Skip to main content

Protein–Protein Interaction Network for the Identification of New Targets Against Novel Coronavirus

  • Protocol
  • First Online:
In Silico Modeling of Drugs Against Coronaviruses

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus in the family of Coronaviridae. The virus causes COVID-19, an infectious disease. SARS-CoV-2 provides novel clinical, immunological, and pathological features, compared to other established coronaviruses, such as the Middle East respiratory syndrome (MERS-CoV) and severe acute respiratory syndrome (SARS-CoV). There is no indication of successful antiviral treatment or vaccination at this stage. Several computational methods have been used to quickly understand the pathogenesis of viruses and to classify antiviral medications. Protein–protein interaction networks allow us to understand pathogenic pathways that contribute to the disease, infection, and development and to translate this knowledge to effective diagnostics and therapeutic strategies. The conventional approach to drug intervention and treatment interventions started to evolve with the complexity of dependence and drug pathways. Identifying drug–target interactions cannot only minimize drug production cycles and costs but can also strengthen awareness of how the future drugs and targets are identified. The purpose of a pharmaceutical network is to classify multi-target compounds focused at different protein groups involved in disrupted complexes. This promotes a network-centered viewpoint on drug action via the mapping of the goal network and offers a fresh insight into the role of polypharmacology in the drug operation. In this chapter, we shall address the available experiments and computations of protein–protein interaction methods to identify SARS-CoV-2 drugs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Boezio B, Audouze K et al (2017) Network-based approaches in pharmacology. Wiley Online Library, Weinheim

    Book  Google Scholar 

  2. Legrain P, Wojcik J, Gauthier JM (2001) Protein-protein interaction maps: a lead towards cellular functions. Trends Genet 17:346–352

    Article  CAS  PubMed  Google Scholar 

  3. Cho S, Park SG, Lee DH, Park BC (2004) Protein-protein interaction networks: from interactions to networks. J Biochem Mol Biol 37:45–52

    CAS  PubMed  Google Scholar 

  4. Ezkurdia I, Bartoli L, Fariselli P et al (2009) Progress and challenges in predicting protein-protein interaction sites. Brief Bioinform 10:233–246. https://doi.org/10.1093/bib/bbp021

    Article  CAS  PubMed  Google Scholar 

  5. Shoemaker BA, Panchenko AR (2007) Deciphering protein–protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLoS Comput Biol 3:e43. https://doi.org/10.1371/journal.pcbi.0030043

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ruffner H, Bauer A, Bouwmeester T (2007) Human protein-protein interaction networks and the value for drug discovery. Drug Discov Today 12:709–716

    Article  CAS  PubMed  Google Scholar 

  7. Jin L, Wang W, Fang G (2014) Targeting protein-protein interaction by small molecules. Annu Rev Pharmacol Toxicol 54:435–456. https://doi.org/10.1146/annurev-pharmtox-011613-140028

    Article  CAS  PubMed  Google Scholar 

  8. Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664

    Article  CAS  PubMed  Google Scholar 

  9. Bruggeman FJ, Westerhoff H (2007) The nature of systems biology. Trends Microbiol 15:45–50

    Article  CAS  PubMed  Google Scholar 

  10. Westerhoff HV, Palsson BO (2004) The evolution of molecular biology into systems biology. Nat Biotechnol 22:1249–1252

    Article  CAS  PubMed  Google Scholar 

  11. Butcher EC, Berg EL, Kunkel EJ (2004) Systems biology in drug discovery. Nat Biotechnol 22:1253–1259

    Article  CAS  PubMed  Google Scholar 

  12. Hood L, Heath JR, Phelps ME, Lin B (2004) Systems biology and new technologies enable predictive and preventative medicine. Science 306:640–643

    Article  CAS  PubMed  Google Scholar 

  13. Hucka M, Finney A, Sauro HM et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531. https://doi.org/10.1093/bioinformatics/btg015

    Article  CAS  PubMed  Google Scholar 

  14. Patil A, Kinoshita K, Nakamura H (2010) Hub promiscuity in protein-protein interaction networks. Int J Mol Sci 11:1930–1943. https://doi.org/10.3390/ijms11041930

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Jhoti H (2001) High-throughput structural proteomics using x-rays. Trends Biotechnol 19:S67–S71

    Article  CAS  PubMed  Google Scholar 

  16. Protein structure determination in solution by NMR spectroscopy. https://www.jbc.org/content/265/36/22059.short. Accessed 27 Sep 2020

