Articles In Press
Back to the articles list |
Back to browse issues page
Arya Dwi Wijaya Saputra1 
, Nadaa Ramadhia Hariyanto1 , Tharisa Sabrina Ardya Gharini1 , Maharani Retna Duhita2
, Nadaa Ramadhia Hariyanto1 , Tharisa Sabrina Ardya Gharini1 , Maharani Retna Duhita2
1- Department Biology, Faculty of Science and Technology, UIN Maulana Malik Ibrahim Malang
2- Department Biology, Faculty of Science and Technology, UIN Maulana Malik Ibrahim Malang ,maharaniretna.duhita@uin-malang.ac.id
2- Department Biology, Faculty of Science and Technology, UIN Maulana Malik Ibrahim Malang ,
Abstract: (313 Views)
Background and Objective: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge, with existing vaccines like BCG showing limited efficacy, particularly in adults. This highlights the urgent need for innovative vaccine designs. In silico approaches offer significant advantages, such as enhanced time efficiency, reduced costs, and the ability to predict epitope properties with high precision, making them a valuable tool for developing next-generation vaccines. This study identifies and characterizes the ERv 57-64 epitope as a potential peptide-based vaccine candidate.
Methods: Epitope selection was based on antigenic regions within the ERv protein sequence. ERv 53–63 was chosen due to higher predicted antigenicity and favorable binding potential. VaxiJen was used for antigenicity assessment, AllerTOP v2.0 for allergenicity, and ToxinPred for toxicity evaluation. Structural docking simulations were conducted using PyMOL, with a human B-cell receptor (PDB ID: 5DRW) as the docking target to evaluate epitope–receptor interaction.
Results: ERv 53-63 demonstrated high antigenicity (VaxiJen score: 0.9599), was predicted as non-allergenic and non-toxic, and exhibited strong binding affinity with the B-cell receptor (interaction energy: –877.8 kcal/mol), indicating stable complex formation.
Conclusion: These findings support ERv 53–63 as a novel and promising in silico-derived B-cell epitope, outperforming prior candidates such as ERv 105–118. It holds strong potential for peptide-based TB vaccine development. Further in vitro and in vivo studies are recommended to validate its immunogenicity and safety.
Methods: Epitope selection was based on antigenic regions within the ERv protein sequence. ERv 53–63 was chosen due to higher predicted antigenicity and favorable binding potential. VaxiJen was used for antigenicity assessment, AllerTOP v2.0 for allergenicity, and ToxinPred for toxicity evaluation. Structural docking simulations were conducted using PyMOL, with a human B-cell receptor (PDB ID: 5DRW) as the docking target to evaluate epitope–receptor interaction.
Results: ERv 53-63 demonstrated high antigenicity (VaxiJen score: 0.9599), was predicted as non-allergenic and non-toxic, and exhibited strong binding affinity with the B-cell receptor (interaction energy: –877.8 kcal/mol), indicating stable complex formation.
Conclusion: These findings support ERv 53–63 as a novel and promising in silico-derived B-cell epitope, outperforming prior candidates such as ERv 105–118. It holds strong potential for peptide-based TB vaccine development. Further in vitro and in vivo studies are recommended to validate its immunogenicity and safety.
Keywords: ER 53-63 epitope, B cell receptor, in silico vaccine design, peptide-based vaccine, tuberculosis
Type of Study: Original Research Article |
Subject:
Microbial Bioinformatics
Received: 2025/02/4 | Accepted: 2025/06/30
Received: 2025/02/4 | Accepted: 2025/06/30
References
1. Natarajan A, Beena PM, Devnikar AV, Mali S. A systemic review on tuberculosis. Indian J Tuberc. 2020;67(3):295-311. [DOI:10.1016/j.ijtb.2020.02.005] [PMID]
2. Making MA, Banhae YK, Aty MY, Abanit YM, Selasa P, Israfil I. Analisa faktor pengetahuan dan sikap dengan perilaku pencegahan tb paru pada kontak serumah selama era new normal covid 19. J Penelitian Perawat Profesional. 2023;5(1):43-50.
3. Hauser S, Longo DL, Jameson JL, Kasper DL, Loscalzo J. Harrison's Principles of Internal Medicine: 18th ed. USA: McGraw Hill; 2012.
4. Rahlwes KC, Dias BR, Campos PC, Alvarez-Arguedas S, Shiloh MU. Pathogenicity and virulence of Mycobacterium tuberculosis. Virulence. 2023;14(1):2150449. [DOI:10.1080/21505594.2022.2150449] [PMID] [PMCID]
5. Ravesloot-Chávez MM, Van Dis E, Stanley SA. The innate immune response to Mycobacterium tuberculosis infection. Annu Rev Immunol. 2021;39(1):611-37. [DOI:10.1146/annurev-immunol-093019-010426] [PMID]
6. Tenkate T, Fleming ML, FitzGerald G. Disease control and management. Professional Nursing and Midwifery Practice [Custom Edition for Monash University]. 2017:336.
7. World Health Organization (WHO). Tuberculosis preventive treatment: rapid communication. World Health Organization; 2024. Geneva, Switzerland: WHO.
8. Cao X, Fu YX, Peng H. Promising cytokine adjuvants for enhancing tuberculosis vaccine immunity. Vaccines. 2024;12(5):477. [DOI:10.3390/vaccines12050477] [PMID] [PMCID]
9. Chen L, Wei B, Di DL. A narrative review of tissue-resident memory T cells and their role in immune surveillance and COVID-19. Eur Rev Med Pharmacol Sci. 2022;26(12):4486-96.
10. Wicherska-Pawłowska K, Wróbel T, Rybka J. Toll-like receptors (TLRs), NOD-like receptors (NLRs), and RIG-I-like receptors (RLRs) in innate immunity. TLRs, NLRs, and RLRs ligands as immunotherapeutic agents for hematopoietic diseases. Int J Mol Sci. 2021;22(24):13397. [DOI:10.3390/ijms222413397] [PMID] [PMCID]
11. Ali A, Waris A, Khan MA, Asim M, Khan AU, Khan S, et al. Recent advancement, immune responses, and mechanism of action of various vaccines against intracellular bacterial infections. Life Sci. 2023;314:121332. [DOI:10.1016/j.lfs.2022.121332] [PMID]
12. Nazhofah Q, Hadi EN. Dukungan Keluarga Terhadap Kepatuhan Pengobatan Pada Pasien Tuberculosis: Literature Review. Promosi Kesehat Indones. 2022;5(6):628-32. [DOI:10.56338/mppki.v5i6.2338]
13. Maulidan M, Dedi D, Khadafi M. Dukungan Keluarga Berhubungan dengan Kepatuhan Minum Obat pada Pasien Tuberkulosis Paru. J Penelit Perawat Prof. 2021;3(3):575-84. [DOI:10.37287/jppp.v3i3.549]
14. Davenne T, McShane H. Why don't we have an effective tuberculosis vaccine yet?. Expert Rev Vaccines. 2016;15(8):1009-13. [DOI:10.1586/14760584.2016.1170599] [PMID] [PMCID]
15. Andersen P, Doherty TM. The success and failure of BCG-implications for a novel tuberculosis vaccine. Nat Rev Microbiol. 2005;3(8):656-62. [DOI:10.1038/nrmicro1211] [PMID]
16. Yun JS, Kim AR, Kim SM, Shin E, Ha SJ, Kim D, et al. In silico analysis for the development of multi-epitope vaccines against Mycobacterium tuberculosis. Front Immunol. 2024;15:1474346. [DOI:10.3389/fimmu.2024.1474346] [PMID] [PMCID]
17. Khoshnood S, Heidary M, Haeili M, Drancourt M, Darban-Sarokhalil D, Nasiri MJ, et al. Novel vaccine candidates against Mycobacterium tuberculosis. Int J Biol Macromol. 2018;120:180-8. [DOI:10.1016/j.ijbiomac.2018.08.037] [PMID]
18. Andongma BT, Huang Y, Chen F, Tang Q, Yang M, Chou SH, et al. In silico design of a promiscuous chimeric multi-epitope vaccine against Mycobacterium tuberculosis. Comput Struct Biotechnol J. 2023;21:991-1004. [DOI:10.1016/j.csbj.2023.01.019] [PMID] [PMCID]
19. Tedjokusumo LI, Goenawan YA, Wahjudi M. Desain vaksin In Silico berdasarkan Epitope Protein Mammalian Cell Entry Associated Membrane Rv1973 untuk Tuberculosis (TBC) paru-paru. Indones J Biotechnol Biodivers. 2023;7(1):22-33. [DOI:10.47007/ijobb.v7i1.168]
20. Goumari MM, Farhani I, Nezafat N, Mahmoodi S. Multi-epitope vaccines (MEVs), as a novel strategy against infectious diseases. Curr Proteomics. 2020;17(5):354-64. [DOI:10.2174/1570164617666190919120140]
21. Makatita FA. Riset in silico dalam pengembangan sains di bidang pendidikan, studi kasus: analisis potensi cendana sebagai agen anti-aging. J ABDI (Sosial, Budaya dan Sains). 2020;2(1):59-67.
22. Prieto-Martínez FD, Arciniega M, Medina-Franco JL. Molecular docking: current advances and challenges. Tip Revista Especializada En Ciencias Quimico-biologicas. 2018;1(21):1-23. [DOI:10.22201/fesz.23958723e.2018.0.143]
23. Bhatia P, Sharma V, Alam O, Manaithiya A, Alam P, Alam MT, et al. Novel quinazoline-based EGFR kinase inhibitors: A review focussing on SAR and molecular docking studies (2015-2019). Eur J Med Chem. 2020;204:112640. [DOI:10.1016/j.ejmech.2020.112640] [PMID]
24. Ahmed RM, Almofti YA, Abd-Elrahman KA. Analysis of foot and mouth disease virus polyprotein for multi peptides vaccine design: An in silico strategy. J Pure Appl Microbiol. 2022;16(3):2083-98. [DOI:10.22207/JPAM.16.3.63]
25. Depamede SN, Indarsih B, Wiryawan IK, Tamsil MH, Maskur M. Prediksi In Silico Dan Analisis Model 3d Epitop Potensial Heat Shock Protein-70 (Hsp70) Gallus Gallus Sebagai Kandidat Biomarka Uji Kualitas Daging Unggas. Pros Saintek. 2023;5:48-54.
26. Khatoon N, Pandey RK, Prajapati VK. Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach. Sci Rep. 2017;7(1):8285. [DOI:10.1038/s41598-017-08842-w] [PMID] [PMCID]
27. Marisa F, Budiarsa IM, Kundera IN. Prediksi Epitop untuk Desain Vaksin Virus Dengue secara In Silico. J Biol Sci Educ. 2020;8(1):544-52.
28. Kharisma VD, Dian FA, Burkov P, Scherbakov P, Derkho M, Sepiashvili E, et al. Molecular Simulation of B-Cell Epitope Mapping from Nipah Virus Attachment Protein to Construct Peptide-Based Vaccine Candidate: A Reverse Vaccinology Approach. Makara J Sci. 2023;27(2):106-14. [DOI:10.7454/mss.v27i2.1316]
29. Gasteiger E, Hoogland C, Gattiker A, Duvaud SE, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server. InThe proteomics protocols handbook. 2005. (pp. 571-607). Totowa, NJ, U.S.: Humana press. [DOI:10.1385/1-59259-890-0:571]
30. Toepak EP. Perancangan Vaksin 3H5S HVC Sederhana Secara In Silico: Simple In Silico 3H5S Vaccine Design. J Jejaring Matematika dan Sains. 2021;3(2):40-4. [DOI:10.36873/jjms.2022.v3.i2.605]
31. Luthfianto D, Indriputri C, AK ST, Imun M, Kp DP, Faizal IA, et al. Buku Ajar Imunologi. 2023. Pangkalpinang, Indonesia: Penerbit Science Techno Direct.
32. Akhtar N, Joshi A, Kaushik V, Kumar M, Mannan MA. In-silico design of a multivalent epitope-based vaccine against Candida auris. Microb Pathog. 2021;155:104879. [DOI:10.1016/j.micpath.2021.104879] [PMID]
33. Yazdani Z, Rafiei A, Valadan R, Ashrafi H, Pasandi M, Kardan M. Designing a potent L1 protein-based HPV peptide vaccine: A bioinformatics approach. Comput Biol Chem. 2020;85:107209. [DOI:10.1016/j.compbiolchem.2020.107209] [PMID]
34. Arturo CV, Alicia JA, Paulina ML. The impact of bioinformatics on vaccine design and development. Vaccines. 2017;2:3-6.
35. Hu YF, Hu JC, Gong HR, Danchin A, Sun R, Chu H, et al. Computation of antigenicity predicts SARS-CoV-2 vaccine breakthrough variants. Front Immunol. 2022;13:861050. [DOI:10.3389/fimmu.2022.861050] [PMID] [PMCID]
36. Ducret A, Ackaert C, Bessa J, Bunce C, Hickling T, Jawa V, et al. Assay format diversity in pre-clinical immunogenicity risk assessment: Toward a possible harmonization of antigenicity assays. InMAbs. 2022. Vol. 14, No. 1, p. 1993522. Milton, England: Taylor & Francis. [DOI:10.1080/19420862.2021.1993522] [PMID] [PMCID]
37. Ossendorp F, Ho NI, Van Montfoort N. How B cells drive T-cell responses: A key role for cross-presentation of antibody-targeted antigens. Adv Immunol. 2023;160:37-57. [DOI:10.1016/bs.ai.2023.09.002] [PMID]
38. Ahmad HI, Jabbar A, Mushtaq N, Javed Z, Hayyat MU, Bashir J, et al. Immune tolerance vs. immune resistance: the interaction between host and pathogens in infectious diseases. Front Vet Sci. 2022;9:827407. [DOI:10.3389/fvets.2022.827407] [PMID] [PMCID]
39. Chen Z, Gao X, Yu D. Longevity of vaccine protection: Immunological mechanism, assessment methods, and improving strategy. View. 2022;3(1):20200103. [DOI:10.1002/VIW.20200103]
40. Rahmahani J, Akbar DI, Suwarno S. Comparison of antigenicity between local and massachusetts strains of infectious bronchitis virus using indirect ELISA test. J Medik Veteriner. 2022;5(1):28-33. [DOI:10.20473/jmv.vol5.iss1.2022.28-33]
41. Huang H, Wang C, Rubelt F, Scriba TJ, Davis MM. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nat Biotechnol. 2020;38(10):1194-202. [DOI:10.1038/s41587-020-0505-4] [PMID] [PMCID]
42. Chowdhury MA, Hossain N, Kashem MA, Shahid MA, Alam A. Immune response in COVID-19: A review. J Infect Public Health. 2020;13(11):1619-29. [DOI:10.1016/j.jiph.2020.07.001] [PMID] [PMCID]
43. Tulaeva I, Kratzer B, Campana R, Curin M, van Hage M, Karsonova A, et al. Preventive allergen-specific vaccination against allergy: mission possible?. Front Immunol. 2020;11:1368. [DOI:10.3389/fimmu.2020.01368] [PMID] [PMCID]
44. Mueller S. Challenges and opportunities of mRNA vaccines against SARS-CoV-2. Cham, Switzerland: Springer International Publishing. 2023;10:978-3. [DOI:10.1007/978-3-031-18903-6]
45. Gülsen A, Wedi B, Jappe U. Hypersensitivity reactions to biologics (part I): allergy as an important differential diagnosis in complex immune-derived adverse events. Allergo J Int. 2020;29(4):97-125. [DOI:10.1007/s40629-020-00126-6] [PMID] [PMCID]
46. Banerji A, Wickner PG, Saff R, Stone Jr CA, Robinson LB, Long AA, et al. mRNA vaccines to prevent COVID-19 disease and reported allergic reactions: current evidence and suggested approach. J Allergy Clin Immunol Pract. 2021;9(4):1423-37. [DOI:10.1016/j.jaip.2020.12.047] [PMID] [PMCID]
47. Asmara IG. Hipersensitivitas Terhadap Vaksin. J Kedokteran. 2016;5(3):39. [DOI:10.29303/jku.v5i3.302]
48. Zahra M, Zachreini I. Hubungan Rinitis Alergi dengan Kualitas Hidup pada Guru SDN di Kecamatan Banda Sakti Kota Lhokseumawe. J Ilm Manusia Kesehatan. 2023;6(2):263-72. [DOI:10.31850/makes.v6i2.2177]
49. Johnston MS, Galan A, Watsky KL, Little AJ. Delayed localized hypersensitivity reactions to the Moderna COVID-19 vaccine: a case series. JAMA Dermatol. 2021;157(6):716-20. [DOI:10.1001/jamadermatol.2021.1214] [PMID] [PMCID]
50. Firmansyah MA, Susilo A, Haryanti SD, Herowati R. Desain Vaksin Berbasis Epitop dengan Pendekatan Bioinformatika untuk Menekan Glikoprotein Spike SARS-CoV-2. J Farm Indones. 2021;18(2):82-96. [DOI:10.31001/jfi.v18i2.1351]
51. LER I. Panduan Praktis Klinis Interna. 2021. VBOOK.PUB. Link: [https://vbook.pub/documents/panduan-praktis-klinis-internapdf-r21dmr4dd123]
52. Rezaldi F, Taupiqurrohman O, Fadillah MF, Rochmat A, Humaedi A, Fadhilah F. Identifikasi Kandidat Vaksin COVID-19 Berbasis Peptida dari Glikoprotein Spike SARS CoV-2 untuk Ras Asia secara In Silico. J Biotek Medisiana Indones. 2021;10(1):77-85.
53. Fauziah PN, Mainassy MC, Ode I, Affandi RI, Cesa FY, Umar F, et al. Imunologi. Penerbit Widina; 2023.
54. Barozet A, Bianciotto M, Vaisset M, Simeon T, Minoux H, Cortés J. Protein loops with multiple meta‐stable conformations: a challenge for sampling and scoring methods. Proteins Struct Funct Bioinf. 2021;89(2):218-31. [DOI:10.1002/prot.26008] [PMID]
55. Haysom SF. Analysing structural data to explore the function of an essential bacterial protein foldase. Biochemist. 2021;43(5):90-6. [DOI:10.1042/bio_2021_166]
56. Attwood MM, Fabbro D, Sokolov AV, Knapp S, Schiöth HB. Trends in kinase drug discovery: targets, indications and inhibitor design. Nat Rev Drug Discov. 2021;20(11):839-61. [DOI:10.1038/s41573-021-00252-y] [PMID]
57. Dehghani B, Hasanshahi Z, Hashempour T, Motamedifar M. The possible regions to design Human Papilloma Viruses vaccine in Iranian L1 protein. Biologia. 2020;75(5):749-59. [DOI:10.2478/s11756-019-00386-w] [PMID] [PMCID]
58. Nugroho MA, Rivai M. Sistem Kontrol dan Monitoring Kadar Amonia untuk Budidaya Ikan yang Diimplementasi pada Raspberry Pi 3B. J Tek ITS. 2019;7(2):A374-9. [DOI:10.12962/j23373539.v7i2.30920]
59. Rosyda M, Rahani FF. Analisis Epitope Sel T pada SARS-Cov2 dengan Pendekatan Bioinformatika. J Natl Tek Elektro Teknol Inform. 2020;9(3):233-8. [DOI:10.22146/.v9i3.408]
60. Magfirah I, Prangdimurti E, Palupi NS, Wijaya H. Penentuan struktur tiga dimensi dan profil hasil hidrolisis protein alergen arginin kinase secara in silico. Warta IHP. 2022;39(1):40-6. [DOI:10.32765/wartaihp.v39i1.7612]
61. Ahmad MM, Komari N. Pemodelan Calponin Ikan Gabus (Channa striata) dengan Phyre2 dan Interaksi dengan Protein Lain. J Nat Sci. 2022;2(1):19-31. [DOI:10.20527/jns.v2i1.4790]
62. Ebrahimi MM, Shahsavandi S, Shayan P, Goudarzi H, Masoudi S. An immunoinformatic assay to design bio adjuvanted vaccine against infectious bursal disease virus. J Biol Today's World. 2016;5:13-9. [DOI:10.15412/J.JBTW.01050102]
63. Khanam A, Hridoy HM, Alam MS, Sultana A, Hasan I. An immunoinformatics approach for a potential NY-ESO-1 and WT1 based multi-epitope vaccine designing against triple-negative breast cancer. Heliyon. 2024;10(17):e36935. [DOI:10.1016/j.heliyon.2024.e36935] [PMID] [PMCID]
64. Gong W, Pan C, Cheng P, Wang J, Zhao G, Wu X. Peptide-based vaccines for tuberculosis. Front Immunol. 2022;13:830497. [DOI:10.3389/fimmu.2022.830497] [PMID] [PMCID]
65. Waqas M, Aziz S, Liò P, Khan Y, Ali A, Iqbal A, et al. Immunoinformatics design of multivalent epitope vaccine against monkeypox virus and its variants using membrane-bound, enveloped, and extracellular proteins as targets. Front Immunol. 2023;14:1091941. [DOI:10.3389/fimmu.2023.1091941] [PMID] [PMCID]
66. Olotu FA, Soliman ME. Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus. Biomed J. 2021;44(3):317-37. [DOI:10.1016/j.bj.2020.01.002] [PMID] [PMCID]
67. Gupta N, Shah K, Singh M. An epitope‐imprinted piezoelectric diagnostic tool for Neisseria meningitidis detection. J Mol Recognit. 2016;29(12):572-9. [DOI:10.1002/jmr.2557] [PMID]
68. Hua Z, Hou B. The role of B cell antigen presentation in the initiation of CD4+ T cell response. Immunol Rev. 2020;296(1):24-35. [DOI:10.1111/imr.12859] [PMID]
69. Rastogi I, Jeon D, Moseman JE, Muralidhar A, Potluri HK, McNeel DG. Role of B cells as antigen presenting cells. Front Immunol. 2022;13:954936. [DOI:10.3389/fimmu.2022.954936] [PMID] [PMCID]
70. Lucas C, Perdriger A, Amé P. Definition of B cell helper T cells in rheumatoid arthritis and their behavior during treatment. InSeminars in Arthritis and Rheumatism. 2020. Vol. 50. No. 5. pp. 867-72. Philadelphia, PA, U.S.: W.B. Saunders Company Publisher. [DOI:10.1016/j.semarthrit.2020.06.021] [PMID]
71. Poladian N, Orujyan D, Narinyan W, Oganyan AK, Navasardyan I, Velpuri P, et al. Role of NF-κB during Mycobacterium tuberculosis Infection. Int J Mol Sci. 2023;24(2):1772. [DOI:10.3390/ijms24021772] [PMID] [PMCID]
72. Posteraro B, Pastorino R, Di Giannantonio P, Ianuale C, Amore R, Ricciardi W, et al. The link between genetic variation and variability in vaccine responses: systematic review and meta-analyses. Vaccine. 2014;32(15):1661-9. [DOI:10.1016/j.vaccine.2014.01.057] [PMID]
73. Malone B, Simovski B, Moliné C, Cheng J, Gheorghe M, Fontenelle H, et al. Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs. Sci Rep. 2020;10(1):22375. [DOI:10.1038/s41598-020-78758-5] [PMID] [PMCID]
74. Pérez CL, Larsen MV, Gustafsson R, Norström MM, Atlas A, Nixon DF, et al. Broadly immunogenic HLA class I supertype-restricted elite CTL epitopes recognized in a diverse population infected with different HIV-1 subtypes. J Immunol. 2008;180(7):5092-100. [DOI:10.4049/jimmunol.180.7.5092] [PMID]
75. De Groot AS, Moise L, Terry F, Gutierrez AH, Hindocha P, Richard G, et al. Better epitope discovery, precision immune engineering, and accelerated vaccine design using immunoinformatics tools. Front Immunol. 2020;11:442. [DOI:10.3389/fimmu.2020.00442] [PMID] [PMCID]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Copyright Policy
Iranian Journal of Medical Microbiology by Farname is licensed under CC BY-NC 4.0
