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Infectious Disorders - Drug Targets

Editor-in-Chief

ISSN (Print): 1871-5265
ISSN (Online): 2212-3989

Research Article

Immuno-informatics-based Identification of Novel Potential B Cell and T Cell Epitopes to Fight Zika Virus Infections

Author(s): Wahiba Ezzemani, Marc P. Windisch, Anass Kettani, Haya Altawalah, Jalal Nourlil, Soumaya Benjelloun and Sayeh Ezzikouri*

Volume 21, Issue 4, 2021

Published on: 10 August, 2020

Page: [572 - 581] Pages: 10

DOI: 10.2174/1871526520666200810153657

Price: $65

Abstract

Background: Globally, the recent outbreak of Zika virus (ZIKV) in Brazil, Asia Pacific, and other countries highlighted the unmet medical needs. Currently, there are neither effective vaccines nor therapeutics available to prevent or treat ZIKV infection.

Objective: In this study, we aimed to design an epitope-based vaccine for ZIKV using an in silico approach to predict and analyze B- and T-cell epitopes.

Methods: The prediction of the most antigenic epitopes has targeted the capsid and envelope proteins as well as non-structural proteins NS5 and NS3 using immune-informatics tools PROTPARAM, CFSSP, PSIPRED, and Vaxijen v2.0. B and T-cell epitopes were predicted using ABCpred, IEDB, TepiTool, and their toxicity was evaluated using ToxinPred. The 3-dimensional epitope structures were generated by PEP-FOLD. Energy minimization was performed using Swiss- Pdb Viewer, and molecular docking was conducted using PatchDock and FireDock server.

Results: As a result, we predicted 307 epitopes of MHCI (major histocompatibility complex class I) and 102 epitopes of MHCII (major histocompatibility complex class II). Based on immunogenicity and antigenicity scores, we identified the four most antigenic MHC I epitopes: MVLAILAFLR (HLA-A*68:01), ETLHGTVTV (HLA-A*68:02), DENHPYRTW (HLA-B*44:02), QEGVFH TMW (HLA-B*44:03) and TASGRVIEEW (HLA-B*58:01), and MHC II epitopes: IIKKFKKDLAAMLRI (HLA-DRB3*02:02), ENSKMMLELDPPFGD (HLA-DRB3*01:01), HAET WFFDENHPYRT (HLA-DRB3*01:01), TDGVYRVMTRRLLGS (HLA-DRB1*11:01), and DGCW YGMEIRPRKEP (HLA-DRB5*01:01).

Conclusion: This study provides novel potential B cell and T cell epitopes to fight against Zika virus infections and may prompt further development of vaccines against ZIKV and other emerging infectious diseases. However, further investigations for protective immune response by in vitro and in vivo studies to ratify immunogenicity, the safety of the predicted structure, and ultimately for the vaccine properties to prevent ZIKV infections are warranted.

Keywords: Zika virus, peptide vaccine, in silico, immuno-informatics, epitopes, molecular docking.