  17. Wagner K, Racaityte K, Unger KK et al (2000) Protein mapping by two-dimensional high performance liquid chromatography. J Chromatogr A 893:293–305. https://doi.org/10.1016/S0021-9673(00)00736-6

    Article  CAS  PubMed  Google Scholar 

  18. Sydor JR, Scalf M, Sideris S et al (2003) Chip-based analysis of protein-protein interactions by fluorescence detection and on-chip immunoprecipitation combined with μLC-MS/MS analysis. Anal Chem 75:6163–6170. https://doi.org/10.1021/ac034258u

    Article  CAS  PubMed  Google Scholar 

  19. Baskaran K, Duarte JM, Biyani N et al (2014) A PDB-wide, evolution-based assessment of protein-protein interfaces. BMC Struct Biol 14:1–11. https://doi.org/10.1186/s12900-014-0022-0

    Article  CAS  Google Scholar 

  20. Berggård T, Linse S, James P (2007) Methods for the detection and analysis of protein-protein interactions. Proteomics 7:2833–2842

    Article  PubMed  CAS  Google Scholar 

  21. Wessels HJCT, Vogel RO, van den Heuvel L et al (2009) LC-MS/MS as an alternative for SDS-PAGE in blue native analysis of protein complexes. Proteomics 9:4221–4228. https://doi.org/10.1002/pmic.200900157

    Article  CAS  PubMed  Google Scholar 

  22. Muronetz VI, Sholukh M, Korpela T (2001) Use of protein-protein interactions in affinity chromatography. J Biochem Biophys Methods 49:29–47. https://doi.org/10.1016/S0165-022X(01)00187-7

    Article  CAS  PubMed  Google Scholar 

  23. Kukar T, Eckenrode S, Gu Y et al (2002) Protein microarrays to detect protein-protein interactions using red and green fluorescent proteins. Anal Biochem 306:50–54. https://doi.org/10.1006/abio.2002.5614

    Article  CAS  PubMed  Google Scholar 

  24. Kanno A, Ozawa T, Umezawa Y (2011) Detection of protein-protein interactions in bacteria by GFP-fragment reconstitution. Methods Mol Biol 705:251–258. https://doi.org/10.1007/978-1-61737-967-3_15

    Article  CAS  PubMed  Google Scholar 

  25. Luban J, Goff SP (1995) The yeast two-hybrid system for studying protein-protein interactions. Curr Opin Biotechnol 6:59–64. https://doi.org/10.1016/0958-1669(95)80010-7

    Article  CAS  PubMed  Google Scholar 

  26. Walhout AJM, Vidal M (2001) High-throughput yeast two-hybrid assays for large-scale protein interaction mapping. Methods 24:297–306. https://doi.org/10.1006/meth.2001.1190

    Article  CAS  PubMed  Google Scholar 

  27. Coates P, Hall P (2003) The yeast two-hybrid system for identifying protein-protein interactions. J Pathol 199:4–7. https://doi.org/10.1002/path.1267

    Article  CAS  PubMed  Google Scholar 

  28. Mehla J, Caufield JH, Uetz P (2015) The yeast two-hybrid system: a tool for mapping protein-protein interactions. Cold Spring Harb Protoc 2015:425–430. https://doi.org/10.1101/pdb.top083345

    Article  PubMed  Google Scholar 

  29. Legrain P, Selig L (2000) Genome-wide protein interaction maps using two-hybrid systems. FEBS Lett 480:32–36

    Article  CAS  PubMed  Google Scholar 

  30. van Criekinge W, Beyaert R (1999) Yeast two-hybrid: state of the art. Biol Proced Online 2:1–38. https://doi.org/10.1251/bpo16

    Article  PubMed  PubMed Central  Google Scholar 

  31. Skrabanek L, Saini HK, Bader GD, Enright AJ (2008) Computational prediction of protein-protein interactions. Mol Biotechnol 38:1–17

    Article  CAS  PubMed  Google Scholar 

  32. Tong AHY, Drees B, Nardelli G et al (2002) A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science 295:321–324. https://doi.org/10.1126/science.1064987

    Article  CAS  PubMed  Google Scholar 

  33. Pitre S, Alamgir M, Green JR et al (2008) Computational methods for predicting protein-protein interactions. Adv Biochem Eng Biotechnol 110:247–267

    CAS  PubMed  Google Scholar 

  34. Martin EPG, Bremer EG, Guerin MC et al (2004) Analysis of protein/protein interactions through biomedical literature: text mining of abstracts vs. text mining of full text articles. In: Lecture notes in artificial intelligence (subseries of Lecture notes in computer science). Springer, Berlin, pp 96–108

    Google Scholar 

  35. Chowdhary R, Zhang J, Tan SL et al (2013) PIMiner: a web tool for extraction of protein interactions from biomedical literature. Int J Data Min Bioinform 7:450. https://doi.org/10.1504/IJDMB.2013.054232

    Article  PubMed  PubMed Central  Google Scholar 

  36. de Bruijn B, Martin J (2002) Getting to the (c)ore of knowledge: mining biomedical literature. Int J Med Inform 67:7

    Article  PubMed  Google Scholar 

  37. Shatkay H, Feldman R (2003) Mining the biomedical literature in the genomic era: an overview. J Comput Biol 10:821–855

    Article  CAS  PubMed  Google Scholar 

  38. Chaussabel D (2004) Biomedical literature mining: challenges and solutions in the “omics” era. Am J Pharmacogenomics 4:383–393

    Article  CAS  PubMed  Google Scholar 

  39. Jensen LJ, Saric J, Bork P (2006) Literature mining for the biologist: from information retrieval to biological discovery. Nat Rev Genet 7:119–129

    Article  CAS  PubMed  Google Scholar 

  40. Zhou D, He Y (2008) Extracting interactions between proteins from the literature. J Biomed Inform 41:393–407

    Article  CAS  PubMed  Google Scholar 

  41. Ramani AK, Bunescu RC, Mooney RJ, Marcotte EM (2005) Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biol 6:1–12. https://doi.org/10.1186/gb-2005-6-5-r40

    Article  Google Scholar 

  42. Krallinger M, Leitner F, Valencia A (2010) Analysis of biological processes and diseases using text mining approaches. Methods Mol Biol 593:341–382

    Article  CAS  PubMed  Google Scholar 

  43. Soong TT, Wrzeszczynski KO, Rost B (2008) Physical protein-protein interactions predicted from microarrays. Bioinformatics 24:2608–2614. https://doi.org/10.1093/bioinformatics/btn498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chua HN, Sung WK, Wong L (2006) Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 22:1623–1630. https://doi.org/10.1093/bioinformatics/btl145

    Article  CAS  PubMed  Google Scholar 

  45. Chua HN, Sung WK, Wong L (2007) Using indirect protein interactions for the prediction of Gene Ontology functions. BMC Bioinformatics 8:1–13. https://doi.org/10.1186/1471-2105-8-S4-S8

    Article  CAS  Google Scholar 

  46. Burger L, van Nimwegen E (2010) Disentangling direct from indirect co-evolution of residues in protein alignments. PLoS Comput Biol 6:1000633. https://doi.org/10.1371/journal.pcbi.1000633

    Article  CAS  Google Scholar 

  47. Chen XW, Liu M (2005) Prediction of protein-protein interactions using random decision forest framework. Bioinformatics 21:4394–4400. https://doi.org/10.1093/bioinformatics/bti721

    Article  CAS  PubMed  Google Scholar 

  48. van Wijk SJL, de Vries SJ, Kemmeren P et al (2009) A comprehensive framework of E2–RING E3 interactions of the human ubiquitin–proteasome system. Mol Syst Biol 5:295. https://doi.org/10.1038/msb.2009.55

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kerrien S, Aranda B, Breuza L et al (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40:D841–D846. https://doi.org/10.1093/nar/gkr1088

    Article  CAS  PubMed  Google Scholar 

  50. Salwinski L, Miller CS, Smith AJ et al (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res 32:D449–D451. https://doi.org/10.1093/nar/gkh086

    Article  PubMed  PubMed Central  Google Scholar 

  51. Keshava Prasad TS, Goel R, Kandasamy K et al (2009) Human protein reference database - 2009 update. Nucleic Acids Res 37:767–772. https://doi.org/10.1093/nar/gkn892

    Article  CAS  Google Scholar 

  52. Zanzoni A, Montecchi-Palazzi L, Quondam M et al (2002) MINT: a molecular INTeraction database. FEBS Lett 513:135–140

    Article  CAS  PubMed  Google Scholar 

  53. Mewes H, Frishman D et al (2002) MIPS: a database for genomes and protein sequences. Nucleic Acids Res 30:31

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Stark C, Breitkreutz B, Reguly T et al (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D35

    Article  CAS  Google Scholar 

  55. Das J, Yu H (2012) HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol 6:92. https://doi.org/10.1186/1752-0509-6-92

    Article  PubMed  PubMed Central  Google Scholar 

  56. Kuhn M, von Mering C, Campillos M et al (2008) STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res 36:D684–D688. https://doi.org/10.1093/nar/gkm795

    Article  CAS  PubMed  Google Scholar 

  57. Szklarczyk D, Morris J, Cook H et al (2017) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 45:D362

    Article  CAS  PubMed  Google Scholar 

  58. Davis F, Sali A (2005) PIBASE: a comprehensive database of structurally defined protein interfaces. Bioinformatics 21:1901

    Article  CAS  PubMed  Google Scholar 

  59. Stein A, Russell R, Aloy P (2005) 3did: interacting protein domains of known three-dimensional structure. Nucleic Acids Res 33:D413

    Article  CAS  PubMed  Google Scholar 

  60. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kutmon M, van Iersel MP, Bohler A et al (2015) PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol 11:e1004085. https://doi.org/10.1371/journal.pcbi.1004085

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Dubois J, Cottret L, Ghozlane A et al (2012) Systrip: a visual environment for the investigation of time-series data in the context of metabolic networks. IEEE, Washington, DC

    Google Scholar 

  63. Shulman-Peleg A, Shatsky M, Nussinov R, Wolfson HJ (2005) MAPPIS: multiple 3D alignment of protein-protein interfaces. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics). Springer, Berlin, pp 91–103

    Google Scholar 

  64. Murakami Y, Mizuguchi K (2018) PSOPIA: toward more reliable protein-protein interaction prediction from sequence information. In: ICIIBMS 2017 - 2nd International Conference on Intelligent Informatics and Biomedical Sciences. Institute of Electrical and Electronics Engineers Inc, Washington, DC, pp 255–261

    Google Scholar 

  65. Prieto C, de Las RJ (2006) APID: agile protein interaction DataAnalyzer. Nucleic Acids Res 34:W298–W302. https://doi.org/10.1093/nar/gkl128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Garcia-Garcia J, Valls-Comamala V, Guney E et al (2017) iFrag: a protein–protein interface prediction server based on sequence fragments. J Mol Biol 429:382–389. https://doi.org/10.1016/j.jmb.2016.11.034

    Article  CAS  PubMed  Google Scholar 

  67. Mosca R, Céol A, Aloy P (2013) Interactome3D: adding structural details to protein networks. Nat Methods 10:47

    Article  CAS  PubMed  Google Scholar 

  68. Segura Mora S, Assi SA, Fernandez-Fuentes N (2010) Presaging critical residues in protein interfaces-web server (PCRPi-W): a web server to chart Hot spots in protein interfaces. PLoS One 5:e12352. https://doi.org/10.1371/journal.pone.0012352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Sukhwal A, Sowdhamini R (2015) PPcheck: a webserver for the quantitative analysis of protein-protein interfaces and prediction of residue hotspots. Bioinformatics Biol Insights 9:141–151. https://doi.org/10.4137/BBI.S25928

    Article  CAS  Google Scholar 

  70. Bösl K, Ianevski A, Than TT et al (2019) Common nodes of virus–host interaction revealed through an integrated network analysis. Front Immunol 10:2186. https://doi.org/10.3389/fimmu.2019.02186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Vignuzzi M, López CB (2019) Defective viral genomes are key drivers of the virus–host interaction. Nat Microbiol 4:1075–1087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Navratil V, de Chassey B, Meyniel L et al (2009) VirHostNet: a knowledge base for the management and the analysis of proteome-wide virus-host interaction networks. Nucleic Acids Res 37:661–668. https://doi.org/10.1093/nar/gkn794

    Article  CAS  Google Scholar 

  73. Enjuanes L, Almazán F, Sola I, Zuñiga S (2006) Biochemical aspects of coronavirus replication and virus-host interaction. Annu Rev Microbiol 60:211–230. https://doi.org/10.1146/annurev.micro.60.080805.142157

    Article  CAS  PubMed  Google Scholar 

  74. Zheng YH, Jeang KT, Tokunaga K (2012) Host restriction factors in retroviral infection: promises in virus-host interaction. Retrovirology 9:112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Cheng F, Liu C, Jiang J et al (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 8(5):e1002503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Yildirim MA, Goh KI, Cusick ME, Barabási AL, Vidal M (2007) Drug-target network. Nat Biotechnol 25(10):1119

    Article  CAS  PubMed  Google Scholar 

  77. Mestres J, Gregori-Puigjané E, Valverde S et al (2009) The topology of drug–target interaction networks: implicit dependence on drug properties and target families. Mol Biosyst 5:1051

    Article  CAS  PubMed  Google Scholar 

  78. Janga S, Tzakos T (2009) Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. Mol Biosyst 5:1536

    Article  CAS  PubMed  Google Scholar 

  79. Vogt I, Mestres J (2010) Drug-target networks. Mol Inform 29:10–14. https://doi.org/10.1002/minf.200900069

    Article  CAS  PubMed  Google Scholar 

  80. Jalencas X, Mestres J (2013) On the origins of drug polypharmacology. Med Chem Commun 4:80

    Article  CAS  Google Scholar 

  81. Boran A, Iyengar R (2010) Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov 13:297

    CAS  Google Scholar 

  82. Peters JU (2013) Polypharmacology - foe or friend? J Med Chem 56:8955–8971

    Article  CAS  PubMed  Google Scholar 

  83. Reddy A, Zhang S (2013) Polypharmacology: drug discovery for the future. Exp Rev Clin Pharmacol 6:41–47

    Article  CAS  Google Scholar 

  84. Awale M, Reymond JL (2017) The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data. J Cheminform 9:11. https://doi.org/10.1186/s13321-017-0199-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Chen B, Wild D, Guha R (2009) PubChem as a source of polypharmacology. J Chem Inf Model 49:2044–2055. https://doi.org/10.1021/ci9001876

    Article  CAS  PubMed  Google Scholar 

  86. Berger S, Iyengar R (2009) Network analyses in systems pharmacology. Bioinformatics 25:2466

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690

    Article  CAS  PubMed  Google Scholar 

  88. Zhang Y, Cheng X, et al 2012 Drug repositioning: an important application of network pharmacology. en.cnki.com.cn

  89. Ye H, Wei J, Tang K, Feuers R, Hong H (2016) Drug repositioning through network pharmacology. Curr Top Med Chem 16:3646. https://doi.org/10.2174/1568026616666160530181328

    Article  CAS  PubMed  Google Scholar 

  90. von Eichborn J, Murgueitio M et al (2010) PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res 39:D1060

    Article  CAS  Google Scholar 

  91. Sydow D, Burggraaff L, Szengel A et al (2019) Advances and challenges in computational target prediction. ACS Publ 59:1728–1742. https://doi.org/10.1021/acs.jcim.8b00832

    Article  CAS  Google Scholar 

  92. Messina F, Giombini E, Agrati C et al (2020) COVID-19: viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection. J Transl Med 18:233. https://doi.org/10.1186/s12967-020-02405-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Hazafa A, ur-Rahman K, Haq I et al (2020) The broad-spectrum antiviral recommendations for drug discovery against COVID-19. Drug Metab Rev 52:408–424

    Article  CAS  PubMed  Google Scholar 

  94. Gordon D, Jang G, Bouhaddou M et al (2020) A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583:459

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Zhou Y, Hou Y, Shen J et al (2020) Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov 6:14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Gysi DM, do Valle Í, Zitnik M et al (2020) Network medicine framework for identifying drug repurposing opportunities for COVID-19. ArXiv

    Google Scholar 

  97. Mu C, Sheng Y, Wang Q et al (2020) Potential compound from herbal food of rhizoma polygonati for treatment of COVID-19 analyzed by network pharmacology and molecular docking technology. J Funct Foods:104149

    Google Scholar 

  98. Sadegh S, Matschinske J, Blumenthal DB et al (2020) Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing. Nat Commun 11:3518

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Dominguez Andres A, Feng Y, Rosa Campos A et al SARS-CoV-2 ORF9c is a membrane-associated protein that suppresses antiviral responses in cells. BioRxiv. https://doi.org/10.1101/2020.08.18.256776

  100. Martin R, Löchel H, Welzel M et al (2020) CORDITE: the curated CORona Drug InTERactions database for SARS-CoV-2. iScience 23:101297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Xu J, Xu X, Jiang L et al (2020) SARS-CoV-2 induces transcriptional signatures in human lung epithelial cells that promote lung fibrosis. Respir Res 21:182. https://doi.org/10.1186/s12931-020-01445-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Kumar S (2020) COVID-19: a drug repurposing and biomarker identification by using comprehensive gene-disease associations through protein-protein interaction network analysis. Preprints. https://doi.org/10.20944/preprints202003.0440.v1

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suresh Kumar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Kumar, S. (2021). Protein–Protein Interaction Network for the Identification of New Targets Against Novel Coronavirus. In: Roy, K. (eds) In Silico Modeling of Drugs Against Coronaviruses. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/7653_2020_62

Download citation

  • DOI: https://doi.org/10.1007/7653_2020_62

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1365-8

  • Online ISBN: 978-1-0716-1366-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics