• WHO (2019) Number of new leprosy cases. https://apps.who.int/neglected_diseases/ntddata/leprosy/leprosy.html. Accessed 9 Aug 2020.

  • Lazo-Porras M, Prutsky GJ, Barrionuevo P et al (2020) World Health Organization (WHO) antibiotic regimen against other regimens for the treatment of leprosy: a systematic review and meta-analysis. BMC Infect Dis 20(1):1–14. https://doi.org/10.1186/S12879-019-4665-0.

    Article 

    Google Scholar
     

  • Rambukkana A (1979) Role of α-dystroglycan as a Schwann cell receptor for Mycobacterium leprae. Science 282:2076–2079. https://doi.org/10.1126/science.282.5396.2076.

    Article 

    Google Scholar
     

  • Duthie MS, Pena MT, Ebenezer GJ et al (2018) LepVax, a defined subunit vaccine that provides effective pre-exposure and post-exposure prophylaxis of M. leprae infection. NPJ Vaccines 3:12. https://doi.org/10.1038/s41541-018-0050-z.

    Article 

    Google Scholar
     

  • Rambukkana A (2004) Mycobacterium leprae-induced demyelination: a model for early nerve degeneration. Curr Opin Immunol 16:511–518. https://doi.org/10.1016/j.coi.2004.05.021.

    Article 

    Google Scholar
     

  • Mi Z, Liu H, Zhang F (2020) Advances in the immunology and genetics of leprosy. Front Immunol 11:567. https://doi.org/10.3389/fimmu.2020.00567.

    Article 

    Google Scholar
     

  • Nath I, Saini C, Valluri VL (2015) Immunology of leprosy and diagnostic challenges. Clin Dermatol 33:90–98. https://doi.org/10.1016/j.clindermatol.2014.07.005.

    Article 

    Google Scholar
     

  • Sharma R, Singh P, McCoy RC et al (2020) Isolation of Mycobacterium lepromatosis and development of molecular diagnostic assays to distinguish Mycobacterium leprae and M. lepromatosis. Clin Infect Dis 71:e262–e269. https://doi.org/10.1093/cid/ciz1121.

    Article 

    Google Scholar
     

  • de Sousa JR, de Sousa RPM, de Souza Aarão TL et al (2016) In situ expression of M2 macrophage subpopulation in leprosy skin lesions. Acta Trop 157:108–114. https://doi.org/10.1016/j.actatropica.2016.01.008.

    Article 

    Google Scholar
     

  • Froes LAR, Trindade MAB, Sotto MN (2020) Immunology of leprosy. Int Rev Immunol 1–21.https://doi.org/10.1080/08830185.2020.1851370.

  • Talhari C, Talhari S, Penna GO (2015) Clinical aspects of leprosy. Clin Dermatol 33:26–37. https://doi.org/10.1016/j.clindermatol.2014.07.002.

    Article 

    Google Scholar
     

  • Franco-Paredes C, Rodriguez-Morales AJ (2016) Unsolved matters in leprosy: a descriptive review and call for further research. Ann Clin Microbiol Antimicrob 15:33. https://doi.org/10.1186/s12941-016-0149-x.

    Article 

    Google Scholar
     

  • Roset Bahmanyar E, Smith WC, Brennan P et al (2016) Leprosy diagnostic test development as a prerequisite towards elimination: requirements from the user’s perspective. PLoS Negl Trop Dis 10:e0004331. https://doi.org/10.1371/journal.pntd.0004331.

    Article 

    Google Scholar
     

  • Duthie MS, Goto W, Ireton GC et al (2007) Use of protein antigens for early serological diagnosis of leprosy. Clin Vaccine Immunol 14:1400–1408. https://doi.org/10.1128/CVI.00299-07.

    Article 

    Google Scholar
     

  • Kumar A, Parkash O, Girdhar BK (2014) Analysis of antigens of Mycobacterium leprae by interaction to sera IgG, IgM, and IgA response to improve diagnosis of leprosy. Biomed Res Int 2014:1–10. https://doi.org/10.1155/2014/283278.

    Article 

    Google Scholar
     

  • van Hooij A, TjonKon Fat EM, van den Eeden SJF et al (2017) Field-friendly serological tests for determination of M leprae-specific antibodies. Sci Rep 7:8868. https://doi.org/10.1038/s41598-017-07803-7.

    Article 

    Google Scholar
     

  • van Hooij A, Tjon Kon Fat EM, Batista da Silva M et al (2018) Evaluation of immunodiagnostic tests for leprosy in Brazil China and Ethiopia. Sci Rep 8:17920. https://doi.org/10.1038/s41598-018-36323-1.

    Article 

    Google Scholar
     

  • Regional Office for South-East Asia, World Health Organization (2016) Global Leprosy Strategy 2016-2020: Accelerating towards a leprosy-free world. WHO Regional Office for South-East Asia. https://apps.who.int/iris/handle/10665/208824

  • Britton WJ, Lockwood DNJ (2004) Leprosy. The Lancet 363:1209–1219. https://doi.org/10.1016/S0140-6736(04)15952-7.

    Article 

    Google Scholar
     

  • Sampaio LH, Stefani MM, Oliveira RM et al (2011) Immunologically reactive M. leprae antigens with relevance to diagnosis and vaccine development. BMC Infect Dis 11:26. https://doi.org/10.1186/1471-2334-11-26.

    Article 

    Google Scholar
     

  • Duthie MS, Hay MN, Morales CZ et al (2010) Rational design and evaluation of a multiepitope chimeric fusion protein with the potential for leprosy diagnosis. Clin Vaccine Immunol 17:298–303. https://doi.org/10.1128/CVI.00400-09.

    Article 

    Google Scholar
     

  • Davies M, Flower D (2007) Harnessing bioinformatics to discover new vaccines. Drug Discov Today 12:389–395. https://doi.org/10.1016/j.drudis.2007.03.010.

    Article 

    Google Scholar
     

  • de Souza MQ, Galdino AS, dos Santos JC et al (2013) A recombinant multiepitope protein for hepatitis B diagnosis. Biomed Res Int 2013:1–7. https://doi.org/10.1155/2013/148317.

    Article 

    Google Scholar
     

  • Acevedo GR, Juiz NA, Ziblat A et al (2020) In silico guided discovery of novel class I and II Trypanosoma cruzi epitopes recognized by T cells from Chagas’ disease patients. J Immunol 204:1571–1581. https://doi.org/10.4049/jimmunol.1900873.

    Article 

    Google Scholar
     

  • de Serpa Brandão RMS, Faria AR, de Andrade HM et al (2018) Novel recombinant multiepitope proteins for the detection of anti-Cryptococcus antibodies. Future Microbiol 13:429–436. https://doi.org/10.2217/fmb-2017-0184.

    Article 

    Google Scholar
     

  • Oliveira TR, Longhi MT, de Morais ZM et al (2008) Evaluation of leptospiral recombinant antigens MPL17 and MPL21 for serological diagnosis of leptospirosis by enzyme-linked immunosorbent assays. Clin Vaccine Immunol 15:1715–1722. https://doi.org/10.1128/CVI.00214-08.

    Article 

    Google Scholar
     

  • Yin D, Li L, Song X et al (2016) A novel multi-epitope recombined protein for diagnosis of human brucellosis. BMC Infect Dis 16:219. https://doi.org/10.1186/s12879-016-1552-9.

    Article 

    Google Scholar
     

  • Duthie MS, Guderian JA, Vallur AC et al (2016) Multi-epitope proteins for improved serological detection of Trypanosoma cruzi infection and Chagas disease. Diagn Microbiol Infect Dis 84:191–196. https://doi.org/10.1016/j.diagmicrobio.2015.11.006.

    Article 

    Google Scholar
     

  • Heidari S, Hajjaran H, Kazemi B et al (2021) Identification of immunodominant proteins of Leishmania infantum by immunoproteomics to evaluate a recombinant multi-epitope designed antigen for serodiagnosis of human visceral leishmaniasis. Exp Parasitol 222:108065. https://doi.org/10.1016/j.exppara.2021.108065.

    Article 

    Google Scholar
     

  • Jameie F, Dalimi A, Pirestani M, Mohebali M (2020) Detection of leishmania infantum infection in reservoir dogs using a multiepitope recombinant protein (PQ10). Arch Razi Inst 75:327–338. https://doi.org/10.22092/ari.2019.126524.1346.

    Article 

    Google Scholar
     

  • Gutiérrez-Ortega A, Moreno DA, Ferrari SA et al (2021) High-yield production of major T-cell ESAT6-CFP10 fusion antigen of M. tuberculosis complex employing codon-optimized synthetic gene. Int J Biol Macromol 171:82–88. https://doi.org/10.1016/j.ijbiomac.2020.12.179.

    Article 

    Google Scholar
     

  • Ebrahimi M, Seyyedtabaei SJ, Ranjbar MM et al (2020) Designing and modeling of multi-epitope proteins for diagnosis of Toxocara canis infection. Int J Pept Res Ther 26:1371–1380. https://doi.org/10.1007/s10989-019-09940-1.

    Article 

    Google Scholar
     

  • Napoleão-Pêgo P, Carneiro FRG, Durans AM et al (2021) Performance assessment of a multi-epitope chimeric antigen for the serological diagnosis of acute Mayaro fever. Sci Rep 11:15374. https://doi.org/10.1038/s41598-021-94817-x.

    Article 

    Google Scholar
     

  • Ahmad I, Nawaz N, Darwesh NM et al (2018) Overcoming challenges for amplified expression of recombinant proteins using Escherichia coli. Protein Expr Purif 144:12–18. https://doi.org/10.1016/j.pep.2017.11.005.

    Article 

    Google Scholar
     

  • Lockwood DNJ (2019) Chronic aspects of leprosy—neglected but important. Trans R Soc Trop Med Hyg 113:813–817. https://doi.org/10.1093/trstmh/try131.

    Article 

    Google Scholar
     

  • Soares BA, Teixeira KN, de Santana JF et al (2021) Epitope mapping from Mycobacterium leprae proteins: convergent data from in silico and in vitro approaches for serodiagnosis of leprosy. Mol Immunol 138:48–57. https://doi.org/10.1016/j.molimm.2021.07.021.

    Article 

    Google Scholar
     

  • Jaiswal AKJ, Tiwari S, Jamal SB et al (2021) Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against Mycobacterium leprae and Mycobacterium lepromatosis. J Venom Anim Toxins Incl Trop Dis. https://doi.org/10.1590/1678-9199-jvatitd-2020-0027.

    Article 

    Google Scholar
     

  • (2021) Home – Protein – NCBI. https://www.ncbi.nlm.nih.gov/protein/. Accessed 20 Jun 2021.

  • Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8:4. https://doi.org/10.1186/1471-2105-8-4.

    Article 

    Google Scholar
     

  • Martini S, Nielsen M, Peters B, Sette A (2020) The Immune Epitope Database and Analysis Resource Program 2003–2018: reflections and outlook. Immunogenetics 72:57–76. https://doi.org/10.1007/s00251-019-01137-6.

    Article 

    Google Scholar
     

  • Vita R, Mahajan S, Overton JA et al (2019) The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res 47:D339–D343. https://doi.org/10.1093/nar/gky1006.

    Article 

    Google Scholar
     

  • Greenbaum J, Sidney J, Chung J et al (2011) Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes. Immunogenetics 63:325–335. https://doi.org/10.1007/s00251-011-0513-0.

    Article 

    Google Scholar
     

  • Larsen MV, Lundegaard C, Lamberth K et al (2005) An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur J Immunol 35:2295–2303. https://doi.org/10.1002/eji.200425811.

    Article 

    Google Scholar
     

  • Larsen M, v, Lundegaard C, Lamberth K, et al (2007) Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics 8:424. https://doi.org/10.1186/1471-2105-8-424.

    Article 

    Google Scholar
     

  • Jensen KK, Andreatta M, Marcatili P et al (2018) Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154:394–406. https://doi.org/10.1111/imm.12889.

    Article 

    Google Scholar
     

  • Nielsen M, Lund O (2009) NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics 10:296. https://doi.org/10.1186/1471-2105-10-296.

    Article 

    Google Scholar
     

  • Galanis KA, Nastou KC, Papandreou NC, et al (2019) Linear B-cell epitope prediction: a performance review of currently available methods. bioRxiv 833418.

  • Chen J, Liu H, Yang J, Chou K-C (2007) Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33:423–428. https://doi.org/10.1007/s00726-006-0485-9.

    Article 

    Google Scholar
     

  • Singh H, Ansari HR, Raghava GPS (2013) Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PLoS ONE 8:e62216. https://doi.org/10.1371/journal.pone.0062216.

    Article 

    Google Scholar
     

  • Saha S, Raghava GPS (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65:40–48. https://doi.org/10.1002/prot.21078.

    Article 

    Google Scholar
     

  • EL-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting flexible length linear B-cell epitopes. In: Computational systems bioinformatics. Published by Imperial College Press and distributed by World Scientific Publishing Co., pp 121–132.

  • Calis JJA, Maybeno M, Greenbaum JA et al (2013) Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 9:e1003266. https://doi.org/10.1371/journal.pcbi.1003266.

    Article 

    Google Scholar
     

  • Chauhan V, Rungta T, Goyal K, Singh MP (2019) Designing a multi-epitope based vaccine to combat Kaposi Sarcoma utilizing immunoinformatics approach. Sci Rep 9:2517. https://doi.org/10.1038/s41598-019-39299-8.

    Article 

    Google Scholar
     

  • Gasteiger E, Hoogland C, Gattiker A et al (2005) Protein identification and analysis tools on the ExPASy server. The Proteomics Protocols Handbook. Humana Press, Totowa, pp 571–607.

    Chapter 

    Google Scholar
     

  • Hebditch M, Carballo-Amador MA, Charonis S et al (2017) Protein-Sol: a web tool for predicting protein solubility from sequence. Bioinformatics 33:3098–3100. https://doi.org/10.1093/bioinformatics/btx345.

    Article 

    Google Scholar
     

  • Källberg M, Wang H, Wang S et al (2012) Template-based protein structure modeling using the RaptorX web server. Nat Protoc 7:1511–1522. https://doi.org/10.1038/nprot.2012.085.

    Article 

    Google Scholar
     

  • Yang J, Zhang Y (2015) Protein structure and function prediction using I-TASSER. Curr Protoc in Bioinformatics 52:5.8.1-5.8.15. https://doi.org/10.1002/0471250953.bi0508s52.

    Article 

    Google Scholar
     

  • Bowie JU, Lüthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional stucture. Science 253:164–170. https://doi.org/10.1126/science.1853201.

    Article 

    Google Scholar
     

  • Lüthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356:83–85. https://doi.org/10.1038/356083a0.

    Article 

    Google Scholar
     

  • Kelley LA, Mezulis S, Yates CM et al (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858. https://doi.org/10.1038/nprot.2015.053.

    Article 

    Google Scholar
     

  • Ko J, Park H, Heo L, Seok C (2012) GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res 40:W294–W297. https://doi.org/10.1093/nar/gks493.

    Article 

    Google Scholar
     

  • Dhanda SK, Vir P, Raghava GPS (2013) Designing of interferon-gamma inducing MHC class-II binders. Biol Direct 8.https://doi.org/10.1186/1745-6150-8-30.

  • Dhanda SK, Gupta S, Vir P, Raghava GPS (2013) Prediction of IL4 inducing peptides. Clin Dev Immunol 2013:1–9. https://doi.org/10.1155/2013/263952.

    Article 

    Google Scholar
     

  • Nagpal G, Usmani SS, Dhanda SK et al (2017) Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci Rep 7:42851. https://doi.org/10.1038/srep42851.

    Article 

    Google Scholar
     

  • Gupta S, Madhu MK, Sharma AK, Sharma VK (2016) ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins. J Transl Med 14:178. https://doi.org/10.1186/s12967-016-0928-3.

    Article 

    Google Scholar
     

  • Ponomarenko J, Bui H-H, Li W et al (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 9:514. https://doi.org/10.1186/1471-2105-9-514.

    Article 

    Google Scholar
     

  • Ferdous S, Kelm S, Baker TS et al (2019) B-cell epitopes: discontinuity and conformational analysis. Mol Immunol 114:643–650. https://doi.org/10.1016/j.molimm.2019.09.014.

    Article 

    Google Scholar
     

  • Insightful Science (2020) SnapGene® software.


    Google Scholar
     

  • Cole ST, Eiglmeier K, Parkhill J et al (2001) Massive gene decay in the leprosy bacillus. Nature 409:1007–1011. https://doi.org/10.1038/35059006.

    Article 

    Google Scholar
     

  • Chen X, Zaro JL, Shen W-C (2013) Fusion protein linkers: property, design and functionality. Adv Drug Deliv Rev 65:1357–1369. https://doi.org/10.1016/j.addr.2012.09.039.

    Article 

    Google Scholar
     

  • Mirzapour A, Seyyed Tabaei SJ, Bandehpour M et al (2020) Designing a recombinant multi-epitope antigen of Echinococcus granulosus to diagnose human cystic echinococcosis. Iran J Parasitol 15:1–10.


    Google Scholar
     

  • Duthie MS, Pena MT, Khandhar AP et al (2020) Development of LepReact, a defined skin test for paucibacillary leprosy and low-level M. leprae infection. Appl Microbiol Biotechnol 104:3971–3979. https://doi.org/10.1007/s00253-020-10505-2.

    Article 

    Google Scholar
     

  • Sampaio LH, Sousa ALM, Barcelos MC et al (2012) Evaluation of various cytokines elicited during antigen-specific recall as potential risk indicators for the differential development of leprosy. Eur J Clin Microbiol Infect Dis 31:1443–1451. https://doi.org/10.1007/s10096-011-1462-0.

    Article 

    Google Scholar
     

  • Mori T, Sakatani M, Yamagishi F et al (2004) Specific detection of tuberculosis infection. Am J Respir Crit Care Med 170:59–64. https://doi.org/10.1164/rccm.200402-179OC.

    Article 

    Google Scholar
     

  • Lalvani A, Pareek M (2010) Interferon gamma release assays: principles and practice. Enferm Infecc Microbiol Clin 28:245–252. https://doi.org/10.1016/j.eimc.2009.05.012.

    Article 

    Google Scholar
     

  • Sadhu S, Mitra DK (2018) Emerging concepts of adaptive immunity in leprosy. Front Immunol 9.https://doi.org/10.3389/fimmu.2018.00604.

  • Weiss DI, Do TH, De BJ et al (2016) Adaptive immune response in leprosy. International Text Book of Leprosy.


    Google Scholar
     

  • Fonseca AB de L, Simon M do V, Cazzaniga RA et al (2017) The influence of innate and adaptative immune responses on the differential clinical outcomes of leprosy. Infect Dis Poverty 6:5. https://doi.org/10.1186/s40249-016-0229-3.

    Article 

    Google Scholar
     

  • Alibakhshi A, Bandehpour M, Sharifnia Z, Kazemi B (2020) The development and evaluation of a multi-epitope antigen as a serodiagnostic marker of Toxoplasma gondii infection. Adv Clin Exp Med 29:669–675. https://doi.org/10.17219/acem/104554.

    Article 

    Google Scholar
     

  • Yin D, Bai Q, Li L et al (2021) Study on immunogenicity and antigenicity of a novel brucella multiepitope recombined protein. Biochem Biophys Res Commun 540:37–41. https://doi.org/10.1016/j.bbrc.2020.12.098.

    Article 

    Google Scholar
     

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