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Bernardini R, Tengattini S, Li Z, Piubelli L, Bavaro T, Modolea AB, Mattei M, Conti P, Marini S, Zhang Y, Pollegioni L, Temporini C, Terreni M. Effect of glycosylation on the affinity of the MTB protein Ag85B for specific antibodies: towards the design of a dual-acting vaccine against tuberculosis. Biol Direct 2024; 19:11. [PMID: 38268026 PMCID: PMC10809592 DOI: 10.1186/s13062-024-00454-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/18/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND To create a dual-acting vaccine that can fight against tuberculosis, we combined antigenic arabino-mannan analogues with the Ag85B protein. To start the process, we studied the impact of modifying different parts of the Ag85B protein on its ability to be recognized by antibodies. RESULTS Through our research, we discovered that three modified versions of the protein, rAg85B-K30R, rAg85B-K282R, and rAg85B-K30R/K282R, retained their antibody reactivity in healthy individuals and those with tuberculosis. To further test the specificity of the sugar AraMan for AraMan antibodies, we used Human Serum Albumin glycosylated with AraMan-IME and Ara3Man-IME. Our findings showed that this specific sugar was fully and specifically modified. Bio-panning experiments revealed that patients with active tuberculosis exhibited a higher antibody response to Ara3Man, a sugar found in lipoarabinomannan (LAM), which is a major component of the mycobacterial cell wall. Bio-panning with anti-LAM plates could eliminate this increased response, suggesting that the enhanced Ara3Man response was primarily driven by antibodies targeting LAM. These findings highlight the importance of Ara3Man as an immunodominant epitope in LAM and support its role in eliciting protective immunity against tuberculosis. Further studies evaluated the effects of glycosylation on the antibody affinity of recombinant Ag85B and its variants. The results indicated that rAg85B-K30R/K282R, when conjugated with Ara3Man-IME, demonstrated enhanced antibody recognition compared to unconjugated or non-glycosylated versions. CONCLUSIONS Coupling Ara3Man to rAg85B-K30R/K282R could lead to the development of effective dual-acting vaccines against tuberculosis, stimulating protective antibodies against both AraMan and Ag85B, two key tuberculosis antigens.
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Affiliation(s)
- Roberta Bernardini
- Department of Translational Medicine, University of Tor Vergata, Via Montpellier 1, Rome, 00133, Italy.
- Interdepartmental Center for Comparative Medicine, Alternative Techniques and Aquaculture (CIMETA), University of Rome "Tor Vergata", Via Montpellier 1, Rome, 00133, Italy.
| | - Sara Tengattini
- Drug Sciences Department, University of Pavia, Viale Taramelli 12, Pavia, 27100, Italy.
| | - Zhihao Li
- Parisian Institute of Molecular Chemistry, Sorbonne University, UMR CNRS 8232, 4 Place Jussieu, Paris, 75005, France
| | - Luciano Piubelli
- Department of Biotechnology and Life Sciences, University of Insubria, via J.H. Dunant, 3, Insubria, Varese, 21100, Italy
| | - Teodora Bavaro
- Drug Sciences Department, University of Pavia, Viale Taramelli 12, Pavia, 27100, Italy
| | - Anamaria Bianca Modolea
- Interdepartmental Center for Comparative Medicine, Alternative Techniques and Aquaculture (CIMETA), University of Rome "Tor Vergata", Via Montpellier 1, Rome, 00133, Italy
| | - Maurizio Mattei
- Interdepartmental Center for Comparative Medicine, Alternative Techniques and Aquaculture (CIMETA), University of Rome "Tor Vergata", Via Montpellier 1, Rome, 00133, Italy
- Department of Biology, University of Rome "Tor Vergata", Rome, Italy
| | - Paola Conti
- Department of Pharmaceutical Sciences, University of Milan, Via Mangiagalli 25, Milan, 20133, Italy
| | - Stefano Marini
- Department of Translational Medicine, University of Tor Vergata, Via Montpellier 1, Rome, 00133, Italy
| | - Yongmin Zhang
- Parisian Institute of Molecular Chemistry, Sorbonne University, UMR CNRS 8232, 4 Place Jussieu, Paris, 75005, France
| | - Loredano Pollegioni
- Department of Biotechnology and Life Sciences, University of Insubria, via J.H. Dunant, 3, Insubria, Varese, 21100, Italy
| | - Caterina Temporini
- Drug Sciences Department, University of Pavia, Viale Taramelli 12, Pavia, 27100, Italy
| | - Marco Terreni
- Drug Sciences Department, University of Pavia, Viale Taramelli 12, Pavia, 27100, Italy
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Roy AN, Gupta AM, Banerjee D, Chakrabarti J, Raghavendra PB. Unraveling DPP4 Receptor Interactions with SARS-CoV-2 Variants and MERS-CoV: Insights into Pulmonary Disorders via Immunoinformatics and Molecular Dynamics. Viruses 2023; 15:2056. [PMID: 37896834 PMCID: PMC10612102 DOI: 10.3390/v15102056] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 09/23/2023] [Accepted: 09/28/2023] [Indexed: 10/29/2023] Open
Abstract
Human coronaviruses like MERS CoV are known to utilize dipeptidyl peptidase 4 (DPP4), apart from angiotensin-converting enzyme 2(ACE2) as a potential co-receptor for viral cell entry. DPP4, the ubiquitous membrane-bound aminopeptidase, is closely associated with elevation of disease severity in comorbidities. In SARS-CoV-2, there is inadequate evidence for combination of spike protein variants with DPP4, and underlying adversity in COVID-19. To elucidate this mechanistic basis, we have investigated interaction of spike protein variants with DPP4 through molecular docking and simulation studies. The possible binding interactions between the receptor binding domain (RBD) of different spike variants of SARS-CoV-2 and DPP4 have been compared with interactions observed in the experimentally determined structure of the complex of MERS-CoV with DPP4. Comparative binding affinity confers that Delta-CoV-2: DPP4 shows close proximity with MERS-CoV:DPP4, as depicted from accessible surface area, radius of gyration and number of hydrogen bonding in the interface. Mutations in the delta variant, L452R and T478K directly participate in DPP4 interaction, enhancing DPP4 binding. E484K in alpha and gamma variants of spike protein is also found to interact with DPP4. Hence, DPP4 interaction with spike protein becomes more suitable due to mutation, especially due to L452R, T478K and E484K. Furthermore, perturbation in the nearby residues Y495, Q474 and Y489 is evident due to L452R, T478K and E484K, respectively. Virulent strains of spike protein are more susceptible to DPP4 interaction and are prone to be victimized in patients due to comorbidities. Our results will aid the rational optimization of DPP4 as a potential therapeutic target to manage COVID-19 disease severity.
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Affiliation(s)
- Arpan Narayan Roy
- National Institute of Biomedical Genomics, Kalyani 741251, West Bengal, India; (A.N.R.); (D.B.)
| | - Aayatti Mallick Gupta
- Department of Physics of Complex Systems, S. N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700106, West Bengal, India; (A.M.G.); (J.C.)
| | - Deboshmita Banerjee
- National Institute of Biomedical Genomics, Kalyani 741251, West Bengal, India; (A.N.R.); (D.B.)
| | - Jaydeb Chakrabarti
- Department of Physics of Complex Systems, S. N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700106, West Bengal, India; (A.M.G.); (J.C.)
- Technical Research Centre, S. N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700106, West Bengal, India
| | - Pongali B. Raghavendra
- National Institute of Biomedical Genomics, Kalyani 741251, West Bengal, India; (A.N.R.); (D.B.)
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Liu X, Ke L, Lei K, Yu Q, Zhang W, Li C, Tian Z. Antibiotic-induced gut microbiota dysbiosis has a functional impact on purine metabolism. BMC Microbiol 2023; 23:187. [PMID: 37442943 PMCID: PMC10339580 DOI: 10.1186/s12866-023-02932-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Dysbiosis of the gut microbiota is closely linked to hyperuricemia. However, the effect of the microbiome on uric acid (UA) metabolism remains unclear. This study aimed to explore the mechanisms through which microbiomes affect UA metabolism with the hypothesis that modifying the intestinal microbiota influences the development of hyperuricemia. RESULTS We proposed combining an antibiotic strategy with protein-protein interaction analysis to test this hypothesis. The data demonstrated that antibiotics altered the composition of gut microbiota as UA increased, and that the spectrum of the antibiotic was connected to the purine salvage pathway. The antibiotic-elevated UA concentration was dependent on the increase in microbiomes that code for the proteins involved in purine metabolism, and was paralleled by the depletion of bacteria-coding enzymes required for the purine salvage pathway. On the contrary, the microbiota with abundant purine salvage proteins decreased hyperuricemia. We also found that the antibiotic-increased microbiota coincided with a higher relative abundance of bacteria in hyperuricemia mice. CONCLUSIONS An antibiotic strategy combined with the prediction of microbiome bacterial function presents a feasible method for defining the key bacteria involved in hyperuricemia. Our investigations discovered that the core microbiomes of hyperuricemia may be related to the gut microbiota that enriches purine metabolism related-proteins. However, the bacteria that enrich the purine salvage-proteins may be a probiotic for decreasing urate, and are more likely to be killed by antibiotics. Therefore, the purine salvage pathway may be a potential target for the treatment of both hyperuricemia and antibiotic resistance.
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Affiliation(s)
- Xin Liu
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Leyong Ke
- Department of Cosmetic surgery, Kunming Medical University, Kunming, 650000, China
| | - Ke Lei
- Center of Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Qian Yu
- Center of Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Wenqing Zhang
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Changgui Li
- Institute of Metabolic Diseases, Qingdao University, Qingdao, 266003, China
| | - Zibin Tian
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China.
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4
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Ibrahim AH, Karabulut OC, Karpuzcu BA, Türk E, Süzek BE. A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction. PLoS One 2023; 18:e0285168. [PMID: 37130110 PMCID: PMC10153705 DOI: 10.1371/journal.pone.0285168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 04/17/2023] [Indexed: 05/03/2023] Open
Abstract
Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature selection. We also tested the performance of these baseline models against the previously available tools to ensure their predictive power is acceptable. Here, the Pearson coefficient provides the best performance with respect to the baseline model as measured by AUPR; a drop of 0.003 in AUPR while achieving a 73.3% (from 686 to 183) reduction in the number of tripeptides features for random forest. The results suggest our correlation coefficient-based feature selection approach, while decreasing the computation time and space complexity, has a limited impact on the prediction performance of virus-host PPI prediction tools.
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Affiliation(s)
- Ahmed Hassan Ibrahim
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Onur Can Karabulut
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Betül Asiye Karpuzcu
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Erdem Türk
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Barış Ethem Süzek
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
- Georgetown University Medical Center, Biochemistry and Molecular & Cellular Biology, Washington DC, United States of America
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5
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Karpuzcu BA, Türk E, Ibrahim AH, Karabulut OC, Süzek BE. Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction. Methods Mol Biol 2023; 2690:401-417. [PMID: 37450162 DOI: 10.1007/978-1-0716-3327-4_31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
The attachment of a virion to a respective cellular receptor on the host organism occurring through the virus-host protein-protein interactions (PPIs) is a decisive step for viral pathogenicity and infectivity. Therefore, a vast number of wet-lab experimental techniques are used to study virus-host PPIs. Taking the great number and enormous variety of virus-host PPIs and the cost as well as labor of laboratory work, however, computational approaches toward analyzing the available interaction data and predicting previously unidentified interactions have been on the rise. Among them, machine-learning-based models are getting increasingly more attention with a great body of resources and tools proposed recently.In this chapter, we first provide the methodology with major steps toward the development of a virus-host PPI prediction tool. Next, we discuss the challenges involved and evaluate several existing machine-learning-based virus-host PPI prediction tools. Finally, we describe our experience with several ensemble techniques as utilized on available prediction results retrieved from individual PPI prediction tools. Overall, based on our experience, we recognize there is still room for the development of new individual and/or ensemble virus-host PPI prediction tools that leverage existing tools.
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Affiliation(s)
- Betül Asiye Karpuzcu
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Erdem Türk
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Ahmad Hassan Ibrahim
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Onur Can Karabulut
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Barış Ethem Süzek
- Bioinformatics Graduate Program, Graduate School of Natural and Applied Sciences, Muğla Sıtkı Koçman University, Muğla, Turkey.
- Department of Computer Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey.
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6
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Yu H, Li L, Huffman A, Beverley J, Hur J, Merrell E, Huang HH, Wang Y, Liu Y, Ong E, Cheng L, Zeng T, Zhang J, Li P, Liu Z, Wang Z, Zhang X, Ye X, Handelman SK, Sexton J, Eaton K, Higgins G, Omenn GS, Athey B, Smith B, Chen L, He Y. A new framework for host-pathogen interaction research. Front Immunol 2022; 13:1066733. [PMID: 36591248 PMCID: PMC9797517 DOI: 10.3389/fimmu.2022.1066733] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.
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Affiliation(s)
- Hong Yu
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Li Li
- Department of Genetics, Harvard Medical School, Boston, MA, United States
| | - Anthony Huffman
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - John Beverley
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
- Asymmetric Operations Sector, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, United States
| | - Eric Merrell
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Hsin-hui Huang
- University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yang Wang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Yingtong Liu
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Liang Cheng
- Department of Bioinformatics, Harbin Medical University, Harbin, Helongjian, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Jingsong Zhang
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Pengpai Li
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhiping Liu
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhigang Wang
- Department of Biomedical Engineering, Institute of Basic Medical Sciences and School of Basic Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiangyan Zhang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Xianwei Ye
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | | | - Jonathan Sexton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Kathryn Eaton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gerry Higgins
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gilbert S. Omenn
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Brian Athey
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, United States
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7
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Maleksabet H, Rezaee E, Tabatabai SA. Host-Cell Surface Binding Targets in SARS-CoV-2 for Drug Design. Curr Pharm Des 2022; 28:3583-3591. [PMID: 36420875 DOI: 10.2174/1381612829666221123111849] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/20/2022] [Accepted: 08/31/2022] [Indexed: 11/27/2022]
Abstract
The ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became a major public health threat to all countries worldwide. SARS-CoV-2 interactions with its receptor are the first step in the invasion of the host cell. The coronavirus spike protein (S) is crucial in binding to receptors on host cells. Additionally, targeting the SARS-CoV-2 viral receptors is considered a therapeutic option in this regard. In this review of literature, we summarized five potential host cell receptors, as host-cell surface bindings, including angiotensin-converting enzyme 2 (ACE2), neuropilin 1 (NRP-1), dipeptidyl peptidase 4 (DPP4), glucose regulated protein-78 (GRP78), and cluster of differentiation 147 (CD147) related to the SARS-CoV-2 infection. Among these targets, ACE2 was recognized as the main SARS-CoV-2 receptor, expressed at a low/moderate level in the human respiratory system, which is also involved in SARS-CoV-2 entrance, so the virus may utilize other secondary receptors. Besides ACE2, CD147 was discovered as a novel SARS-CoV-2 receptor, CD147 appears to be an alternate receptor for SARSCoV- 2 infection. NRP-1, as a single-transmembrane glycoprotein, has been recently found to operate as an entrance factor and enhance SARS Coronavirus 2 (SARS-CoV-2) infection under in-vitro. DPP4, which was discovered as the first gene clustered with ACE2, may serve as a potential SARS-CoV-2 spike protein binding target. GRP78 could be recognized as a secondary receptor for SARS-CoV-2 because it is widely expressed at substantially greater levels, rather than ACE2, in bronchial epithelial cells and the respiratory mucosa. This review highlights recent literature on this topic.
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Affiliation(s)
- Hanieh Maleksabet
- Department of Pharmaceutical Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Elham Rezaee
- Department of Pharmaceutical Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sayyed Abbas Tabatabai
- Department of Pharmaceutical Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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8
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Jain A, Mittal S, Tripathi LP, Nussinov R, Ahmad S. Host-pathogen protein-nucleic acid interactions: A comprehensive review. Comput Struct Biotechnol J 2022; 20:4415-4436. [PMID: 36051878 PMCID: PMC9420432 DOI: 10.1016/j.csbj.2022.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 12/02/2022] Open
Abstract
Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention. Here, we discuss the newest advances in studies of molecular interactions involving pathogen nucleic acids and host factors, including their drug design, molecular structure and specific patterns. We observed that two groups of nucleic acid recognizing molecules, Toll-like receptors (TLRs) and the cytoplasmic retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) form the backbone of host responses to pathogen nucleic acids, with additional support provided by absent in melanoma 2 (AIM2) and DNA-dependent activator of Interferons (IFNs)-regulatory factors (DAI) like cytosolic activity. We review the structural, immunological, and other biological aspects of these representative groups of molecules, especially in terms of their target specificity and affinity and challenges in leveraging host-pathogen protein-nucleic acid interactions (HP-PNI) in drug discovery.
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Affiliation(s)
- Anuja Jain
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Shikha Mittal
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, 173234, India
| | - Lokesh P. Tripathi
- National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan
- Riken Center for Integrative Medical Sciences, Tsurumi, Yokohama, Kanagawa, Japan
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National, Laboratory for Cancer Research, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Israel
| | - Shandar Ahmad
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
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9
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Wang S, Wu R, Lu J, Jiang Y, Huang T, Cai YD. Protein-protein interaction networks as miners of biological discovery. Proteomics 2022; 22:e2100190. [PMID: 35567424 DOI: 10.1002/pmic.202100190] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/28/2022] [Accepted: 04/29/2022] [Indexed: 11/12/2022]
Abstract
Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Steven Wang
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Runxin Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jiaqi Lu
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
| | - Yijia Jiang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tao Huang
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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10
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Hu RS, Hesham AEL, Zou Q. Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases. Front Cell Infect Microbiol 2022; 12:882995. [PMID: 35573796 PMCID: PMC9097758 DOI: 10.3389/fcimb.2022.882995] [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: 02/24/2022] [Accepted: 03/28/2022] [Indexed: 12/24/2022] Open
Abstract
In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.
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Affiliation(s)
- Rui-Si Hu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, Egypt
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- *Correspondence: Quan Zou,
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11
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Abstract
Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.
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12
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Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins. Processes (Basel) 2022. [DOI: 10.3390/pr10020291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Infectious diseases are one of the core biological complications for public health. It is important to recognize the pathogen-specific mechanisms to improve our understanding of infectious diseases. Differentiations between bacterial- and viral-targeted human proteins are important for improving both prognosis and treatment for the patient. Here, we introduce machine learning-based classifiers to discriminate between the two groups of human proteins. We used the sequence, network, and gene ontology features of human proteins. Among different classifiers and features, the deep neural network (DNN) classifier with amino acid composition (AAC), dipeptide composition (DC), and pseudo-amino acid composition (PAAC) (445 features) achieved the best area under the curve (AUC) value (0.939), F1-score (94.9%), and Matthews correlation coefficient (MCC) value (0.81). We found that each of the selected top 100 of the bacteria- and virus-targeted human proteins from a candidate pool of 1618 and 3916 proteins, respectively, were part of distinct enriched biological processes and pathways. Our proposed method will help to differentiate between the bacterial and viral infections based on the targeted human proteins on a global scale. Furthermore, identification of the crucial pathogen targets in the human proteome would help us to better understand the pathogen-specific infection strategies and develop novel therapeutics.
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13
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Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219. Mol Biotechnol 2021; 63:1252-1267. [PMID: 34382159 DOI: 10.1007/s12033-021-00377-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/30/2021] [Indexed: 10/20/2022]
Abstract
The reconstruction and analysis of the protein-protein interaction (PPI) network is a powerful approach to understand the complex biological and molecular functions in normal and disease states of the cell. The interactome of most organisms is largely unidentified except some model organisms. The current study focused on the construction of PPI network for the human pathogen Mycobacterium tuberculosis (MTB)-resistant strain XDR1219 using computational methods. In this work, a bioinformatics approach was employed to reveal potential drug targets. The pipeline adopted the combination of an extensive integrated network analysis that led to identify 22 key proteins involved in drug resistance, resistant metabolic pathways, virulence, pathogenesis and persistency of the infection. The MTB XDR1219 interactome consists of 11,383 non-redundant PPIs among 1499 proteins covering 38% of the entire MTB XDR1219 proteome. The overall quality of the network was assessed and topological parameters of the PPI were calculated. The predicted interactions were functionally annotated and their relevance was assessed with the functional similarity. The study attempts to present the interactome of previously unidentified MTB XDR1219 and revealed potential drug targets that can be further explored by scientific community.
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14
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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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15
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Pathogen and Host-Pathogen Protein Interactions Provide a Key to Identify Novel Drug Targets. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11607-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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16
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The MERS-CoV Receptor DPP4 as a Candidate Binding Target of the SARS-CoV-2 Spike. iScience 2020; 23:101160. [PMID: 32405622 PMCID: PMC7219414 DOI: 10.1016/j.isci.2020.101160] [Citation(s) in RCA: 148] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 04/28/2020] [Accepted: 05/11/2020] [Indexed: 12/14/2022] Open
Abstract
The ongoing outbreak of the novel coronavirus pneumonia COVID-19 has caused great number of cases and deaths, but our understanding about the pathogen SARS-CoV-2 remains largely unclear. The attachment of the virus with the cell-surface receptor and a cofactor is the first step for the infection. Here, bioinformatics approaches combining human-virus protein interaction prediction and protein docking based on crystal structures have revealed the high affinity between human dipeptidylpeptidase 4 (DPP4) and the spike (S) receptor-binding domain of SARS-CoV-2. Intriguingly, the crucial binding residues of DPP4 are identical to those that are bound to the MERS-CoV-S. Moreover, E484 insertion and adjacent substitutions should be most essential for this DPP4-binding ability acquirement of SARS-CoV-2-S compared with SARS-CoV-S. This potential utilization of DPP4 as a binding target for SARS-CoV-2 may offer novel insight into the viral pathogenesis and help the surveillance and therapeutics strategy for meeting the challenge of COVID-19.
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Wekesa JS, Luan Y, Meng J. Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis. J Comput Biol 2020; 28:1-18. [PMID: 32302512 DOI: 10.1089/cmb.2019.0120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.
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Affiliation(s)
- Jael Sanyanda Wekesa
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
- School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - Yushi Luan
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
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18
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Pellá MC, Silva OA, Pellá MG, Beneton AG, Caetano J, Simões MR, Dragunski DC. Effect of gelatin and casein additions on starch edible biodegradable films for fruit surface coating. Food Chem 2020; 309:125764. [DOI: 10.1016/j.foodchem.2019.125764] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 10/19/2019] [Accepted: 10/21/2019] [Indexed: 01/13/2023]
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19
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Gupta SK, Srivastava M, Osmanoglu Ö, Dandekar T. Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs. Sci Rep 2020; 10:2334. [PMID: 32047225 PMCID: PMC7012867 DOI: 10.1038/s41598-020-59344-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 01/22/2020] [Indexed: 12/18/2022] Open
Abstract
Apart from some model organisms, the interactome of most organisms is largely unidentified. High-throughput experimental techniques to determine protein-protein interactions (PPIs) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. Our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6% of the entire C. floridanus proteome.
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Affiliation(s)
- Shishir K Gupta
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany.,Department of Microbiology, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Mugdha Srivastava
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Özge Osmanoglu
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany. .,EMBL Heidelberg, BioComputing Unit, Meyerhofstraße 1, 69117, Heidelberg, Germany.
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20
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Mei S, Zhang K. In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks. Comput Struct Biotechnol J 2019; 18:100-113. [PMID: 31956393 PMCID: PMC6956678 DOI: 10.1016/j.csbj.2019.12.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 12/07/2019] [Accepted: 12/14/2019] [Indexed: 01/08/2023] Open
Abstract
Pathogen-host protein interactions are fundamental for pathogens to manipulate host signaling pathways and subvert host immune defense. For most pathogens, very few or no experimental studies have been conducted to investigate their signaling cross-talks with host. In this study, we propose a computational framework to validate the biological assumption that human protein-protein interaction (PPI) networks alone are sufficient to infer pathogen-host PPIs via pathogen functional mimicry. Pathogen functional mimicry assumes that a pathogen functionally mimics and substitutes host counterpart proteins in order for the pathogen to get involved in or hijack the host cellular processes. Through pathogen functional mimicry defined via gene ontology (GO) semantic similarity, we first use the known human PPIs as templates to infer pathogen-host PPIs, and the PPIs are further used as training data to build an l2-regularized logistic regression model for novel pathogen-host PPI prediction. Independent tests on the experimental data from human immunodeficiency virus and Francisella tularensis validate the effectiveness of the proposed pathogen functional mimicry technique. Performance comparisons also show that the proposed technique y excels the existing pathogen sequence mimicry approaches and transfer learning methods. The proposed framework provides a new avenue to study the experimentally less-studied pathogens in the worst scenarios that very few or no experimental pathogen-host PPIs are available. As two case studies, we apply the proposed framework to Salmonella typhimurium and Human respiratory syncytial virus to reconstruct the pathogen-host PPI networks and further investigate the interference of these two pathogens with human immune signaling and transcription regulatory system.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang 110034, China
| | - Kun Zhang
- Bioinformatics Core of Xavier RCMI Center for Cancer Research, Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
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21
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Mei S, Zhang K. Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks. Int J Mol Sci 2019; 20:ijms20205075. [PMID: 31614890 PMCID: PMC6829266 DOI: 10.3390/ijms20205075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 10/11/2019] [Indexed: 12/11/2022] Open
Abstract
Rapid reconstruction of genome-scale protein-protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e., pairs of proteins that do not interact) is still a major issue that needs to be properly addressed in computational modeling. In this study, we take advantage of the very limited experimentally verified negative data from Negatome to infer more negative data for computational modeling. We assume that the paralogs or orthologs of two non-interacting proteins also do not interact with high probability. We coin an assumption as "Neglog" this assumption is to some extent supported by paralogous/orthologous structure conservation. To reduce the risk of bias toward the negative data from Negatome, we combine Neglog with less biased random sampling according to a certain ratio to construct training data. L2-regularized logistic regression is used as the base classifier to counteract noise and train on a large dataset. Computational results show that the proposed Neglog method outperforms pure random sampling method with sound biological interpretability. In addition, we find that independent test on negative data is indispensable for bias control, which is usually neglected by existing studies. Lastly, we use the Neglog method to validate the PPIs in STRING, which are supported by gene ontology (GO) enrichment analyses.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang 110034, China.
| | - Kun Zhang
- Bioinformatics facility of Xavier NIH RCMI Cancer Research Center, Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA.
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22
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Sim EUH, Talwar SP. In silico evidence of de novo interactions between ribosomal and Epstein - Barr virus proteins. BMC Mol Cell Biol 2019; 20:34. [PMID: 31416416 PMCID: PMC6694676 DOI: 10.1186/s12860-019-0219-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 08/08/2019] [Indexed: 12/29/2022] Open
Abstract
Background Association of Epstein-Barr virus (EBV) encoded latent gene products with host ribosomal proteins (RPs) has not been fully explored, despite their involvement in the aetiology of several human cancers. To gain an insight into their plausible interactions, we employed a computational approach that encompasses structural alignment, gene ontology analysis, pathway analysis, and molecular docking. Results In this study, the alignment analysis based on structural similarity allows the prediction of 48 potential interactions between 27 human RPs and the EBV proteins EBNA1, LMP1, LMP2A, and LMP2B. Gene ontology analysis of the putative protein-protein interactions (PPIs) reveals their probable involvement in RNA binding, ribosome biogenesis, metabolic and biosynthetic processes, and gene regulation. Pathway analysis shows their possible participation in viral infection strategies (viral translation), as well as oncogenesis (Wnt and EGFR signalling pathways). Finally, our molecular docking assay predicts the functional interactions of EBNA1 with four RPs individually: EBNA1-eS10, EBNA1-eS25, EBNA1-uL10 and EBNA1-uL11. Conclusion These interactions have never been revealed previously via either experimental or in silico approach. We envisage that the calculated interactions between the ribosomal and EBV proteins herein would provide a hypothetical model for future experimental studies on the functional relationship between ribosomal proteins and EBV infection. Electronic supplementary material The online version of this article (10.1186/s12860-019-0219-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Edmund Ui-Hang Sim
- Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia.
| | - Shruti Prashant Talwar
- Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia
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Cuesta-Astroz Y, Santos A, Oliveira G, Jensen LJ. Analysis of Predicted Host-Parasite Interactomes Reveals Commonalities and Specificities Related to Parasitic Lifestyle and Tissues Tropism. Front Immunol 2019; 10:212. [PMID: 30815000 PMCID: PMC6381214 DOI: 10.3389/fimmu.2019.00212] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 01/24/2019] [Indexed: 01/03/2023] Open
Abstract
The study of molecular host–parasite interactions is essential to understand parasitic infection and adaptation within the host system. As well, prevention and treatment of infectious diseases require a clear understanding of the molecular crosstalk between parasites and their hosts. Yet, large-scale experimental identification of host–parasite molecular interactions remains challenging, and the use of computational predictions becomes then necessary. Here, we propose a computational integrative approach to predict host—parasite protein—protein interaction (PPI) networks resulting from the human infection by 15 different eukaryotic parasites. We used an orthology-based approach to transfer high-confidence intraspecies interactions obtained from the STRING database to the corresponding interspecies homolog protein pairs in the host–parasite system. Our approach uses either the parasites predicted secretome and membrane proteins, or only the secretome, depending on whether they are uni- or multi-cellular, respectively, to reduce the number of false predictions. Moreover, the host proteome is filtered for proteins expressed in selected cellular localizations and tissues supporting the parasite growth. We evaluated the inferred interactions by analyzing the enriched biological processes and pathways in the predicted networks and their association with known parasitic invasion and evasion mechanisms. The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark. We also provided a study case focusing on a closer examination of the human–S. mansoni predicted interactome, detecting central proteins that have relevant roles in the human–S. mansoni network, and identifying tissue-specific interactions with key roles in the life cycle of the parasite. The predicted PPI networks can be visualized and downloaded at http://orthohpi.jensenlab.org.
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Affiliation(s)
- Yesid Cuesta-Astroz
- Instituto René Rachou, Fundação Oswaldo Cruz - FIOCRUZ, Belo Horizonte, Brazil
| | - Alberto Santos
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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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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25
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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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26
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Halder AK, Dutta P, Kundu M, Basu S, Nasipuri M. Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions. Brief Funct Genomics 2018; 17:381-391. [PMID: 29028879 PMCID: PMC7109800 DOI: 10.1093/bfgp/elx026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.
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Affiliation(s)
- Anup Kumar Halder
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Pritha Dutta
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mahantapas Kundu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, India
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Sun J, Yang LL, Chen X, Kong DX, Liu R. Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions. J Proteome Res 2018; 17:3810-3823. [PMID: 30269499 DOI: 10.1021/acs.jproteome.8b00497] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.
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Zhou X, Park B, Choi D, Han K. A generalized approach to predicting protein-protein interactions between virus and host. BMC Genomics 2018; 19:568. [PMID: 30367586 PMCID: PMC6101077 DOI: 10.1186/s12864-018-4924-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background Viral infection involves a large number of protein-protein interactions (PPIs) between virus and its host. These interactions range from the initial binding of viral coat proteins to host membrane receptor to the hijacking the host transcription machinery by viral proteins. Therefore, identifying PPIs between virus and its host helps understand the mechanism of viral infections and design antiviral drugs. Many computational methods have been developed to predict PPIs, but most of them are intended for PPIs within a species rather than PPIs across different species such as PPIs between virus and host. Results In this study, we developed a prediction model of virus-host PPIs, which is applicable to new viruses and hosts. We tested the prediction model on independent datasets of virus-host PPIs, which were not used in training the model. Despite a low sequence similarity between proteins in training datasets and target proteins in test datasets, the prediction model showed a high performance comparable to the best performance of other methods for single virus-host PPIs. Conclusions Our method will be particularly useful to find PPIs between host and new viruses for which little information is available. The program and support data are available at http://bclab.inha.ac.kr/VirusHostPPI.
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Affiliation(s)
- Xiang Zhou
- Department of Computer Engineering, Inha University, Incheon, 22212, South Korea
| | - Byungkyu Park
- Department of Computer Engineering, Inha University, Incheon, 22212, South Korea
| | - Daesik Choi
- Department of Computer Engineering, Inha University, Incheon, 22212, South Korea
| | - Kyungsook Han
- Department of Computer Engineering, Inha University, Incheon, 22212, South Korea.
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Soyemi J, Isewon I, Oyelade J, Adebiyi E. Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed. Curr Bioinform 2018; 13:396-406. [PMID: 31496926 PMCID: PMC6691774 DOI: 10.2174/1574893613666180108155851] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 12/31/2017] [Accepted: 01/02/2018] [Indexed: 01/01/2023]
Abstract
BACKGROUND Host-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases. OBJECTIVE Many studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose. METHODS The various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed. RESULT We discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information. CONCLUSION This review presents useful knowledge and future directions on the subject matter.
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Affiliation(s)
- Jumoke Soyemi
- Department of Computer Science, The Federal Polytechnic, Ilaro, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Ota, Nigeria
| | - Itunnuoluwa Isewon
- Department of Computer & Information Sciences, Covenant University, Ota, Nigeria and
- Covenant University Bioinformatics Research (CUBRe), Ota, Nigeria
| | - Jelili Oyelade
- Department of Computer & Information Sciences, Covenant University, Ota, Nigeria and
- Covenant University Bioinformatics Research (CUBRe), Ota, Nigeria
| | - Ezekiel Adebiyi
- Department of Computer & Information Sciences, Covenant University, Ota, Nigeria and
- Covenant University Bioinformatics Research (CUBRe), Ota, Nigeria
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Bose T, Das C, Dutta A, Mahamkali V, Sadhu S, Mande SS. Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach. BMC Genomics 2018; 19:555. [PMID: 30053801 PMCID: PMC6064076 DOI: 10.1186/s12864-018-4947-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 07/19/2018] [Indexed: 01/17/2023] Open
Abstract
Background Mycobacterium tuberculosis infection in humans is often associated with extended period of latency. To adapt to the hostile hypoxic environment inside a macrophage, M. tuberculosis cells undergo several physiological and metabolic changes. Previous studies have mostly focused on inspecting individual facets of this complex process. In order to gain deeper insights into the infection process and to understand the coordination among different regulatory/ metabolic pathways in the pathogen, the current in silico study investigates three aspects, namely, (i) host-pathogen interactions (HPIs) between human and M. tuberculosis proteins, (ii) gene regulatory network pertaining to adaptation of M. tuberculosis to hypoxia and (iii) alterations in M. tuberculosis metabolism under hypoxic condition. Subsequently, cross-talks between these components have been probed to evaluate possible gene-regulatory events as well as HPIs which are likely to drive metabolic changes during pathogen’s adaptation to the intra-host hypoxic environment. Results The newly identified HPIs suggest the pathogen’s ability to subvert host mediated reactive oxygen intermediates/ reactive nitrogen intermediates (ROI/ RNI) stress as well as their potential role in modulating host cell cycle and cytoskeleton structure. The results also indicate a significantly pronounced effect of HPIs on hypoxic metabolism of M. tuberculosis. Findings from the current study underscore the necessity of investigating the infection process from a systems-level perspective incorporating different facets of intra-cellular survival of the pathogen. Conclusions The comprehensive host-pathogen interaction network, a Boolean model of M. tuberculosis H37Rv (Mtb) hypoxic gene-regulation, as well as a genome scale metabolic model of Mtb, built for this study are expected to be useful resources for future studies on tuberculosis infection. Electronic supplementary material The online version of this article (10.1186/s12864-018-4947-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tungadri Bose
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.,Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Chandrani Das
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.,Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Anirban Dutta
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.
| | - Vishnuvardhan Mahamkali
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.,Present Address: Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Australia
| | - Sudipta Sadhu
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India
| | - Sharmila S Mande
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Limited, Pune, India.
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31
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Identification of Antifungal Targets Based on Computer Modeling. J Fungi (Basel) 2018; 4:jof4030081. [PMID: 29973534 PMCID: PMC6162656 DOI: 10.3390/jof4030081] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 06/24/2018] [Accepted: 06/29/2018] [Indexed: 01/07/2023] Open
Abstract
Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host⁻pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.
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32
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Mei S, Flemington EK, Zhang K. Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis. BMC Genomics 2018; 19:505. [PMID: 29954330 PMCID: PMC6027805 DOI: 10.1186/s12864-018-4873-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 06/18/2018] [Indexed: 12/11/2022] Open
Abstract
Background Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date. Methods In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l2-regularized logistic regression model. Results The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance. Conclusions The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways. Electronic supplementary material The online version of this article (10.1186/s12864-018-4873-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang, 110034, China.
| | - Erik K Flemington
- Department of Pathology, Tulane Cancer Center, Tulane University, New Orleans, LA, 70112, USA.
| | - Kun Zhang
- Department of Computer Science, Bioinformatics facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, 70125, USA.
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Predicting Interactions between Virus and Host Proteins Using Repeat Patterns and Composition of Amino Acids. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:1391265. [PMID: 29854357 PMCID: PMC5966669 DOI: 10.1155/2018/1391265] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 03/27/2018] [Accepted: 04/17/2018] [Indexed: 11/29/2022]
Abstract
Previous methods for predicting protein-protein interactions (PPIs) were mainly focused on PPIs within a single species, but PPIs across different species have recently emerged as an important issue in some areas such as viral infection. The primary focus of this study is to predict PPIs between virus and its targeted host, which are involved in viral infection. We developed a general method that predicts interactions between virus and host proteins using the repeat patterns and composition of amino acids. In independent testing of the method with PPIs of new viruses and hosts, it showed a high performance comparable to the best performance of other methods for single virus-host PPIs. In comparison of our method with others using same datasets, our method outperformed the others. The repeat patterns and composition of amino acids are simple, yet powerful features for predicting virus-host PPIs. The method developed in this study will help in finding new virus-host PPIs for which little information is available.
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Mei S. In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks. J Proteome Res 2018; 17:1749-1760. [PMID: 29611419 DOI: 10.1021/acs.jproteome.7b00702] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M. tuberculosis H37Rv (MTB) PPI networks in STRING. Computational results show that the experimental data derived jointly by two-hybrid and copurification approaches are the most reliable to train an L2-regularized logistic regression model for MTB PPI network validation. On the basis of the validated MTB PPI networks, we further study the three problems via breadth-first graph search algorithm: (1) discovery of MTB drug-resistance pathways through searching for the paths between known drug-target genes and drug-resistance genes, (2) choosing potential cotarget genes via searching for the critical genes located on multiple pathways, and (3) choosing essential drug-target genes via analysis of network degree distribution. In addition, we further combine the validated MTB PPI networks with human PPI networks to analyze the potential pharmacological risks of known and candidate drug-target genes from the point of view of system pharmacology. The evidence from protein structure alignment demonstrates that the drugs that act on MTB target genes could also adversely act on human signaling pathways.
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Affiliation(s)
- Suyu Mei
- Software College , Shenyang Normal University , Shenyang 110034 , China
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35
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Yang S, Li H, He H, Zhou Y, Zhang Z. Critical assessment and performance improvement of plant–pathogen protein–protein interaction prediction methods. Brief Bioinform 2017; 20:274-287. [DOI: 10.1093/bib/bbx123] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Indexed: 01/15/2023] Open
Affiliation(s)
- Shiping Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University
| | - Hong Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University
| | - Huaqin He
- College of Life Sciences, Fujian Agriculture and Forestry University
| | - Yuan Zhou
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University
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36
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Bose T, Venkatesh KV, Mande SS. Computational Analysis of Host-Pathogen Protein Interactions between Humans and Different Strains of Enterohemorrhagic Escherichia coli. Front Cell Infect Microbiol 2017; 7:128. [PMID: 28469995 PMCID: PMC5395655 DOI: 10.3389/fcimb.2017.00128] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 03/28/2017] [Indexed: 01/18/2023] Open
Abstract
Serotype O157:H7, an enterohemorrhagic Escherichia coli (EHEC), is known to cause gastrointestinal and systemic illnesses ranging from diarrhea and hemorrhagic colitis to potentially fatal hemolytic uremic syndrome. Specific genetic factors like ompA, nsrR, and LEE genes are known to play roles in EHEC pathogenesis. However, these factors are not specific to EHEC and their presence in several non-pathogenic strains indicates that additional factors are involved in pathogenicity. We propose a comprehensive effort to screen for such potential genetic elements, through investigation of biomolecular interactions between E. coli and their host. In this work, an in silico investigation of the protein–protein interactions (PPIs) between human cells and four EHEC strains (viz., EDL933, Sakai, EC4115, and TW14359) was performed in order to understand the virulence and host-colonization strategies of these strains. Potential host–pathogen interactions (HPIs) between human cells and the “non-pathogenic” E. coli strain MG1655 were also probed to evaluate whether and how the variations in the genomes could translate into altered virulence and host-colonization capabilities of the studied bacterial strains. Results indicate that a small subset of HPIs are unique to the studied pathogens and can be implicated in virulence. This subset of interactions involved E. coli proteins like YhdW, ChuT, EivG, and HlyA. These proteins have previously been reported to be involved in bacterial virulence. In addition, clear differences in lineage and clade-specific HPI profiles could be identified. Furthermore, available gene expression profiles of the HPI-proteins were utilized to estimate the proportion of proteins which may be involved in interactions. We hypothesized that a cumulative score of the ratios of bound:unbound proteins (involved in HPIs) would indicate the extent of colonization. Thus, we designed the Host Colonization Index (HCI) measure to determine the host colonization potential of the E. coli strains. Pathogenic strains of E. coli were observed to have higher HCIs as compared to a non-pathogenic laboratory strain. However, no significant differences among the HCIs of the two pathogenic groups were observed. Overall, our findings are expected to provide additional insights into EHEC pathogenesis and are likely to aid in designing alternate preventive and therapeutic strategies.
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Affiliation(s)
- Tungadri Bose
- Bio-Sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services LimitedPune, India.,Department of Chemical Engineering, Indian Institute of Technology BombayMumbai, India
| | - K V Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology BombayMumbai, India
| | - Sharmila S Mande
- Bio-Sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services LimitedPune, India
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Mahajan G, Mande SC. Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis. BMC Bioinformatics 2017; 18:201. [PMID: 28376709 PMCID: PMC5379762 DOI: 10.1186/s12859-017-1550-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 02/16/2017] [Indexed: 12/31/2022] Open
Abstract
Background A comprehensive map of the human-M. tuberculosis (MTB) protein interactome would help fill the gaps in our understanding of the disease, and computational prediction can aid and complement experimental studies towards this end. Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs); these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved. Results We propose leveraging the domain-domain interaction (DDI) information in PDB complexes to score and prioritize candidate PPIs between host and pathogen proteomes based on targeted sequence-level comparisons. Our method picks out a small set of human-MTB protein pairs as candidates for physical interactions, and the use of functional meta-data suggests that some of them could contribute to the in vivo molecular cross-talk between pathogen and host that regulates the course of the infection. Further, we present numerical data for Pfam domain families that highlights interaction specificity on the domain level. Not every instance of a pair of domains, for which interaction evidence has been found in a few instances (i.e. structures), is likely to functionally interact. Our sorting approach scores candidates according to how “distant” they are in sequence space from known examples of DDIs (templates). Thus, it provides a natural way to deal with the heterogeneity in domain-level interactions. Conclusions Our method represents a more informed application of local alignment to the sequence-based search for potential human-microbial interactions that uses available PPI data as a prior. Our approach is somewhat limited in its sensitivity by the restricted size and diversity of the template dataset, but, given the rapid accumulation of solved protein complex structures, its scope and utility are expected to keep steadily improving. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1550-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gaurang Mahajan
- National Centre for Cell Science, Ganeshkhind, Pune, 411 007, India. .,Indian Institute of Science Education and Research, Pashan, Pune, 411 008, India.
| | - Shekhar C Mande
- National Centre for Cell Science, Ganeshkhind, Pune, 411 007, India
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Gromiha MM, Yugandhar K, Jemimah S. Protein-protein interactions: scoring schemes and binding affinity. Curr Opin Struct Biol 2016; 44:31-38. [PMID: 27866112 DOI: 10.1016/j.sbi.2016.10.016] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 09/30/2016] [Accepted: 10/25/2016] [Indexed: 01/16/2023]
Abstract
Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.
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Affiliation(s)
- M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.
| | - K Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Sherlyn Jemimah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
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39
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Folador EL, de Carvalho PVSD, Silva WM, Ferreira RS, Silva A, Gromiha M, Ghosh P, Barh D, Azevedo V, Röttger R. In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks. BMC SYSTEMS BIOLOGY 2016; 10:103. [PMID: 27814699 PMCID: PMC5097352 DOI: 10.1186/s12918-016-0346-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 10/18/2016] [Indexed: 12/27/2022]
Abstract
Background Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Results Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. Conclusions The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0346-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Edson Luiz Folador
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.,Institute of Biological Sciences, Federal University of Para, Belém, PA, Brazil.,Biotechnology Center (CBiotec), Federal University of Paraiba (UFPB), João Pessoa, Brazil
| | - Paulo Vinícius Sanches Daltro de Carvalho
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Wanderson Marques Silva
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Rafaela Salgado Ferreira
- Department of Biochemistry and Immunology, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Artur Silva
- Institute of Biological Sciences, Federal University of Para, Belém, PA, Brazil
| | - Michael Gromiha
- Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Tamilnadu, India
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Debmalya Barh
- Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur, West Bengal, India
| | - Vasco Azevedo
- Department of General Biology, Instituto de Ciências Biológicas (ICB), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
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Li CW, Lee YL, Chen BS. Genetic-and-Epigenetic Interspecies Networks for Cross-Talk Mechanisms in Human Macrophages and Dendritic Cells during MTB Infection. Front Cell Infect Microbiol 2016; 6:124. [PMID: 27803888 PMCID: PMC5067469 DOI: 10.3389/fcimb.2016.00124] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 09/22/2016] [Indexed: 12/29/2022] Open
Abstract
Tuberculosis is caused by Mycobacterium tuberculosis (Mtb) infection. Mtb is one of the oldest human pathogens, and evolves mechanisms implied in human evolution. The lungs are the first organ exposed to aerosol-transmitted Mtb during gaseous exchange. Therefore, the guards of the immune system in the lungs, such as macrophages (Mϕs) and dendritic cells (DCs), are the most important defense against Mtb infection. There have been several studies discussing the functions of Mϕs and DCs during Mtb infection, but the genome-wide pathways and networks are still incomplete. Furthermore, the immune response induced by Mϕs and DCs varies. Therefore, we analyzed the cross-talk genome-wide genetic-and-epigenetic interspecies networks (GWGEINs) between Mϕs vs. Mtb and DCs vs. Mtb to determine the varying mechanisms of both the host and pathogen as it relates to Mϕs and DCs during early Mtb infection. First, we performed database mining to construct candidate cross-talk GWGEIN between human cells and Mtb. Then we constructed dynamic models to characterize the molecular mechanisms, including intraspecies gene/microRNA (miRNA) regulation networks (GRNs), intraspecies protein-protein interaction networks (PPINs), and the interspecies PPIN of the cross-talk GWGEIN. We applied a system identification method and a system order detection scheme to dynamic models to identify the real cross-talk GWGEINs using the microarray data of Mϕs, DCs and Mtb. After identifying the real cross-talk GWGEINs, the principal network projection (PNP) method was employed to construct host-pathogen core networks (HPCNs) between Mϕs vs. Mtb and DCs vs. Mtb during infection process. Thus, we investigated the underlying cross-talk mechanisms between the host and the pathogen to determine how the pathogen counteracts host defense mechanisms in Mϕs and DCs during Mtb H37Rv early infection. Based on our findings, we propose Rv1675c as a potential drug target because of its important defensive role in Mϕs. Furthermore, the membrane essential proteins v1098c, and Rv1696 (or cytoplasm for Rv0667), in Mtb could also be potential drug targets because of their important roles in Mtb survival in both cell types. We also propose the drugs Lopinavir, TMC207, ATSM, and GTSM as potential therapeutic treatments for Mtb infection since they target the above potential drug targets.
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Affiliation(s)
- Cheng-Wei Li
- Laboratory of Control and Systems Biology, National Tsing Hua University Hsinchu, Taiwan
| | - Yun-Lin Lee
- Laboratory of Control and Systems Biology, National Tsing Hua University Hsinchu, Taiwan
| | - Bor-Sen Chen
- Laboratory of Control and Systems Biology, National Tsing Hua University Hsinchu, Taiwan
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Ramakrishnan G, Chandra NR, Srinivasan N. Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv. MOLECULAR BIOSYSTEMS 2016; 11:3316-31. [PMID: 26429199 DOI: 10.1039/c5mb00476d] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery. Such a cost and time-saving approach gains immense importance for pathogens of special interest, such as Mycobacterium tuberculosis H37Rv. We report a comprehensive approach to repurpose drugs, based on the exploration of evolutionary relationships inferred from the comparative sequence and structural analyses between targets of FDA-approved drugs and the proteins of M. tuberculosis. This approach has facilitated the identification of several polypharmacological drugs that could potentially target unexploited M. tuberculosis proteins. A total of 130 FDA-approved drugs, originally intended against other diseases, could be repurposed against 78 potential targets in M. tuberculosis. Additionally, we have also made an attempt to augment the chemical space by recognizing compounds structurally similar to FDA-approved drugs. For three of the attractive cases we have investigated the probable binding modes of the drugs in their corresponding M. tuberculosis targets by means of structural modelling. Such prospective targets and small molecules could be prioritized for experimental endeavours, and could significantly influence drug-discovery and drug-development programmes for tuberculosis.
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Affiliation(s)
- Gayatri Ramakrishnan
- Indian Institute of Science Mathematics Initiative, Indian Institute of Science, Bangalore-560012, India and Molecular Biophysics Unit, Indian Institute of Science, Bangalore-560012, India.
| | - Nagasuma R Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore-560012, India
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Kim B, Alguwaizani S, Zhou X, Huang DS, Park B, Han K. An improved method for predicting interactions between virus and human proteins. J Bioinform Comput Biol 2016; 15:1650024. [PMID: 27397631 DOI: 10.1142/s0219720016500244] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The interaction of virus proteins with host proteins plays a key role in viral infection and consequent pathogenesis. Many computational methods have been proposed to predict protein-protein interactions (PPIs), but most of the computational methods are intended for PPIs within a species rather than PPIs across different species such as virus-host PPIs. We developed a method that represents key features of virus and human proteins of variable length into a feature vector of fixed length. The key features include the relative frequency of amino acid triplets (RFAT), the frequency difference of amino acid triplets (FDAT) between virus and host proteins, and amino acid composition (AC). We constructed several support vector machine (SVM) models to evaluate our method and to compare our method with others on PPIs between human and two types of viruses: human papillomaviruses (HPV) and hepatitis C virus (HCV). Comparison of our method to others with same datasets of HPV-human PPIs and HCV-human PPIs showed that the performance of our method is significantly higher than others in all performance measures. Using the SVM model with gene ontology (GO) annotations of proteins, we predicted new HPV-human PPIs. We believe our approach will be useful in predicting heterogeneous PPIs.
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Affiliation(s)
- Byungmin Kim
- * Department of Computer Science and Engineering, Inha University, Incheon 22212, South Korea
| | - Saud Alguwaizani
- * Department of Computer Science and Engineering, Inha University, Incheon 22212, South Korea
| | - Xiang Zhou
- * Department of Computer Science and Engineering, Inha University, Incheon 22212, South Korea
| | - De-Shuang Huang
- † Machine Learning and Systems Biology Lab, College of Electronics and Information Engineering, Tongji University, Shanghai 201804, P. R. China
| | - Byunkyu Park
- * Department of Computer Science and Engineering, Inha University, Incheon 22212, South Korea
| | - Kyungsook Han
- * Department of Computer Science and Engineering, Inha University, Incheon 22212, South Korea
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Wu X, Wu G, Yao X, Hou G, Jiang F. The clinicopathological significance and ethnic difference of FHIT hypermethylation in non-small-cell lung carcinoma: a meta-analysis and literature review. DRUG DESIGN DEVELOPMENT AND THERAPY 2016; 10:699-709. [PMID: 26929601 PMCID: PMC4760666 DOI: 10.2147/dddt.s85253] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Emerging evidence indicates that FHIT is a candidate tumor suppressor in many types of tumors including non-small-cell lung carcinoma (NSCLC). However, the prognostic value and correlation between FHIT hypermethylation and clinicopathological characteristics of NSCLC remains unclear. In this report, we performed a meta-analysis to evaluate the effects of FHIT hypermethylation on the incidence of NSCLC and clinicopathological characteristics of human NSCLC patients. Final analysis of 1,801 NSCLC patients from 18 eligible studies was performed. FHIT hypermethylation was found to be significantly higher in NSCLC than in normal lung tissue. The pooled odds ratio (OR) from ten studies included 819 NSCLC and 792 normal lung tissues (OR =7.51, 95% confidence interval [CI] =2.98-18.91, P<0.0001). Subgroup analysis based on ethnicity implied that FHIT hypermethylation level was higher in NSCLC tissues than in normal tissues in both Caucasians (P=0.02) and Asians (P<0.0001), indicating that the difference in Asians was much more significant. FHIT hypermethylation was also correlated with sex status, smoking status, as well as pathological types. In addition, patients with FHIT hypermethylation had a lower survival rate than those without (hazard ratio =1.73, 95% CI =1.10-2.71, P=0.02). The results of this meta-analysis suggest that FHIT hypermethylation is associated with an increased risk and poor survival in NSCLC patients. FHIT hypermethylation, which induces the inactivation of FHIT gene, plays an important role in the carcinogenesis and clinical outcome and may serve as a potential diagnostic marker and drug target of NSCLC.
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Affiliation(s)
- Xiaoyu Wu
- Department of Surgical Oncology, Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, People's Republic of China
| | - Guannan Wu
- Department of Surgical Oncology, Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, People's Republic of China
| | - Xuequan Yao
- Department of Surgical Oncology, Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, People's Republic of China
| | - Gang Hou
- Department of Respiratory Medicine, The First Hospital of China Medical University, Shenyang, People's Republic of China
| | - Feng Jiang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, People's Republic of China
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Abbasi WA, Minhas FUAA. Issues in performance evaluation for host-pathogen protein interaction prediction. J Bioinform Comput Biol 2016; 14:1650011. [PMID: 26932275 DOI: 10.1142/s0219720016500116] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein-protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host-pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose.
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Affiliation(s)
- Wajid Arshad Abbasi
- 1 Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan
| | - Fayyaz Ul Amir Afsar Minhas
- 1 Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan
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Eid FE, ElHefnawi M, Heath LS. DeNovo: virus-host sequence-based protein–protein interaction prediction. Bioinformatics 2015; 32:1144-50. [DOI: 10.1093/bioinformatics/btv737] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 12/12/2015] [Indexed: 01/02/2023] Open
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Coelho ED, Santiago AM, Arrais JP, Oliveira JL. Computational methodology for predicting the landscape of the human–microbial interactome region level influence. J Bioinform Comput Biol 2015; 13:1550023. [DOI: 10.1142/s0219720015500237] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Microbial communities thrive in close association among themselves and with the host, establishing protein–protein interactions (PPIs) with the latter, and thus being able to benefit (positively impact) or disturb (negatively impact) biological events in the host. Despite major collaborative efforts to sequence the Human microbiome, there is still a great lack of understanding their impact. We propose a computational methodology to predict the impact of microbial proteins in human biological events, taking into account the abundance of each microbial protein and its relation to all other microbial and human proteins. This alternative methodology is centered on an improved impact estimation algorithm that integrates PPIs between human and microbial proteins with Reactome pathway data. This methodology was applied to study the impact of 24 microbial phyla over different cellular events, within 10 different human microbiomes. The results obtained confirm findings already described in the literature and explore new ones. We believe the Human microbiome can no longer be ignored as not only is there enough evidence correlating microbiome alterations and disease states, but also the return to healthy states once these alterations are reversed.
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Affiliation(s)
- Edgar D. Coelho
- Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
| | - André M. Santiago
- Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, Polo2, Pinhal de Marrocos, 3030-290 Coimbra, Portugal
| | - Joel P. Arrais
- Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, Polo2, Pinhal de Marrocos, 3030-290 Coimbra, Portugal
| | - José Luís Oliveira
- Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
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Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv. BIOMED RESEARCH INTERNATIONAL 2015; 2015:212061. [PMID: 26618166 PMCID: PMC4651637 DOI: 10.1155/2015/212061] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 09/09/2015] [Accepted: 09/27/2015] [Indexed: 12/01/2022]
Abstract
Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.
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Pan A, Lahiri C, Rajendiran A, Shanmugham B. Computational analysis of protein interaction networks for infectious diseases. Brief Bioinform 2015; 17:517-26. [PMID: 26261187 PMCID: PMC7110031 DOI: 10.1093/bib/bbv059] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Indexed: 12/13/2022] Open
Abstract
Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.
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Remmele CW, Luther CH, Balkenhol J, Dandekar T, Müller T, Dittrich MT. Integrated inference and evaluation of host-fungi interaction networks. Front Microbiol 2015; 6:764. [PMID: 26300851 PMCID: PMC4523839 DOI: 10.3389/fmicb.2015.00764] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 07/13/2015] [Indexed: 12/18/2022] Open
Abstract
Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data.
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Affiliation(s)
| | | | | | - Thomas Dandekar
- Department of Bioinformatics, University of Würzburg Würzburg, Germany
| | - Tobias Müller
- Department of Bioinformatics, University of Würzburg Würzburg, Germany
| | - Marcus T Dittrich
- Department of Bioinformatics, University of Würzburg Würzburg, Germany ; Department of Human Genetics, University of Würzburg Würzburg, Germany
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50
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Melak T, Gakkhar S. Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network. Clin Transl Med 2015; 4:61. [PMID: 26061871 PMCID: PMC4467812 DOI: 10.1186/s40169-015-0061-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2015] [Accepted: 06/02/2015] [Indexed: 01/26/2023] Open
Abstract
Background In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacteriumtuberculosis H37Rv based on their flow to resistance genes. Methods The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation. Results A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism. Conclusion Potential drug targets of Mycobacteriumtuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to resistance genes is more likely to disrupt the communication to these genes. Purposely selected literature review of the top 14 proteins showed that many of them in this list were proposed as drug targets of the pathogen. Electronic supplementary material The online version of this article (doi:10.1186/s40169-015-0061-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tilahun Melak
- Department of Computer Science, Dilla University, Gedeo, Ethiopia,
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