AccScience Publishing / MI / Online First / DOI: 10.36922/MI025450121
Submit to MI
Cite this article
1
Download
20
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

An in silico approach to design a multi-epitope vaccine against small ruminant lentiviruses causing Maedi-Visna and caprine arthritis encephalitis in sheep and goats

Rumesa Ghazanfar1† ,  Zainab Nagari1† ,  Muhammad Atiq Ur Rehman2† ,  Chandra Sourav3 ,  Muhammad Zeeshan Shabbir1,4*
Show Less
1 Department of Biological Research, BioMind Research Institute, MindSet Development Foundation, Islamabad, Pakistan
2 RI-Biomics, Advanced Radiation Technology Institute, Korean Atomic Energy Research Institute, Jeongeup, Jeollabuk, Republic of Korea
3 Department of Electronics and Informational Engineering, Jeonbuk National University, Jeonju, Jeollabuk, South Korea
4 Department of Animal Breeding and Genetics, Justus Liebig University Giessen, Gießen, Hesse, Germany
†These authors contributed equally to this work.
MI, 025450121 https://doi.org/10.36922/MI025450121
Received: 6 November 2025 | Revised: 26 April 2026 | Accepted: 6 May 2026 | Published online: 9 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

Small ruminant lentiviruses are common viral pathogens affecting livestock, primarily sheep and goats, and are responsible for chronic and fatal diseases such as Maedi-Visna and caprine arthritis encephalitis. These infections compromise livestock productivity. Developing a safe and effective vaccine that provides broad protection across host species remains a major challenge. Consequently, we employed different bioinformatics tools to design a novel multi-epitope vaccine construct based on the envelope gene of the causative lentiviruses. The secondary and tertiary structures of the construct were predicted and subsequently refined. The stability and binding affinity of the vaccine construct were evaluated using advanced computational approaches, including molecular docking and molecular dynamics simulation. Also, the immunogenic potential was assessed through immune simulation studies, followed by codon optimization and in silico cloning. However, in vivo studies are required in the near future to confirm the vaccine construct’s efficacy and immunogenicity.

Graphical abstract
Keywords
Reverse vaccinology
Small ruminant lentiviruses
Molecular docking
Molecular dynamics simulation
Funding
None.
Conflict of interest
The authors declare no conflicts of interest.
References
  1. Mosa AH, Zenad MM. Serological and histopathological detection of Maedi-Visna virus in middle Iraq regions. Plant Arch. 2020;20(2):6339–6343.
  2. Blacklaws BA. Small ruminant lentiviruses: immunopathogenesis of visna-maedi and caprine arthritis and encephalitis virus. Comp Immunol Microbiol Infect Dis. 2012;35(3):259-269. doi:10.1016/j.cimid.2011.12.003
  3. Moroz A, Czopowicz M, Sobczak-Filipiak M, et al. The prevalence of histopathological features of pneumonia in goats with symptomatic caprine arthritis-encephalitis. Pathogens. 2022;11(6):629. doi:10.3390/pathogens11060629
  4. Enache DA, Baraitareanu S, Dan M, et al. Preliminary results of MVV and CAEV seroprevalence in Romanian sheep and goats. Sci Works Series C Vet Med. 2017;63(1):95-100.
  5. Rahman MH, Ahmed E, Haque MN, Hassan MZ, Ali MZ. Major respiratory diseases of goat and their epidemiology, prevention and control. Bangladesh J Livest Res. 2022;29(1- 2):1-20. doi:10.3329/bjlr.v29i1.72031
  6. Blacklaws BA, Berriatua E, Torsteinsdottir S, et al. Transmission of small ruminant lentiviruses. Vet Microbiol. 2004;101(3):199-208. doi:10.1016/j.vetmic.2004.04.006
  7. Minardi da Cruz JC, Singh DK, Lamara A, Chebloune Y. Small ruminant lentiviruses (SRLVs) break the species barrier to acquire new host range. Viruses. 2013;5(7):1867- 1884. doi:10.3390/v5071867
  8. Arcangeli C, Torricelli M, Sebastiani C, et al. Genetic characterization of small ruminant lentiviruses (SRLVs) circulating in naturally infected sheep in Central Italy. Viruses. 2022;14(4):686. doi:10.3390/v14040686
  9. Santry LA, de Jong J, Gold AC, Walsh SR, Menzies PI, Wootton SK. Genetic characterization of small ruminant lentiviruses circulating in naturally infected sheep and goats in Ontario, Canada. Virus Res. 2013;175(1):30-44. doi:10.1016/j.virusres.2013.03.019
  10. Gomez-Lucia E, Barquero N, Domenech A. Maedi-Visna virus: current perspectives. Vet Med. 2018;9:11-21. doi:10.2147/VMRR.S136705
  11. Olech M, Valas S, Kuźmak J. Epidemiological survey in single-species flocks from Poland reveals expanded genetic and antigenic diversity of small ruminant lentiviruses. PLoS ONE. 2018;13(3):e0193892. doi:10.1371/journal.pone.0193892
  12. Pépin M, Vitu C, Russo P, Mornex JF, Peterhans E. Maedi-visna virus infection in sheep: a review. Vet Res. 1998;29(3- 4):341-367.
  13. Querat G, Audoly G, Sonigo P, Vigne R. Nucleotide sequence analysis of SA-OMVV, a visna-related ovine lentivirus: phylogenetic history of lentiviruses. Virology. 1990;175(2):434-447. doi:10.1016/0042-6822(90)90428-T
  14. Molaee V, Bazzucchi M, De Mia GM, et al. Phylogenetic analysis of small ruminant lentiviruses in Germany and Iran suggests their expansion with domestic sheep. Sci Rep. 2020;10(1):2243. doi:10.1038/s41598-020-58990-9
  15. Michiels R, Van Mael E, Quinet C, Adjadj NR, Cay AB, De Regge N. Comparative analysis of different serological and molecular tests for the detection of small ruminant lentiviruses (SRLVs) in Belgian sheep and goats. Viruses. 2018;10(12):696. doi:10.3390/v10120696
  16. Pétursson G, Matthíasdóttir S, Svansson V, et al. Mucosal vaccination with an attenuated maedi–visna virus clone. Vaccine. 2005;23(24):3223-3228. doi:10.1016/j.vaccine.2004.11.074
  17. González B, Reina R, García I, et al. Mucosal immunization of sheep with a Maedi-Visna virus (MVV) env DNA vaccine protects against early MVV productive infection. Vaccine. 2005;23(34):4342-4352. doi:10.1016/j.vaccine.2005.03.032
  18. Torsteinsdóttir S, Carlsdóttir HM, Svansson V, Matthíasdóttir S, Martin AH, Pétursson G. Vaccination of sheep with Maedi-visna virus gag gene and protein, beneficial or harmful? Vaccine. 2007;25(37-38):6713-6720. doi:10.1016/j.vaccine.2007.07.004
  19. De Andres X, Reina R, Ciriza J, et al. Use of B7 costimulatory molecules as adjuvants in a prime-boost vaccination against Visna–Maedi ovine lentivirus. Vaccine. 2009;27(34):4591- 4600. doi:10.1016/j.vaccine.2009.05.080
  20. Koçkaya ES, Can H, Yaman Y, Ün C. In silico discovery of epitopes of gag and env proteins for the development of a multi-epitope vaccine candidate against Maedi Visna virus using reverse vaccinology approach. Biologicals. 2023;84:101715. doi:10.1016/j.biologicals.2023.101715
  21. Cheevers WP, Knowles DP, McGuire TC, Baszler TV, Hullinger GA. Caprine arthritis-encephalitis lentivirus (CAEV) challenge of goats immunized with recombinant vaccinia virus expressing CAEV surface and transmembrane envelope glycoproteins. Vet Immunol Immunopathol. 1994;42(3-4):237-251. doi:10.1016/0165-2427(94)90070-1
  22. Sievers F, Higgins DG. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 2018;27(1):135-145. doi:10.1002/pro.3290
  23. Troshin PV, Procter JB, Barton GJ. Java bioinformatics analysis web services for multiple sequence alignment— JABAWS: MSA. Bioinformatics. 2011;27(14):2001-2002. doi:10.1093/bioinformatics/btr304
  24. Hall T. BioEdit: an important software for molecular biology. GERF Bull Biosci. 2011;2(1):60-61.
  25. Saha S, Raghava GPS. Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network. Proteins. 2006;65(1):40-48. doi:10.1002/prot.21078
  26. Saha S, Raghava GPS. BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. In: Flower DR, Dimitrov I, eds. Immunoinformatics: Predicting Immunogenicity in Silico. Berlin, Heidelberg: Springer; 2004:197-204.
  27. Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45(W1):W24-W29. doi:10.1093/nar/gkx346
  28. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 2007;8(1):4. doi:10.1186/1471-2105-8-4
  29. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v. 2—a server for in silico prediction of allergens. J Mol Model. 2014;20(6):2278. doi:10.1007/s00894-014-2278-5
  30. Sharma N, Naorem LD, Jain S, Raghava GP. ToxinPred2: an improved method for predicting toxicity of proteins. Brief Bioinform. 2022;23(5):bbac174. doi:10.1093/bib/bbac174
  31. Singh H, Raghava GPS. ProPred: prediction of HLA-DR binding sites. Bioinformatics. 2001;17(12):1236-1237. doi:10.1093/bioinformatics/17.12.1236
  32. Doytchinova IA, Guan P, Flower DR. EpiJen: a server for multistep T cell epitope prediction. BMC Bioinformatics. 2006;7(1):131. doi:10.1186/1471-2105-7-131
  33. Trolle T, Metushi IG, Greenbaum JA, et al. Automated benchmarking of peptide-MHC class I binding predictions. Bioinformatics. 2015;31(13):2174-2181. doi:10.1093/bioinformatics/btv123
  34. Lybeck K, Tollefsen S, Mikkelsen H, et al. Selection of vaccine-candidate peptides from Mycobacterium avium subsp. paratuberculosis by in silico prediction, in vitro T-cell line proliferation, and in vivo immunogenicity. Front Immunol. 2024;15:1297955. doi:10.3389/fimmu.2024.1297955
  35. Gaafar BB, Ali SA, Abd-Elrahman KA, Almofti YA. Immunoinformatics approach for multiepitope vaccine prediction from H, M, F, and N proteins of Peste des petits ruminants virus. J Immunol Res. 2019;2019:6124030. doi:10.1155/2019/6124030
  36. Oladipo EK, Taiwo OR, Teniola FO, et al. Immunoinformatics approach to Rift Valley fever virus vaccine design in ruminants. Biomed Res Ther. 2024;11(2):6233-6247. doi:10.15419/bmrat.v11i2.869
  37. Yang Y, Wei Z, Cia G, et al. MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods. Front Immunol. 2024;15:1293706. doi:10.3389/fimmu.2024.1293706
  38. Ponomarenko J, Bui HH, Li W, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinform. 2008;9(1):514. doi:10.1186/1471-2105-9-514
  39. Crowe J, Masone BS, Ribbe J. One-step purification of recombinant proteins with the 6xHis tag and Ni-NTA resin. Mol Biotechnol. 1995;4(3):247-258. doi:10.1007/bf02779018
  40. Gasteiger E, Hoogland C, Gattiker A, et al. Protein identification and analysis tools on the ExPASy server. In: Walker JM, eds. The Proteomics Protocols Handbook. Totowa, NJ: Humana Press; 2005:571-607.
  41. Magnan CN, Randall A, Baldi P. SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics. 2009;25(17):2200-2207. doi:10.1093/bioinformatics/btp386
  42. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics. 1995;11(6):681-684. doi:10.1093/bioinformatics/11.6.681
  43. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics. 2000;16(4):404- 405. doi:10.1093/bioinformatics/16.4.404
  44. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinform. 2008;9(1):40. doi:10.1186/1471-2105-9-40
  45. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr. 1993;26:283-291. doi:10.1107/S0021889892009944
  46. Heo L, Park H, Seok C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 2013;41(W1):W384-W388. doi:10.1093/nar/gkt458
  47. 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(Suppl 2):W407-W410. doi:10.1093/nar/gkm290
  48. Kozakov D, Hall DR, Xia B, et al. The ClusPro web server for protein–protein docking. Nat Protoc. 2017;12(2):255-278. doi:10.1038/nprot.2016.169
  49. Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein–protein complexes. Bioinformatics. 2016;32(23):3676-3678. doi:10.1093/bioinformatics/btw514
  50. Krissinel E, Henrick K. Inference of macromolecular assemblies from crystalline state. J Mol Biol. 2007;372(3):774- 797. doi:10.1016/j.jmb.2007.05.022
  51. Pettersen EF, Goddard TD, Huang CC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004;25(13):1605-1612. doi:10.1002/jcc.20084
  52. Seeliger D, de Groot BL. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des. 2010;24(5):417-422. doi:10.1007/s10822-010-9352-6
  53. Laskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: Structural summaries of PDB entries. Protein Sci. 2018;27(1):129-134. doi:10.1002/pro.3289
  54. Kosloff D, Kosloff R. A Fourier method solution for the time dependent Schrödinger equation as a tool in molecular dynamics. J Comput Phys. 1983;52(1):35-53. doi:10.1016/0021-9991(83)90015-3
  55. Rapin N, Lund O, Castiglione F. Immune system simulation online. Bioinformatics. 2011;27(14):2013-2014. doi:10.1093/bioinformatics/btr335
  56. 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(Suppl 2):W526-W531. doi:10.1093/nar/gki376
Next article in this issue
Share
Back to top
Microbes & Immunity, Electronic ISSN: 3029-2883 Print ISSN: 3041-0886, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept