Prediction of Putative Protein Interactions between Zika Virus and Its Hosts Using Computational Techniques

  • Surendra Kumar Sagar Department of Zoology, Swami Shraddhanand College (University of Delhi), Delhi, India.
  • Manoj Kumar Centre for Economic Studies and Planning, Jawaharlal Nehru University, New Delhi, India.
  • Prithvi Singh Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.
  • Shweta Sankhwar Department of Computer Science, Maitreyi College (University of Delhi), New Delhi, India.
  • Ravins Dohare Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.
Keywords: ZIKV, PPI; hZIKV-similar, Protein Structure, Protein-Interaction Prediction

Abstract

Generally, protein-interaction prediction between the proteins of any host and the virus’s proteins is quite crucial for the infection and the pathogenesis of the virus, which makes it striking target for the development of the therapeutics. The major aim of the present study was to utilize the structure-based approach to predict proteins responsible for the propagation of the ZIKV infection in the host machinery. A computational structure-based approach has been applied for the prediction of interacting proteins. From this methodology, we come up with the interactions which are very crucial for the virus infection propagation into the host’s cellular system. As there is a notable relationship between the Zika virus and the neurodevelopment abnormalities, still there is no specific system underlying which impaired neurological development has not been determined. We encounter some of the interactions which are predicted from the methodology adopted in our work, through which we can say that these are some interactions which cause neuron disorders as the major problem associated with this viral infection.

How to cite this article:
Sagar SK, Kumar M, Singh P, Sankhwar S, Dohare R. Prediction of Putative Protein Interactions between Zika Virus and Its Hosts Using Computational Techniques. J Commun Dis. 2021;53(4):84-96.

DOI: https://doi.org/10.24321/0019.5138.202178

References

Duffy MR, Chen TH, Hancock WT, Powers AM, Kool JL, Lanciotti RS, Pretrick M, Marfel M, Holzbauer S, Dubray C, Guillaumot L, Griggs A, Bel M, Lambert AJ, Laven J, Kosoy O, Panella A, Biggerstaff BJ, Fischer M, Hayes EB. Zika virus outbreak on Yap Island, Federated States of Micronesia. N Engl J Med. 2009 Jun;360(24):2536-43. [PubMed] [Google Scholar]

Hancock WT, Marfel M, Bel M. Zika Virus, French Polynesia, South Pacific, 2013. Emerg Infect Dis. 2014 Nov;20(11):1960. [PubMed] [Google Scholar]

Musso D, Roche C, Robin E, Nhan T, Teissier A, Cao-Lormeau VM. Potential sexual transmission of Zika virus. Emerg Infect Dis. 2015 Feb;21(2):359-61. [PubMed] [Google Scholar]

Doolittle JM, Gomez SM. Mapping protein interactions between dengue virus and its human and insect hosts. PLoS Negl Trop Dis. 2011 Feb;5(2):e954. [PubMed] [Google Scholar]

Lee SA, Chan CH, Tsai CH, Lai JM, Wang FS, Kao CY, Huang CY. Ortholog-based protein-protein interaction prediction and its application to inter-species interactions. BMC Bioinformatics. 2008 Dec;9 Suppl 12(Suppl 12):S11. [PubMed] [Google Scholar]

Dyer MD, Murali TM, Sobral BW. Computational prediction of host-pathogen protein protein interactions. Bioinformatics. 2007 Jul;23(13):i159-66. [PubMed] [Google Scholar]

Evans P, Dampier W, Ungar L, Tozeren A. Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs. BMC Med Genomics. 2009 May 18;2:27. [PubMed] [Google Scholar]

Lu L, Lu H, Skolnick J. MULTIPROSPECTOR: An algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins. 2002 Nov;49(3):350-64. [PubMed] [Google Scholar]

Aloy P, Russell RB. InterPreTS: protein interaction prediction through tertiary structure. Bioinformatics. 2003 Jan;19(1):161-2. [PubMed] [Google Scholar]

Holm L, Sander C. Protein structure comparison by alignment of distance matrices. J Mol Biol. 1993 Sep;233(1):123-38. [PubMed] [Google Scholar]

Holm L, Kääriäinen S, Rosenström P, Schenkel A. Searching protein structure databases with DaliLite v.3. Bioinformatics. 2008 Dec;24(23):2780-1. [PubMed] [Google Scholar]

Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A. Human Protein Reference Database--2009 update. Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72. [PubMed] [Google Scholar]

Yu J, Pacifico S, Liu G, Finley RL Jr. DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions. BMC Genomics. 2008 Oct 7;9:461. [PubMed] [Google Scholar]

Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H, Armstrong J, Bairoch A, Cesareni G, Sherman D, Apweiler R. IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004 Jan;32(Database issue):D452-5. [PubMed] [Google Scholar]

Crosby MA, Goodman JL, Strelets VB, Zhang P, Gelbart WM; FlyBase Consortium. FlyBase: genomes by the dozen. Nucleic Acids Res. 2007 Jan;35(Database issue):D486-91. [PubMed] [Google Scholar]

Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44-57. [PubMed] [Google Scholar]

Berman HM. The protein data bank. Nucleic Acids Res. 2000 Jan;28(1):235-42. [PubMed] [Google Scholar]

Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics. 2008 Jan;9:40. [PubMed] [Google Scholar]

Garcez PP, Nascimento JM, de Vasconcelos JM, Madeiro da Costa R, Delvecchio R, Trindade P, Loiola EC, Higa LM, Cassoli JS, Vitória G, Sequeira PC, Sochacki J, Aguiar RS, Fuzii HT, de Filippis AM, da Silva Gonçalves Vianez Júnior JL, Tanuri A, Martins-de-Souza D, Rehen SK. Zika virus disrupts molecular fingerprinting of human neurospheres. Sci Rep. 2017 Jan;7:40780. [PubMed] [Google Scholar]

Teng Y, Liu S, Guo X, Liu S, Jin Y, He T, Bi D, Zhang P, Lin B, An X, Feng D, Mi Z, Tong Y. An integrative analysis reveals a central role of P53 activation via MDM2 in Zika virus infection induced cell death. Front Cell Infect Microbiol. 2017 Jul;7:327. [PubMed] [Google Scholar]

Garcez PP, Nascimento JM, de Vasconcelos JM, da Costa RM, Delvecchio R, Trindade P, Loiola EC, Higa LM, Cassoli JS, Vitória G, SequeiraPC, Sochacki L, Aguiar RS, Fuzii HS, de Filippis AMB, Vianez Júnior JLSG, Tanuri A, Martins-de-Souza D, Rehen SK. Zika virus disrupts molecular fingerprinting of human neurospheres. Sci Rep 7: 40780. DOI: 10.1038/srep40780. [PubMed] [Google Scholar]

Heaton NS, Randall G. Dengue virus-induced autophagy regulates lipid metabolism. Cell Host Microbe. 2010 Nov;8(5):422-32. [PubMed] [Google Scholar]

Lee J, Shin O. Advances in Zika virus–host cell interaction: current knowledge and future perspectives. Int J Mol Sci. 2019 Mar;20(5):1101. [PubMed] [Google Scholar]

Published
2021-12-31