noida, UP, 201313, India
| | - Shivani Sharda
- Amity Institute of Biotechnology, J-3 Block, Amity University Campus, Sector-125, Noida, UP, 201313, India.
| | - Bhawna Rathi
- Amity Institute of Biotechnology, J-3 Block, Amity University Campus, Sector-125, Noida, UP, 201313, India
| | - Pallavi Somvanshi
- Department of Biotechnology, TERI School of Advanced Studies, 10, Institutional Area, Vasant Kunj, New Delhi, 110070, India
| | - Bimlesh Dhar Pandey
- Fortis Hospital, B-22, Sector 62, Gautam Buddh Nagar, Noida, Uttar Pradesh, 201301, India
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Chen H, Li F, Wang L, Jin Y, Chi CH, Kurgan L, Song J, Shen J. Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions. Brief Bioinform 2020; 22:5847611. [PMID: 32459334 DOI: 10.1093/bib/bbaa068] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/31/2020] [Accepted: 04/01/2020] [Indexed: 12/11/2022] Open
Abstract
In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.
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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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Pranavathiyani G, Prava J, Rajeev AC, Pan A. Novel Target Exploration from Hypothetical Proteins of Klebsiella pneumoniae MGH 78578 Reveals a Protein Involved in Host-Pathogen Interaction. Front Cell Infect Microbiol 2020; 10:109. [PMID: 32318354 PMCID: PMC7146069 DOI: 10.3389/fcimb.2020.00109] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 02/28/2020] [Indexed: 11/13/2022] Open
Abstract
The opportunistic pathogen Klebsiella pneumoniae is a causative agent of several hospital-acquired infections. It has become resistant to a wide range of currently available antibiotics, leading to high mortality rates among patients; this has further led to a demand for novel therapeutic intervention to treat such infections. Using a series of in silico analyses, the present study aims to explore novel drug/vaccine candidates from the hypothetical proteins of K. pneumoniae. A total of 540 proteins were found to be hypothetical in this organism. Analysis of these 540 hypothetical proteins revealed 30 pathogen-specific proteins essential for pathogen survival. A motifs/domain family analysis, similarity search against known proteins, gene ontology, and protein–protein interaction analysis of the shortlisted 30 proteins led to functional assignment for 17 proteins. They were mainly cataloged as enzymes, lipoproteins, stress-induced proteins, transporters, and other proteins (viz., two-component proteins, skeletal proteins and toxins). Among the annotated proteins, 16 proteins, located in the cytoplasm, periplasm, and inner membrane, were considered as potential drug targets, and one extracellular protein was considered as a vaccine candidate. A druggability analysis indicated that the identified 17 drug/vaccine candidates were “novel”. Furthermore, a host–pathogen interaction analysis of these identified target candidates revealed a betaine/carnitine/choline transporters (BCCT) family protein showing interactions with five host proteins. Structure prediction and validation were carried out for this protein, which could aid in structure-based inhibitor design.
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Affiliation(s)
- G Pranavathiyani
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India
| | - Jyoti Prava
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India
| | - Athira C Rajeev
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India
| | - Archana Pan
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India
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Gemovic B, Sumonja N, Davidovic R, Perovic V, Veljkovic N. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. Curr Med Chem 2019; 26:3890-3910. [PMID: 29446725 DOI: 10.2174/0929867325666180214113704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/14/2017] [Accepted: 01/29/2018] [Indexed: 01/04/2023]
Abstract
BACKGROUND The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.
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Affiliation(s)
- Branislava Gemovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Vladimir Perovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
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Ahmed I, Witbooi P, Christoffels A. Prediction of human-Bacillus anthracis protein-protein interactions using multi-layer neural network. Bioinformatics 2019; 34:4159-4164. [PMID: 29945178 PMCID: PMC6289132 DOI: 10.1093/bioinformatics/bty504] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 06/24/2018] [Indexed: 12/22/2022] Open
Abstract
Motivation Triplet amino acids have successfully been included in feature selection to predict human-HPV protein-protein interactions (PPI). The utility of supervised learning methods is curtailed due to experimental data not being available in sufficient quantities. Improvements in machine learning techniques and features selection will enhance the study of PPI between host and pathogen. Results We present a comparison of a neural network model versus SVM for prediction of host-pathogen PPI based on a combination of features including: amino acid quadruplets, pairwise sequence similarity, and human interactome properties. The neural network and SVM were implemented using Python Sklearn library. The neural network model using quadruplet features and other network features outperformance the SVM model. The models are tested against published predictors and then applied to the human-B.anthracis case. Gene ontology term enrichment analysis identifies immunology response and regulation as functions of interacting proteins. For prediction of Human-viral PPI, our model (neural network) is a significant improvement in overall performance compared to a predictor using the triplets feature and achieves a good accuracy in predicting human-B.anthracis PPI. Availability and implementation All code can be downloaded from ftp://ftp.sanbi.ac.za/machine_learning/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ibrahim Ahmed
- South African National Bioinformatics Institute, South African MRC Bioinformatics Unit
| | - Peter Witbooi
- Department of Mathematics and Applied Mathematics, University of the Western Cape, Bellville, South Africa
| | - Alan Christoffels
- South African National Bioinformatics Institute, South African MRC Bioinformatics Unit
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Gabaldón T. Recent trends in molecular diagnostics of yeast infections: from PCR to NGS. FEMS Microbiol Rev 2019; 43:517-547. [PMID: 31158289 PMCID: PMC8038933 DOI: 10.1093/femsre/fuz015] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 05/31/2019] [Indexed: 12/29/2022] Open
Abstract
The incidence of opportunistic yeast infections in humans has been increasing over recent years. These infections are difficult to treat and diagnose, in part due to the large number and broad diversity of species that can underlie the infection. In addition, resistance to one or several antifungal drugs in infecting strains is increasingly being reported, severely limiting therapeutic options and showcasing the need for rapid detection of the infecting agent and its drug susceptibility profile. Current methods for species and resistance identification lack satisfactory sensitivity and specificity, and often require prior culturing of the infecting agent, which delays diagnosis. Recently developed high-throughput technologies such as next generation sequencing or proteomics are opening completely new avenues for more sensitive, accurate and fast diagnosis of yeast pathogens. These approaches are the focus of intensive research, but translation into the clinics requires overcoming important challenges. In this review, we provide an overview of existing and recently emerged approaches that can be used in the identification of yeast pathogens and their drug resistance profiles. Throughout the text we highlight the advantages and disadvantages of each methodology and discuss the most promising developments in their path from bench to bedside.
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Affiliation(s)
- Toni Gabaldón
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
- ICREA, Pg Lluís Companys 23, 08010 Barcelona, Spain
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31
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Rozewicki J, Li S, Amada KM, Standley DM, Katoh K. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Res 2019; 47:W5-W10. [PMID: 31062021 PMCID: PMC6602451 DOI: 10.1093/nar/gkz342] [Citation(s) in RCA: 242] [Impact Index Per Article: 48.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/07/2019] [Accepted: 04/25/2019] [Indexed: 12/22/2022] Open
Abstract
Here, we describe a web server that integrates structural alignments with the MAFFT multiple sequence alignment (MSA) tool. For this purpose, we have prepared a web-based Database of Aligned Structural Homologs (DASH), which provides structural alignments at the domain and chain levels for all proteins in the Protein Data Bank (PDB), and can be queried interactively or by a simple REST-like API. MAFFT-DASH integration can be invoked with a single flag on either the web (https://mafft.cbrc.jp/alignment/server/) or command-line versions of MAFFT. In our benchmarks using 878 cases from the BAliBase, HomFam, OXFam, Mattbench and SISYPHUS datasets, MAFFT-DASH showed 10-20% improvement over standard MAFFT for MSA problems with weak similarity, in terms of Sum-of-Pairs (SP), a measure of how well a program succeeds at aligning input sequences in comparison to a reference alignment. When MAFFT alignments were supplemented with homologous sequences, further improvement was observed. Potential applications of DASH beyond MSA enrichment include functional annotation through detection of remote homology and assembly of template libraries for homology modeling.
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Affiliation(s)
- John Rozewicki
- Department of Genome Informatics, Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Systems Immunology Laboratory, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Songling Li
- Department of Genome Informatics, Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Systems Immunology Laboratory, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Karlou Mar Amada
- Systems Immunology Laboratory, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Daron M Standley
- Department of Genome Informatics, Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Systems Immunology Laboratory, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Kazutaka Katoh
- Department of Genome Informatics, Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Systems Immunology Laboratory, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
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Karthikeyan R, Gayathri P, Gunasekaran P, Jagannadham MV, Rajendhran J. Comprehensive proteomic analysis and pathogenic role of membrane vesicles of Listeria monocytogenes serotype 4b reveals proteins associated with virulence and their possible interaction with host. Int J Med Microbiol 2019; 309:199-212. [PMID: 30962079 DOI: 10.1016/j.ijmm.2019.03.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 03/19/2019] [Accepted: 03/25/2019] [Indexed: 02/07/2023] Open
Abstract
Membrane vesicles (MVs) are produced by various Gram positive and Gram negative pathogenic bacteria and play an important role in virulence. In this study, the membrane vesicles (MVs) of L. monocytogenes were isolated from the culture supernatant. High-resolution electron microscopy and dynamic light scattering analysis revealed that L. monocytogenes MVs are spherical with a diameter of 200 to 300 nm in size. Further, comprehensive proteomic analyses of MVs and whole cells of L. monocytogenes were performed using LC/MS/MS. A total of 1355 and 312 proteins were identified in the L. monocytogenes cells and MVs, respectively. We identified that 296 proteins are found in both whole cells, and MV proteome and 16 proteins were identified only in the MVs. Also, we have identified the virulence factors such as listeriolysin O (LLO), internalin B (InlB), autolysin, p60, NLP/P60 family protein, UPF0356 protein, and PLC-A in MVs. Computational prediction of host-MV interactions revealed a total of 1841 possible interactions with the host involving 99 MV proteins and 1513 host proteins. We elucidated the possible pathway that mediates internalization of L. monocytogenes MV to host cells and the subsequent pathogenesis mechanisms. The in vitro infection assays showed that the purified MVs could induce cytotoxicity in Caco-2 cells. Using endocytosis inhibitors, we demonstrated that MVs are internalized via actin-mediated endocytosis. These results suggest that L. monocytogenes MVs can interact with host cell and contribute to the pathogenesis of L. monocytogenes during infection.
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Affiliation(s)
- Raman Karthikeyan
- Department of Genetics, School of Biological Sciences, Madurai Kamaraj University, Madurai, 625021, Tamil Nadu, India
| | - Pratapa Gayathri
- CSIR - Centre for Cellular and Molecular Biology, Tarnaka, Hyderabad, 500007, India
| | | | | | - Jeyaprakash Rajendhran
- Department of Genetics, School of Biological Sciences, Madurai Kamaraj University, Madurai, 625021, Tamil Nadu, India.
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Kalindamar S, Lu J, Abdelhamed H, Tekedar HC, Lawrence ML, Karsi A. Transposon mutagenesis and identification of mutated genes in growth-delayed Edwardsiella ictaluri. BMC Microbiol 2019; 19:55. [PMID: 30849940 PMCID: PMC6408766 DOI: 10.1186/s12866-019-1429-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 02/27/2019] [Indexed: 01/23/2023] Open
Abstract
Background Edwardsiella ictaluri is a Gram-negative facultative intracellular anaerobe and the etiologic agent of enteric septicemia of channel catfish (ESC). To the catfish industry, ESC is a devastating disease due to production losses and treatment costs. Identification of virulence mechanisms of E. ictaluri is critical to developing novel therapeutic approaches for the disease. Here, we report construction of a transposon insertion library and identification of mutated genes in growth-delayed E. ictaluri colonies. We also provide safety and efficacy of transposon insertion mutants in catfish. Results An E. ictaluri transposon insertion library with 45,000 transposants and saturating 30.92% of the TA locations present in the E. ictaluri genome was constructed. Transposon end mapping of 250 growth-delayed E. ictaluri colonies and bioinformatic analysis of sequences revealed 56 unique E. ictaluri genes interrupted by the MAR2xT7 transposon, which are involved in metabolic and cellular processes and mostly localized in the cytoplasm or cytoplasmic membrane. Of the 56 genes, 30 were associated with bacterial virulence. Safety and vaccine efficacy testing of 19 mutants showed that mutants containing transposon insertions in hypothetical protein (Eis::004), and Fe-S cluster assembly protein (IscX, Eis::039), sulfurtransferase (TusA, Eis::158), and universal stress protein A (UspA, Eis::194) were safe and provided significant protection (p < 0.05) against wild-type E. ictaluri. Conclusions The results indicate that random transposon mutagenesis causing growth-delayed phenotype results in identification bacterial virulence genes, and attenuated strains with transposon interrupted virulence genes could be used as vaccine to activate fish immune system.
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Affiliation(s)
- Safak Kalindamar
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA
| | - Jingjun Lu
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA
| | - Hossam Abdelhamed
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA
| | - Hasan C Tekedar
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA
| | - Mark L Lawrence
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA
| | - Attila Karsi
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA.
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Abstract
Recent research increasingly shows the relevance of network based approaches for our understanding of biological systems. Analyzing human protein interaction networks, we determined collective influencers (CI), defined as network nodes that damage the integrity of the underlying networks to the utmost degree. We found that CI proteins were enriched with essential, regulatory, signaling and disease genes as well as drug targets, indicating their biological significance. Also by focusing on different organisms, we found that CI proteins had a penchant to be evolutionarily conserved as CI proteins, indicating the fundamental role that collective influencers in protein interaction networks plays for our understanding of regulation, diseases and evolution.
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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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Bencurova E, Gupta SK, Oskoueian E, Bhide M, Dandekar T. Omics and bioinformatics applied to vaccine development against Borrelia. Mol Omics 2018; 14:330-340. [PMID: 30113617 DOI: 10.1039/c8mo00130h] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Borrelia burgdorferi is an extracellular spirochete that causes Lyme disease. Currently, no effective vaccine is available for humans and animals except for dogs. In the present study, an extensive bioinformatics pipeline was established to predict new candidates that can be used for vaccine development including building the protein-protein interaction network based on orthologues of experimentally verified protein-protein interaction networks, elucidation of the proteins involved in the immune response, selection of the topologically-interesting proteins and their prioritization based on their antigenicity. Proteomic network analysis yielded an interactome network with 120 nodes with 97 interactions. Proteins were selected to obtain a subnet containing only the borrelial membrane proteins and immune-related host proteins. This strategy resulted in the selection of 15 borrelial targets, which were subjected to extensive bioinformatics analysis to predict their antigenic properties. Based on the strategy applied in this study the proteins encoded by erpX (ErpX proteins, UniProt ID: H7C7L6), erpL (ErpL protein, UniProt ID: H7C7M3) and erpY (ErpY protein, UniProt ID: Q9S0D9) are suggested as a novel set of vaccine targets to control Lyme disease. Moreover, five different tools were used to validate their antigenicity regarding B-cells. The combination of all these proteins in a vaccine should allow improved protection against Borrelia infection.
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Affiliation(s)
- Elena Bencurova
- Department of Bioinformatics, Biocenter, Am Hubland, D-97074 Wuerzburg, Germany.
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A new sequence based encoding for prediction of host-pathogen protein interactions. Comput Biol Chem 2018; 78:170-177. [PMID: 30553999 DOI: 10.1016/j.compbiolchem.2018.12.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 08/23/2018] [Accepted: 12/01/2018] [Indexed: 12/22/2022]
Abstract
Pathogen-host interactions are very important to figure out the infection process at the molecular level, where pathogen proteins physically bind to human proteins to manipulate critical biological processes in the host cell. Data scarcity and data unavailability are two major problems for computational approaches in the prediction of pathogen-host interactions. Developing a computational method to predict pathogen-host interactions with high accuracy, based on protein sequences alone, is of great importance because it can eliminate these problems. In this study, we propose a novel and robust sequence based feature extraction method, named Location Based Encoding, to predict pathogen-host interactions with machine learning based algorithms. In this context, we use Bacillus Anthracis and Yersinia Pestis data sets as the pathogen organisms and human proteins as the host model to compare our method with sequence based protein encoding methods, which are widely used in the literature, namely amino acid composition, amino acid pair, and conjoint triad. We use these encoding methods with decision trees (Random Forest, j48), statistical (Bayesian Networks, Naive Bayes), and instance based (kNN) classifiers to predict pathogen-host interactions. We conduct different experiments to evaluate the effectiveness of our method. We obtain the best results among all the experiments with RF classifier in terms of F1, accuracy, MCC, and AUC.
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Chellapandi P, Bharathi M, Sangavai C, Prathiviraj R. Methanobacterium formicicum as a target rumen methanogen for the development of new methane mitigation interventions: A review. Vet Anim Sci 2018; 6:86-94. [PMID: 32734058 PMCID: PMC7386643 DOI: 10.1016/j.vas.2018.09.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 08/29/2018] [Accepted: 09/12/2018] [Indexed: 12/18/2022] Open
Abstract
Methanobacterium formicicum (Methanobacteriaceae family) is an endosymbiotic methanogenic Archaean found in the digestive tracts of ruminants and elsewhere. It has been significantly implicated in global CH4 emission during enteric fermentation processes. In this review, we discuss current genomic and metabolic aspects of this microorganism for the purpose of the discovery of novel veterinary therapeutics. This microorganism encompasses a typical H2 scavenging system, which facilitates a metabolic symbiosis across the H2 producing cellulolytic bacteria and fumarate reducing bacteria. To date, five genome-scale metabolic models (iAF692, iMG746, iMB745, iVS941 and iMM518) have been developed. These metabolic reconstructions revealed the cellular and metabolic behaviors of methanogenic archaea. The characteristics of its symbiotic behavior and metabolic crosstalk with competitive rumen anaerobes support understanding of the physiological function and metabolic fate of shared metabolites in the rumen ecosystem. Thus, systems biological characterization of this microorganism may provide a new insight to realize its metabolic significance for the development of a healthy microbiota in ruminants. An in-depth knowledge of this microorganism may allow us to ensure a long term sustainability of ruminant-based agriculture.
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Affiliation(s)
- P Chellapandi
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620 024, India
| | - M Bharathi
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620 024, India
| | - C Sangavai
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620 024, India
| | - R Prathiviraj
- Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu 620 024, India
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Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018; 23:E1963. [PMID: 30082644 PMCID: PMC6222862 DOI: 10.3390/molecules23081963] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/03/2018] [Accepted: 08/04/2018] [Indexed: 12/14/2022] Open
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
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Affiliation(s)
- Stephani Joy Y Macalino
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Shaherin Basith
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Nina Abigail B Clavio
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Hyerim Chang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
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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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Devkota P, Danzi MC, Wuchty S. Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets. PLoS One 2018; 13:e0197595. [PMID: 29795705 PMCID: PMC5967884 DOI: 10.1371/journal.pone.0197595] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 05/04/2018] [Indexed: 11/18/2022] Open
Abstract
The availability of large-scale screens of host-virus interaction interfaces enabled the topological analysis of viral protein targets of the host. In particular, host proteins that bind viral proteins are generally hubs and proteins with high betweenness centrality. Recently, other topological measures were introduced that a virus may tap to infect a host cell. Utilizing experimentally determined sets of human protein targets from Herpes, Hepatitis, HIV and Influenza, we pooled molecular interactions between proteins from different pathway databases. Apart from a protein's degree and betweenness centrality, we considered a protein's pathway participation, ability to topologically control a network and protein PageRank index. In particular, we found that proteins with increasing values of such measures tend to accumulate viral targets and distinguish viral targets from non-targets. Furthermore, all such topological measures strongly correlate with the occurrence of a given protein in different pathways. Building a random forest classifier that is based on such topological measures, we found that protein PageRank index had the highest impact on the classification of viral (non-)targets while proteins' ability to topologically control an interaction network played the least important role.
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Affiliation(s)
- Prajwal Devkota
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, United States of America
| | - Matt C. Danzi
- The Miami Project to Cure Paralysis, Miller School of Medicine, University of Miami, Miami, FL, United States of America
- Center for Computational Science, Univ. of Miami, Coral Gables, FL, United States of America
| | - Stefan Wuchty
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, United States of America
- Center for Computational Science, Univ. of Miami, Coral Gables, FL, United States of America
- Dept. of Biology, Univ. of Miami, Coral Gables, FL, United States of America
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States of America
- * E-mail:
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Parvati Sai Arun PV, Miryala SK, Rana A, Kurukuti S, Akhter Y, Yellaboina S. System-wide coordinates of higher order functions in host-pathogen environment upon Mycobacterium tuberculosis infection. Sci Rep 2018; 8:5079. [PMID: 29567998 PMCID: PMC5864717 DOI: 10.1038/s41598-018-22884-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Accepted: 02/28/2018] [Indexed: 01/16/2023] Open
Abstract
Molecular signatures and their interactions behind the successful establishment of infection of Mycobacterium tuberculosis (Mtb) inside macrophage are largely unknown. In this work, we present an inter-system scale atlas of the gene expression signatures, their interactions and higher order gene functions of macrophage-Mtb environment at the time of infection. We have carried out large-scale meta-analysis of previously published gene expression microarray studies andhave identified a ranked list of differentially expressed genes and their higher order functions in intracellular Mtb as well as the infected macrophage. Comparative analysis of gene expression signatures of intracellular Mtb with the in vitro dormant Mtb at different hypoxic and oxidative stress conditions led to the identification of the large number of Mtb functional groups, namely operons, regulons and pathways that were common and unique to the intracellular environment and dormancy state. Some of the functions that are specific to intracellular Mtb are cholesterol degradation and biosynthesis of immunomodulatory phenolic compounds. The molecular signatures we have identified to be involved in adaptation to different stress conditions in macrophage environment may be critical for designing therapeutic interventions against tuberculosis. And, our approach may be broadly applicable for investigating other host-pathogen interactions.
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Affiliation(s)
| | - Sravan Kumar Miryala
- IOB-YU Centre for Systems Biology and Molecular Medicine, Yenepoya Research Centre Yenepoya University, Mangalore, Karnataka, India
| | - Aarti Rana
- Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, India
| | - Sreenivasulu Kurukuti
- Department of Animal Biology, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, Uttar Pradesh, 226025, India
| | - Sailu Yellaboina
- IOB-YU Centre for Systems Biology and Molecular Medicine, Yenepoya Research Centre Yenepoya University, Mangalore, Karnataka, India.
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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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Felgueiras J, Silva JV, Fardilha M. Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools. J Proteomics 2018; 171:127-140. [DOI: 10.1016/j.jprot.2017.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/26/2017] [Accepted: 05/13/2017] [Indexed: 02/02/2023]
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Abstract
Molecular interaction databases collect, organize, and enable the analysis of the increasing amounts of molecular interaction data being produced and published as we move towards a more complete understanding of the interactomes of key model organisms. The organization of these data in a structured format supports analyses such as the modeling of pairwise relationships between interactors into interaction networks and is a powerful tool for understanding the complex molecular machinery of the cell. This chapter gives an overview of the principal molecular interaction databases, in particular the IMEx databases, and their curation policies, use of standardized data formats and quality control rules. Special attention is given to the MIntAct project, in which IntAct and MINT joined forces to create a single resource to improve curation and software development efforts. This is exemplified as a model for the future of molecular interaction data collation and dissemination.
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46
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Yue J, Zhang D, Ban R, Ma X, Chen D, Li G, Liu J, Wisniewski M, Droby S, Liu Y. PCPPI: a comprehensive database for the prediction of Penicillium-crop protein-protein interactions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:3053440. [PMID: 28365721 PMCID: PMC5467543 DOI: 10.1093/database/baw170] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 12/15/2016] [Indexed: 12/20/2022]
Abstract
Penicillium expansum , the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium -crop interaction is a multifaceted process and mediated by pathogen- and host-derived proteins. Identification and characterization of the inter-species protein-protein interactions (PPIs) are fundamental to elucidating the molecular mechanisms underlying infection processes between P. expansum and plant crops. Here, we have developed PCPPI, the Penicillium -Crop Protein-Protein Interactions database, which is constructed based on the experimentally determined orthologous interactions in pathogen-plant systems and available domain-domain interactions (DDIs) in each PPI. Thus far, it stores information on 9911 proteins, 439 904 interactions and seven host species, including apple, kiwifruit, maize, pear, rice, strawberry and tomato. Further analysis through the gene ontology (GO) annotation indicated that proteins with more interacting partners tend to execute the essential function. Significantly, semantic statistics of the GO terms also provided strong support for the accuracy of our predicted interactions in PCPPI. We believe that all the PCPPI datasets are helpful to facilitate the study of pathogen-crop interactions and freely available to the research community. Database URL : http://bdg.hfut.edu.cn/pcppi/index.html.
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Affiliation(s)
- Junyang Yue
- College of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China.,Ministry of Education Key Laboratory for Bio-resource and Eco-environment, College of Life Science and State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China
| | - Danfeng Zhang
- College of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China
| | - Rongjun Ban
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Xiaojing Ma
- School of Medical Engineering, Hefei University of Technology, Hefei 230009, China
| | - Danyang Chen
- College of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China
| | - Guangwei Li
- College of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China
| | - Jia Liu
- College of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China
| | - Michael Wisniewski
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Kearneysville, WV 25430, USA
| | - Samir Droby
- Agricultural Research Organization (ARO), The Volcani Center, 50250 Bet Dagan, Israel
| | - Yongsheng Liu
- College of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China.,Ministry of Education Key Laboratory for Bio-resource and Eco-environment, College of Life Science and State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China
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Ma S, Song Q, Tao H, Harrison A, Wang S, Liu W, Lin S, Zhang Z, Ai Y, He H. Prediction of protein–protein interactions between fungus (Magnaporthe grisea) and rice (Oryza sativa L.). Brief Bioinform 2017; 20:448-456. [DOI: 10.1093/bib/bbx132] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 09/15/2017] [Indexed: 12/13/2022] Open
Affiliation(s)
- Shiwei Ma
- College of Life Sciences, Fujian Agriculture and Forestry University, China
| | - Qi Song
- College of Life Sciences, Fujian Agriculture and Forestry University, China
| | - Huan Tao
- College of Life Sciences, Fujian Agriculture and Forestry University, China
| | - Andrew Harrison
- Department of Mathematical Sciences, University of Essex, UK
| | - Shaobo Wang
- College of Life Sciences, Fujian Agriculture and Forestry University, China
| | - Wei Liu
- College of Life Sciences, Fujian Agriculture and Forestry University, China
| | - Shoukai Lin
- Fujian Provincial Key Laboratory of Ecology-toxicological Effects and Control for Emerging Contaminants, Putian University
| | - Ziding Zhang
- College of Biological Sciences, China Agriculture University, China
| | - Yufang Ai
- College of Life Sciences, Fujian Agriculture and Forestry University, China
| | - Huaqin He
- College of Life Sciences, Fujian Agriculture and Forestry University, China
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Abstract
Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.
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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, United States of America
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, United States of America
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, United States of America
- Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:3-13. [DOI: 10.1016/j.pbiomolbio.2016.10.002] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Revised: 10/15/2016] [Accepted: 10/19/2016] [Indexed: 12/22/2022]
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50
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Voter AF, Keck JL. Development of Protein-Protein Interaction Inhibitors for the Treatment of Infectious Diseases. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2017; 111:197-222. [PMID: 29459032 DOI: 10.1016/bs.apcsb.2017.07.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Protein-protein interaction (PPI) inhibitors are a rapidly expanding class of therapeutics. Recent advances in our understanding of PPIs and success of early examples of PPI inhibitors demonstrate the feasibility of targeting PPIs. This review summarizes the techniques used for the discovery and optimization of a diverse set PPI inhibitors, focusing on the development of PPI inhibitors as new antibacterial and antiviral agents. We close with a summary of the advances responsible for making PPI inhibitors realistic targets for therapeutic intervention and brief outlook of the field.
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Affiliation(s)
- Andrew F Voter
- University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - James L Keck
- University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.
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