Graphical Abstract
[1]
Atif, M.; Azeem, M.; Sarwar, M.R.; Bashir, A. Zika virus disease: a current review of the literature. Infection, 2016, 44(6), 695-705.
[http://dx.doi.org/10.1007/s15010-016-0935-6] [PMID: 27510169]
[2]
WHO. Zika virus 21 May, 2020. Available from: http://www.who.int/news-room/fact-sheets/detail/zika-virus
[3]
Singh, A.; Jana, N.K. Discovery of potential Zika virus RNA polymerase inhibitors by docking-based virtual screening. Comput. Biol. Chem., 2017, 71, 144-151.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.10.007] [PMID: 29096380]
[4]
Pettersson, J.H.; Bohlin, J.; Dupont-Rouzeyrol, M.; Brynildsrud, O.B.; Alfsnes, K.; Cao-Lormeau, V.M.; Gaunt, M.W.; Falconar, A.K.; de Lamballerie, X.; Eldholm, V.; Musso, D.; Gould, E.A. Re-visiting the evolution, dispersal and epidemiology of Zika virus in Asia. Emerg. Microbes Infect., 2018, 7(1), 79.
[http://dx.doi.org/10.1038/s41426-018-0082-5] [PMID: 29739925]
[5]
Musso, D.; Gubler, D.J. Zika Virus. Clin. Microbiol. Rev., 2016, 29(3), 487-524.
[http://dx.doi.org/10.1128/CMR.00072-15] [PMID: 27029595]
[6]
Stephen, P.; Baz, M.; Boivin, G.; Lin, S.X. Structural Insight into NS5 of Zika Virus Leading to the Discovery of MTase Inhibitors. J. Am. Chem. Soc., 2016, 138(50), 16212-16215.
[http://dx.doi.org/10.1021/jacs.6b10399] [PMID: 27998085]
[7]
Zou, J.; Shi, P-Y. Strategies for Zika drug discovery. Curr. Opin. Virol., 2019, 35, 19-26.
[http://dx.doi.org/10.1016/j.coviro.2019.01.005] [PMID: 30852345]
[8]
Grubor-Bauk, B.; Wijesundara, D.K.; Masavuli, M.; Abbink, P.; Peterson, R.L.; Prow, N.A.; Larocca, R.A.; Mekonnen, Z.A.; Shrestha, A.; Eyre, N.S.; Beard, M.R.; Gummow, J.; Carr, J.; Robertson, S.A.; Hayball, J.D.; Barouch, D.H.; Gowans, E.J. NS1 DNA vaccination protects against Zika infection through T cell-mediated immunity in immunocompetent mice. Sci. Adv., 2019, 5(12), eaax2388.
[http://dx.doi.org/10.1126/sciadv.aax2388] [PMID: 31844662]
[9]
Li, A.; Yu, J.; Lu, M.; Ma, Y.; Attia, Z.; Shan, C.; Xue, M.; Liang, X.; Craig, K.; Makadiya, N.; He, J.J.; Jennings, R.; Shi, P.Y.; Peeples, M.E.; Liu, S.L.; Boyaka, P.N.; Li, J. A Zika virus vaccine expressing premembrane-envelope-NS1 polyprotein. Nat. Commun., 2018, 9(1), 3067.
[http://dx.doi.org/10.1038/s41467-018-05276-4] [PMID: 30076287]
[10]
Russo, F.B.; Jungmann, P.; Beltrão-Braga, P.C.B. Zika infection and the development of neurological defects. Cell. Microbiol., 2017, 19(6), 19.
[http://dx.doi.org/10.1111/cmi.12744] [PMID: 28370966]
[11]
Richner, J.M.; Diamond, M.S. Zika virus vaccines: immune response, current status, and future challenges. Curr. Opin. Immunol., 2018, 53, 130-136.
[http://dx.doi.org/10.1016/j.coi.2018.04.024] [PMID: 29753210]
[12]
Delgado, F.G.; Torres, K.I.; Castellanos, J.E.; Romero-Sánchez, C.; Simon-Lorière, E.; Sakuntabhai, A.; Roth, C. Improved Immune Responses Against Zika Virus After Sequential Dengue and Zika Virus Infection in Humans. Viruses, 2018, 10(9), 10.
[http://dx.doi.org/10.3390/v10090480] [PMID: 30205518]
[13]
Berman, H.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.; Weissig, H.; Shindyalov, I.; Bourne, P. The protein data Bank nucleic acids research, www. rcsb. org2000, 28, 235-242..
[14]
Chen, C.; Huang, H.; Wu, C.H. Protein bioinformatics databases and resources.Protein Bioinformatics; Springer, 2017, pp. 3-39..
[http://dx.doi.org/10.1007/978-1-4939-6783-4_1]
[15]
Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F.T.; de Beer, T.A.P.; Rempfer, C.; Bordoli, L.; Lepore, R.; Schwede, T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res., 2018, 46(W1), W296-W303.
[http://dx.doi.org/10.1093/nar/gky427] [PMID: 29788355]
[16]
Gasteiger, E.; Hoogland, C.; Gattiker, A.; Wilkins, M.R.; Appel, R.D.; Bairoch, A. Protein identification and analysis tools on the ExPASy server.The proteomics protocols handbook; Springer, 2005, pp. 571-607.
[http://dx.doi.org/10.1385/1-59259-890-0:571]
[17]
Kumar, T.A. CFSSP: Chou and Fasman secondary structure prediction server. Wide Spectrum, 2013, 1, 15-19.
[18]
Buchan, D.W.A.; Jones, D.T. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res., 2019, 47(W1), W402-W407.
[http://dx.doi.org/10.1093/nar/gkz297] [PMID: 31251384]
[19]
Doytchinova, I.A.; Flower, D.R. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics, 2007, 8, 4.
[http://dx.doi.org/10.1186/1471-2105-8-4] [PMID: 17207271]
[20]
Saha, S.; Raghava, G.P. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins, 2006, 65(1), 40-48.
[http://dx.doi.org/10.1002/prot.21078] [PMID: 16894596]
[21]
Ponomarenko, J.; Bui, H.H.; Li, W.; Fusseder, N.; Bourne, P.E.; Sette, A.; Peters, B. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics, 2008, 9, 514.
[http://dx.doi.org/10.1186/1471-2105-9-514] [PMID: 19055730]
[22]
Vita, R.; Mahajan, S.; Overton, J.A.; Dhanda, S.K.; Martini, S.; Cantrell, J.R.; Wheeler, D.K.; Sette, A.; Peters, B. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res., 2019, 47(D1), D339-D343.
[http://dx.doi.org/10.1093/nar/gky1006] [PMID: 30357391]
[23]
Paul, S; Sidney, J; Sette, A; Peters, B. TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates. Curr Protoc Immunol, 2016, 114, 18.19.1-18 19 24.
[24]
Wang, P.; Sidney, J.; Kim, Y.; Sette, A.; Lund, O.; Nielsen, M.; Peters, B. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics, 2010, 11, 568.
[http://dx.doi.org/10.1186/1471-2105-11-568] [PMID: 21092157]
[25]
Wang, P.; Sidney, J.; Dow, C.; Mothé, B.; Sette, A.; Peters, B. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLOS Comput. Biol., 2008, 4(4), e1000048.
[http://dx.doi.org/10.1371/journal.pcbi.1000048] [PMID: 18389056]
[26]
Karosiene, E.; Rasmussen, M.; Blicher, T.; Lund, O.; Buus, S.; Nielsen, M. NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ. Immunogenetics, 2013, 65(10), 711-724.
[http://dx.doi.org/10.1007/s00251-013-0720-y] [PMID: 23900783]
[27]
Nielsen, M.; Lundegaard, C.; Blicher, T.; Peters, B.; Sette, A.; Justesen, S.; Buus, S.; Lund, O. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLOS Comput. Biol., 2008, 4(7), e1000107.
[http://dx.doi.org/10.1371/journal.pcbi.1000107] [PMID: 18604266]
[28]
Calis, J.J.; Maybeno, M.; Greenbaum, J.A.; Weiskopf, D.; De Silva, A.D.; Sette, A.; Keşmir, C.; Peters, B. Properties of MHC class I presented peptides that enhance immunogenicity. PLOS Comput. Biol., 2013, 9(10), e1003266.
[http://dx.doi.org/10.1371/journal.pcbi.1003266] [PMID: 24204222]
[29]
Gupta, S.; Kapoor, P.; Chaudhary, K.; Gautam, A.; Kumar, R.; Raghava, G.P.; Raghava, G.P. Open Source Drug Discovery Consortium. In silico approach for predicting toxicity of peptides and proteins. PLoS One, 2013, 8(9), e73957.
[http://dx.doi.org/10.1371/journal.pone.0073957] [PMID: 24058508]
[30]
Guex, N; Peitsch, MC SWISS‐MODEL and the Swiss‐Pdb Viewer: an environment for comparative protein modeling. Electrophoresis , 1997, 18, 2714-2723.
[31]
Shen, Y.; Maupetit, J.; Derreumaux, P.; Tufféry, P. Improved PEP-FOLD approach for peptide and miniprotein structure prediction. J. Chem. Theory Comput., 2014, 10(10), 4745-4758.
[http://dx.doi.org/10.1021/ct500592m] [PMID: 26588162]
[32]
Thévenet, P.; Shen, Y.; Maupetit, J.; Guyon, F.; Derreumaux, P.; Tufféry, P. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res., 2012, 40(Web Server issue), W288-W293.
[http://dx.doi.org/10.1093/nar/gks419] [PMID: 22581768]
[33]
Schneidman-Duhovny, D.; Inbar, Y.; Nussinov, R.; Wolfson, H.J. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res., 2005, 33(Web Server issue), W363-W367.
[http://dx.doi.org/10.1093/nar/gki481] [PMID: 15980490]
[34]
Andrusier, N.; Nussinov, R.; Wolfson, H.J. FireDock: fast interaction refinement in molecular docking. Proteins, 2007, 69(1), 139-159.
[http://dx.doi.org/10.1002/prot.21495] [PMID: 17598144]
[35]
Mashiach, E.; Schneidman-Duhovny, D.; Andrusier, N.; Nussinov, R.; Wolfson, H.J. FireDock: a web server for fast interaction refinement in molecular docking. Nucleic Acids Res., 2008, 36(Web Server issue), W229-W232.
[http://dx.doi.org/10.1093/nar/gkn186] [PMID: 18424796]
[36]
De Groot, A.S.; Sbai, H.; Aubin, C.S.; McMurry, J.; Martin, W. Immuno-informatics: Mining genomes for vaccine components. Immunol. Cell Biol., 2002, 80(3), 255-269.
[http://dx.doi.org/10.1046/j.1440-1711.2002.01092.x] [PMID: 12067413]
[37]
Satyam, R.; Janahi, E.M.; Bhardwaj, T.; Somvanshi, P.; Haque, S.; Najm, M.Z. In silico identification of immunodominant B-cell and T-cell epitopes of non-structural proteins of Usutu Virus. Microb. Pathog., 2018, 125, 129-143.
[http://dx.doi.org/10.1016/j.micpath.2018.09.019] [PMID: 30217517]
[38]
Panda, S.; Chandra, G. Physicochemical characterization and functional analysis of some snake venom toxin proteins and related non-toxin proteins of other chordates. Bioinformation, 2012, 8(18), 891-896.
[http://dx.doi.org/10.6026/97320630008891] [PMID: 23144546]
[39]
Pandey, R.K.; Bhatt, T.K.; Prajapati, V.K. Novel Immunoinformatics Approaches to Design Multi-epitope Subunit Vaccine for Malaria by Investigating Anopheles Salivary Protein. Sci. Rep., 2018, 8(1), 1125.
[http://dx.doi.org/10.1038/s41598-018-19456-1] [PMID: 29348555]
[40]
Peters, B.; Sidney, J.; Bourne, P.; Bui, H.H.; Buus, S.; Doh, G.; Fleri, W.; Kronenberg, M.; Kubo, R.; Lund, O.; Nemazee, D.; Ponomarenko, J.V.; Sathiamurthy, M.; Schoenberger, S.P.; Stewart, S.; Surko, P.; Way, S.; Wilson, S.; Sette, A. The design and implementation of the immune epitope database and analysis resource. Immunogenetics, 2005, 57(5), 326-336.
[http://dx.doi.org/10.1007/s00251-005-0803-5] [PMID: 15895191]
[41]
Amrun, S.N.; Yee, W.X.; Abu Bakar, F.; Lee, B.; Kam, Y.W.; Lum, F.M.; Tan, J.J.; Lim, V.W.; Watthanaworawit, W.; Ling, C.; Nosten, F.; Renia, L.; Leo, Y.S.; Ng, L.F. Novel differential linear B-cell epitopes to identify Zika and dengue virus infections in patients. Clin. Transl. Immunology, 2019, 8(7), e1066.
[http://dx.doi.org/10.1002/cti2.1066] [PMID: 31372218]
[42]
Fleri, W.; Paul, S.; Dhanda, S.K.; Mahajan, S.; Xu, X.; Peters, B.; Sette, A. The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Front. Immunol., 2017, 8, 278.
[http://dx.doi.org/10.3389/fimmu.2017.00278] [PMID: 28352270]
[43]
Halder, S.T.; Dhorajiwala, T.M.; Samant, L.R. Multiple docking analysis and In silico absorption, distribution, metabolism, excretion, and toxicity screening of anti-leprosy phytochemicals and dapsone against dihydropteroate synthase of Mycobacterium leprae. Int. J. Mycobacteriol., 2019, 8(3), 229-236.
[http://dx.doi.org/10.4103/ijmy.ijmy_123_19] [PMID: 31512598]
[44]
Duhovny, D.; Nussinov, R.; Wolfson, H.J. Efficient unbound docking of rigid molecules. International workshop on algorithms in bioinformatics., 2002, , pp. 185-200.
[http://dx.doi.org/10.1007/3-540-45784-4_14]
[45]
Nair, A.S.; Dhar, P.K.; Nayarisseri, A. Epitope characterization and docking studies on Chikungunya viral Envelope 2 protein. Int J Sci Res Pub, 2015, 5(2), 1-9.
[46]
Zhang, X.; Jia, R.; Shen, H.; Wang, M.; Yin, Z.; Cheng, A. Structures and functions of the envelope glycoprotein in flavivirus infections. Viruses, 2017, 9(11), 338.
[http://dx.doi.org/10.3390/v9110338] [PMID: 29137162]
[47]
Collins, M.H.; Tu, H.A.; Gimblet-Ochieng, C.; Liou, G.A.; Jadi, R.S.; Metz, S.W.; Thomas, A.; McElvany, B.D.; Davidson, E.; Doranz, B.J.; Reyes, Y.; Bowman, N.M.; Becker-Dreps, S.; Bucardo, F.; Lazear, H.M.; Diehl, S.A.; de Silva, A.M. Human antibody response to Zika targets type-specific quaternary structure epitopes. JCI Insight, 2019, 4(8), 4.
[http://dx.doi.org/10.1172/jci.insight.124588] [PMID: 30996133]
[48]
Bailey, M.J.; Broecker, F.; Freyn, A.W.; Choi, A.; Brown, J.A.; Fedorova, N.; Simon, V.; Lim, J.K.; Evans, M.J.; García-Sastre, A.; Palese, P.; Tan, G.S. Human Monoclonal Antibodies Potently Neutralize Zika Virus and Select for Escape Mutations on the Lateral Ridge of the Envelope Protein. J. Virol., 2019, 93(14), 93.
[http://dx.doi.org/10.1128/JVI.00405-19] [PMID: 31043537]
[49]
Wen, J.; Tang, W.W.; Sheets, N.; Ellison, J.; Sette, A.; Kim, K.; Shresta, S. Identification of Zika virus epitopes reveals immunodominant and protective roles for dengue virus cross-reactive CD8+ T cells. Nat. Microbiol., 2017, 2, 17036.
[http://dx.doi.org/10.1038/nmicrobiol.2017.36] [PMID: 28288094]
[50]
Elong Ngono, A.; Vizcarra, E.A.; Tang, W.W.; Sheets, N.; Joo, Y.; Kim, K.; Gorman, M.J.; Diamond, M.S.; Shresta, S. Mapping and Role of the CD8+ T Cell Response During Primary Zika Virus Infection in Mice. Cell Host Microbe, 2017, 21(1), 35-46.
[http://dx.doi.org/10.1016/j.chom.2016.12.010] [PMID: 28081442]
[51]
Godoy, A.S.; Lima, G.M.; Oliveira, K.I.; Torres, N.U.; Maluf, F.V.; Guido, R.V.; Oliva, G. Crystal structure of Zika virus NS5 RNA-dependent RNA polymerase. Nat. Commun., 2017, 8, 14764.
[http://dx.doi.org/10.1038/ncomms14764] [PMID: 28345596]
[52]
Wang, B.; Thurmond, S.; Hai, R.; Song, J. Structure and function of Zika virus NS5 protein: perspectives for drug design. Cell. Mol. Life Sci., 2018, 75(10), 1723-1736.
[http://dx.doi.org/10.1007/s00018-018-2751-x] [PMID: 29423529]
[53]
Dar, H.; Zaheer, T.; Rehman, M.T.; Ali, A.; Javed, A.; Khan, G.A.; Babar, M.M.; Waheed, Y. Prediction of promiscuous T-cell epitopes in the Zika virus polyprotein: An in silico approach. Asian Pac. J. Trop. Med., 2016, 9(9), 844-850.
[http://dx.doi.org/10.1016/j.apjtm.2016.07.004] [PMID: 27633296]
[54]
Prasasty, V.D.; Grazzolie, K.; Rosmalena, R.; Yazid, F.; Ivan, F.X.; Sinaga, E. Peptide-Based subunit vaccine design of t- and b-cells multi-epitopes against zika virus using immunoinformatics approaches. Microorganisms, 2019, 7(8), 226.
[http://dx.doi.org/10.3390/microorganisms7080226] [PMID: 31370224]

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