Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Immunoinformatics and Reverse Vaccinology Driven Predication of a Multi-epitope Vaccine against Borrelia burgdorferi and Validation through in silico Cloning and Immune Simulation

Author(s): Zulfiqar Hussain, Chandni Hayat, Muhammad Shahab, Ramin Sikandar, Haleema Bibi, Atif Kamil, Guojun Zheng* and Chaoqun Liang

Volume 29, Issue 19, 2023

Published on: 27 June, 2023

Page: [1504 - 1515] Pages: 12

DOI: 10.2174/1381612829666230418104520

Price: $65

Abstract

Background: Borrelia burgdorferi is regarded as an extremely dangerous bacteria causing infectious disease in humans, resulting in musculoskeletal pain, fatigue, fever and cardiac symptom. Because of all alarming concerns, no such prophylaxis setup has been available against Borrelia burgdorferi till now. In fact, vaccine construction using traditional methods is so expensive and time-consuming. Therefore, considering all concerns, we designed a multi-epitope-based vaccine design against Borrelia burgdorferi using in silico approaches.

Objective: To design an effective and safe vaccine that can activate cell-mediated and humoral immunity against Borrelia burgdorferi by using various bioinformatics tools.

Methods: The present study utilized different computational methodologies, covering different ideas and elements in bioinformatics tools. The protein sequence of Borrelia burgdorferi was retrieved from the NCBI database. Different B and T cell epitopes were predicated using the IEDB tool. Efficient B and T cell epitopes were further assessed for vaccine construction using linkers AAY, EAAAK and GPGPG, respectively. Furthermore, the tertiary structure of constructed vaccine was predicated, and its interaction was determined with TLR9 using ClusPro software. In addition, further atomic level detail of docked complex and their immune response were further determined by MD simulation and C-ImmSim tool, respectively.

Results: A protein with immunogenic potential and good vaccine properties (candidate) was identified based on high binding scores, low percentile rank, non-allergenicity and good immunological properties, which were further used to calculate epitopes. Additionally, molecular docking possesses strong interaction; seventeen H-bonds interactions were reported, such as THR101-GLU264, THR185-THR270, ARG 257-ASP210, ARG 257-ASP 210, ASP259-LYS 174, ASN263-GLU237, CYS 265-GLU 233, CYS 265-TYR 197, GLU267- THR202, GLN 270-THR202, TYR345-ASP 210, TYR345-THR 213, ARG 346-ASN209, SER350- GLU141, SER350-GLU141, ASP 424-ARG220 and ARG426-THR216 with TLR-9. Finally, high expression was determined in E. coli (CAI = (0.9045), and GC content = (72%)). Using the IMOD server, all-atom MD simulations of docked complex affirmed its significant stability. The outcomes of immune simulation indicate that both T and B cells represent a strong response to the vaccination component.

Conclusion: This type of in-silico technique may precisely decrease valuable time and expenses in vaccine designing against Borrelia burgdorferi for experimental planning in laboratories. Currently, scientists frequently utilize bioinformatics approaches that speed up their vaccine-based lab work.

« Previous Next »
[1]
Sapi E, Priyanka AST, Truc VP, et al. Effect of RpoN, RpoS and LuxS pathways on the biofilm formation and antibiotic sensitivity of Borrelia burgdorferi. Eur J Microbiol Immunol (Bp) 2016; 6(4): 272-86.
[http://dx.doi.org/10.1556/1886.2016.00026]
[2]
Trevisan G, Cinco M, Trevisini S, et al. Borreliae part 1: Borrelia Lyme group and Echidna-reptile group. Biology 2021; 10(10): 1036.
[http://dx.doi.org/10.3390/biology10101036] [PMID: 34681134]
[3]
Walter L, Sürth V, Röttgerding F, Zipfel PF, Fritz-Wolf K, Kraiczy P. Elucidating the immune evasion mechanisms of Borrelia mayonii, the causative agent of Lyme disease. Front Immunol 2019; 10: 2722.
[http://dx.doi.org/10.3389/fimmu.2019.02722] [PMID: 31849943]
[4]
Rizzoli A, Hauffe HC, Carpi G, Vourc’h GI, Neteler M, Rosà R. Lyme borreliosis in Europe. Euro Surveill 2011; 16(27): 19906.
[http://dx.doi.org/10.2807/ese.16.27.19906-en] [PMID: 21794218]
[5]
Halperin JJ. Diagnosis and management of Lyme neuroborreliosis. Expert Rev Anti Infect Ther 2018; 16(1): 5-11.
[http://dx.doi.org/10.1080/14787210.2018.1417836]
[6]
Dumic I, Vitorovic D, Spritzer S, Sviggum E, Patel J, Ramanan P. Acute transverse myelitis - A rare clinical manifestation of Lyme neuroborreliosis. IDCases 2019; 15: e00479.
[http://dx.doi.org/10.1016/j.idcr.2018.e00479] [PMID: 30622896]
[7]
Hussain S, Hussain A, Aziz U, et al. The role of ticks in the emergence of Borrelia burgdorferi as a zoonotic pathogen and its vector control: A global systemic review. Microorganisms 2021; 9(12): 2412.
[http://dx.doi.org/10.3390/microorganisms9122412] [PMID: 34946014]
[8]
Wormser GP, Dattwyler RJ, Shapiro ED, et al. The clinical assessment, treatment, and prevention of lyme disease, human granulocytic anaplasmosis, and babesiosis: Clinical practice guidelines by the Infectious Diseases Society of America. Clin Infect Dis 2006; 43(9): 1089-134.
[http://dx.doi.org/10.1086/508667] [PMID: 17029130]
[9]
Diuk-Wasser MA, Vannier E, Krause PJ. Coinfection by Ixodes tick-borne pathogens: Ecological, epidemiological, and clinical consequences. Trends Parasitol 2016; 32(1): 30-42.
[http://dx.doi.org/10.1016/j.pt.2015.09.008] [PMID: 26613664]
[10]
Hersh MH, Ostfeld RS, McHenry DJ, et al. Co-infection of blacklegged ticks with Babesia microti and Borrelia burgdorferi is higher than expected and acquired from small mammal hosts. PLoS One 2014; 9(6): e99348.
[http://dx.doi.org/10.1371/journal.pone.0099348] [PMID: 24940999]
[11]
Curcio SR, Tria LP, Gucwa AL. Seroprevalence of Babesia microti in individuals with Lyme disease. Vector Borne Zoonotic Dis 2016; 16(12): 737-43.
[http://dx.doi.org/10.1089/vbz.2016.2020] [PMID: 27911694]
[12]
Yoshinari NH, Bonoldi VLN, Bonin S, Falkingham E, Trevisan G. The current state of knowledge on baggio-yoshinari syndrome (Brazilian Lyme Disease-like Illness): Chronological Presentation of Historical and Scientific Events Observed over the Last 30 Years. Pathogens 2022; 11(8): 889.
[http://dx.doi.org/10.3390/pathogens11080889] [PMID: 36015013]
[13]
Rosenberg R, Lindsey NP, Fischer M, et al. Vital signs: Trends in reported vectorborne disease cases-United States and Territories, 2004-2016. MMWR Morb Mortal Wkly Rep 2018; 67(17): 496-501.
[http://dx.doi.org/10.15585/mmwr.mm6717e1] [PMID: 29723166]
[14]
Schwartz AM, Kugeler KJ, Nelson CA, Marx GE, Hinckley AF. Use of commercial claims data for evaluating trends in Lyme disease diagnoses, United States, 2010-2018. Emerg Infect Dis 2021; 27(2): 499-507.
[http://dx.doi.org/10.3201/eid2702.202728] [PMID: 33496238]
[15]
Sykes RA, Makiello P. An estimate of Lyme borreliosis incidence in Western Europe. Am J Public Health 2017; 39(1): 74-81.
[16]
Oli AN, Wilson OO, Martins OI. Immunoinformatics and vaccine development: An overview. ImmunoTargets The 2020; 9: 13-30.
[http://dx.doi.org/10.2147/ITT.S241064]
[17]
Yang Z, Bogdan P, Nazarian S. An in silico deep learning approach to multi-epitope vaccine design: A SARS-CoV-2 case study. Sci Rep 2021; 11(1): 3238.
[http://dx.doi.org/10.1038/s41598-021-81749-9] [PMID: 33547334]
[18]
Harris PA, Taylor R, Minor BL, et al. The REDCap consortium. Building an international community of software platform partners. J Biomed Inform 2019; 95: 103208.
[http://dx.doi.org/10.1016/j.jbi.2019.103208] [PMID: 31078660]
[19]
Rappuoli R, Bottomley MJ, D’Oro U, Finco O, De Gregorio E. Reverse vaccinology 2.0: Human immunology instructs vaccine antigen design. J Exp Med 2016; 213(4): 469-81.
[http://dx.doi.org/10.1084/jem.20151960] [PMID: 27022144]
[20]
Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007; 8(1): 4.
[http://dx.doi.org/10.1186/1471-2105-8-4] [PMID: 17207271]
[21]
Dimitrov I, Naneva L, Doytchinova I, Bangov I, Allergen FP. Allergenicity prediction by descriptor fingerprints. Bioinformatics 2014; 30(6): 846-51.
[http://dx.doi.org/10.1093/bioinformatics/btt619] [PMID: 24167156]
[22]
Saadi M, Karkhah A, Nouri HR. Development of a multi-epitope peptide vaccine inducing robust T cell responses against brucellosis using immunoinformatics based approaches. Infect Genet Evol 2017; 51: 227-34.
[http://dx.doi.org/10.1016/j.meegid.2017.04.009] [PMID: 28411163]
[23]
Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 2922: 195-202.
[24]
Vita R, Overton JA, Greenbaum JA, et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res 2015; 43(D1): D405-12.
[http://dx.doi.org/10.1093/nar/gku938] [PMID: 25300482]
[25]
Liang Z, Zhu H, Wang X, et al. Adjuvants for coronavirus vaccines. Front Immunol 2020; 11: 589833.
[http://dx.doi.org/10.3389/fimmu.2020.589833] [PMID: 33240278]
[26]
Bhattacharya M, Ashish RS, Prasanta P. Development of epitope‐based peptide vaccine against novel coronavirus 2019 (SARS‐CoV‐2): Immunoinformatics approach. J Med Virol 2020; 92(6): 618-31.
[27]
Wiederstein M, Sippl MJ. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007; 35(Web Server): W407-10.
[http://dx.doi.org/10.1093/nar/gkm290] [PMID: 17517781]
[28]
Vajda S, Yueh C, Beglov D, et al. New additions to the C lus P ro server motivated by CAPRI. Proteins 2017; 85(3): 435-44.
[http://dx.doi.org/10.1002/prot.25219] [PMID: 27936493]
[29]
López-Blanco JR, Aliaga JI, Quintana-Ortí ES, Chacón P. iMODS: Internal coordinates normal mode analysis server. Nucleic Acids Res 2014; 42(W1): W271-6.
[http://dx.doi.org/10.1093/nar/gku339] [PMID: 24771341]
[30]
Grote A, Hiller K, Scheer M, et al. JCat: A novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 2005; 33(S2): W526-31.
[http://dx.doi.org/10.1093/nar/gki376] [PMID: 15980527]
[31]
Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: The use of prediction tools for molecular binding in the simulation of the immune system. PLoS One 2010; 5(4): e9862.
[http://dx.doi.org/10.1371/journal.pone.0009862] [PMID: 20419125]
[32]
Sette A, Fikes J. Epitope-based vaccines: An update on epitope identification, vaccine design and delivery. Curr Opin Immunol 2003; 15(4): 461-70.
[http://dx.doi.org/10.1016/S0952-7915(03)00083-9] [PMID: 12900280]
[33]
Chauhan V, Rungta T, Goyal K, Singh MP. Designing a multiepitope based vaccine to combat Kaposi sarcoma utilizing immunoinformatics approach. Sci Rep 2019; 9(1): 2517.
[http://dx.doi.org/10.1038/s41598-019-39299-8] [PMID: 30626917]
[34]
Lu C, Meng S, Jin Y, et al. A novel multi-epitope vaccine from MMSA-1 and DKK1 for multiple myeloma immunotherapy. Br J Haematol 2017; 178(3): 413-26.
[http://dx.doi.org/10.1111/bjh.14686] [PMID: 28508448]
[35]
He R, Yang X, Liu C, et al. Efficient control of chronic LCMV infection by a CD4 T cell epitope-based heterologous prime-boost vaccination in a murine model. Cell Mol Immunol 2018; 15(9): 815-26.
[http://dx.doi.org/10.1038/cmi.2017.3] [PMID: 28287115]
[36]
Cao Y, Li D, Fu Y, et al. Rational design and efficacy of a multi-epitope recombinant protein vaccine against foot-and-mouth disease virus serotype A in pigs. Antiviral Res 2017; 140: 133-41.
[http://dx.doi.org/10.1016/j.antiviral.2017.01.023] [PMID: 28161579]
[37]
Zhou WY, Shi Y, Wu C, et al. Therapeutic efficacy of a multi-epitope vaccine against Helicobacter pylori infection in BALB/c mice model. Vaccine 2009; 27(36): 5013-9.
[http://dx.doi.org/10.1016/j.vaccine.2009.05.009] [PMID: 19446591]
[38]
Guo L, Yin R, Liu K, et al. Immunological features and efficacy of a multi-epitope vaccine CTB-UE against H. pylori in BALB/c mice model. Appl Microbiol Biotechnol 2014; 98(8): 3495-507.
[http://dx.doi.org/10.1007/s00253-013-5408-6] [PMID: 24370888]
[39]
Jiang P, Cai Y, Chen J, et al. Evaluation of tandem Chlamydia trachomatis MOMP multi-epitopes vaccine in BALB/c mice model. Vaccine 2017; 35(23): 3096-103.
[http://dx.doi.org/10.1016/j.vaccine.2017.04.031] [PMID: 28456528]
[40]
Slingluff CL Jr, Lee S, Zhao F, et al. A randomized phase II trial of multiepitope vaccination with melanoma peptides for cytotoxic T cells and helper T cells for patients with metastatic melanoma (E1602). Clin Cancer Res 2013; 19(15): 4228-38.
[http://dx.doi.org/10.1158/1078-0432.CCR-13-0002] [PMID: 23653149]
[41]
Lennerz V, Gross S, Gallerani E, et al. Immunologic response to the survivin-derived multi-epitope vaccine EMD640744 in patients with advanced solid tumors. Cancer Immunol Immunother 2014; 63(4): 381-94.
[http://dx.doi.org/10.1007/s00262-013-1516-5] [PMID: 24487961]
[42]
Toledo H, Baly A, Castro O, et al. A phase I clinical trial of a multi-epitope polypeptide TAB9 combined with Montanide ISA 720 adjuvant in non-HIV-1 infected human volunteers. Vaccine 2001; 19(30): 4328-36.
[http://dx.doi.org/10.1016/S0264-410X(01)00111-6] [PMID: 11457560]
[43]
Shahab M, Hayat C, Sikandar R, Zheng G, Akter S. In silico designing of a multi-epitope vaccine against Burkholderia pseudomallei: Reverse vaccinology and immunoinformatics. J Genet Eng Biotechnol 2022; 20(1): 100.
[http://dx.doi.org/10.1186/s43141-022-00379-4] [PMID: 35821357]
[44]
Foroutan M, Ghaffarifar F, Sharifi Z, Dalimi A. Vaccination with a novel multi-epitope ROP8 DNA vaccine against acute Toxoplasma gondii infection induces strong B and T cell responses in mice. Comp Immunol Microbiol Infect Dis 2020; 69: 101413.
[http://dx.doi.org/10.1016/j.cimid.2020.101413] [PMID: 31954995]
[45]
Totura AL, Whitmore A, Agnihothram S, et al. Toll-like receptor 3 signaling via TRIF contributes to a protective innate immune response to severe acute respiratory syndrome coronavirus infection. MBio 2015; 6(3): e00638-15.
[http://dx.doi.org/10.1128/mBio.00638-15] [PMID: 26015500]
[46]
Hu W, Yen YT, Singh S, Kao CL, Wu-Hsieh BA. SARS-CoV regulates immune function-related gene expression in human monocytic cells. Viral Immunol 2012; 25(4): 277-88.
[http://dx.doi.org/10.1089/vim.2011.0099] [PMID: 22876772]
[47]
Ullah MA, Sarkar B, Islam SS. Exploiting the reverse vaccinology approach to design novel subunit vaccines against Ebola virus. Immunobiology 2020; 225(3): 151949.
[http://dx.doi.org/10.1016/j.imbio.2020.151949] [PMID: 32444135]
[48]
Solanki V, Sharma S, Tiwari V. Subtractive proteomics and reverse vaccinology strategies for designing a multiepitope vaccine targeting membrane proteins of Klebsiella pneumoniae. Int J Pept Res Ther 2021; 27(2): 1177-95.
[http://dx.doi.org/10.1007/s10989-021-10159-2]
[49]
Zhang L. Multi-epitope vaccines: A promising strategy against tumors and viral infections. Cell Mol Immunol 2018; 15(2): 182-4.
[http://dx.doi.org/10.1038/cmi.2017.92] [PMID: 28890542]
[50]
Solanki V, Tiwari V. Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii. Sci Rep 2018; 8(1): 9044.
[http://dx.doi.org/10.1038/s41598-018-26689-7] [PMID: 29899345]
[51]
Hayat C, Muhammad S, Salman AL, et al. Design of a novel multiple epitope-based vaccine: An immunoinformatics approach to combat monkeypox. J Biomol Struct Dyn 2022; 1-12.
[http://dx.doi.org/10.1080/07391102.2022.2141887]
[52]
Shahab M, Alzahrani AK, Duan X, et al. An immunoinformatics approach to design novel and potent multi-epitope-based vaccine to target lumpy skin disease. Biomedicines 2023; 11(2): 398.
[http://dx.doi.org/10.3390/biomedicines11020398]
[53]
Akter S, Shahab M, Sarkar MMH, et al. Immunoinformatics approach to epitope-based vaccine design against the SARS-CoV-2 in Bangladeshi patients. J Genet Eng Biotechnol 2022; 20(1): 136.
[http://dx.doi.org/10.1186/s43141-022-00410-8] [PMID: 36125645]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy