Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Multi-epitope vaccine against drug-resistant strains of Mycobacterium tuberculosis: a proteome-wide subtraction and immunoinformatics approach

  • Md Tahsin Khan (Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology) ;
  • Araf Mahmud (Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology) ;
  • Md. Muzahidul Islam (Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology) ;
  • Mst. Sayedatun Nessa Sumaia (Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology) ;
  • Zeaur Rahim (Infectious Diseases Division, International Centre for Diarrhoeal Disease Research) ;
  • Kamrul Islam (Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology) ;
  • Asif Iqbal (Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology)
  • Received : 2023.03.28
  • Accepted : 2023.08.23
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, one of the most deadly infections in humans. The emergence of multidrug-resistant and extensively drug-resistant Mtb strains presents a global challenge. Mtb has shown resistance to many frontline antibiotics, including rifampicin, kanamycin, isoniazid, and capreomycin. The only licensed vaccine, Bacille Calmette-Guerin, does not efficiently protect against adult pulmonary tuberculosis. Therefore, it is urgently necessary to develop new vaccines to prevent infections caused by these strains. We used a subtractive proteomics approach on 23 virulent Mtb strains and identified a conserved membrane protein (MmpL4, NP_214964.1) as both a potential drug target and vaccine candidate. MmpL4 is a non-homologous essential protein in the host and is involved in the pathogen-specific pathway. Furthermore, MmpL4 shows no homology with anti-targets and has limited homology to human gut microflora, potentially reducing the likelihood of adverse effects and cross-reactivity if therapeutics specific to this protein are developed. Subsequently, we constructed a highly soluble, safe, antigenic, and stable multi-subunit vaccine from the MmpL4 protein using immunoinformatics. Molecular dynamics simulations revealed the stability of the vaccine-bound Tolllike receptor-4 complex on a nanosecond scale, and immune simulations indicated strong primary and secondary immune responses in the host. Therefore, our study identifies a new target that could expedite the design of effective therapeutics, and the designed vaccine should be validated. Future directions include an extensive molecular interaction analysis, in silico cloning, wet-lab experiments, and evaluation and comparison of the designed candidate as both a DNA vaccine and protein vaccine.

Keywords

Acknowledgement

We would like to thank Md. Nahid Hasan, Ahsan Habib, and Ashikur Rahman Khan (student of genetic engineering and biotechnology, Shahjalal University of Science and Technology) for their help in the methodology section.

References

  1. World Health Organization. Global Tuberculosis Report 2020: Executive Summary. Geneva: World Health Organization, 2020.
  2. World Health Organization. Global tuberculosis report 2019. Geneva: World Health Organization, 2019.
  3. World Health Organization. Global tuberculosis report 2016. Geneva: World Health Organization, 2016.
  4. Tuberculosis: multidrug-resistant tuberculosis (MDR-TB). Geneva: World Health Organization, 2018. Accessed 2021 Aug 8. Available from: https://www.who.int/news-room/q-a-detail/tuberculosis-multidrug-resistant-tuberculosis-(mdr-tb).
  5. Nguyen QH, Contamin L, Nguyen TV, Banuls AL. Insights into the processes that drive the evolution of drug resistance in Mycobacterium tuberculosis. Evol Appl 2018;11:1498-1511. https://doi.org/10.1111/eva.12654
  6. World Health Organization. WHO treatment guidelines for drug-resistant tuberculosis. Geneva: World Health Organization, 2016.
  7. Hema K, Priyadarshini VI, Pradhan D, Munikumar M, Sandeep S, Pradeep N, et al. Identification of putative drug targets and vaccine candidates for pathogens causing atherosclerosis. Biochem Anal Biochem 2015;4:175.
  8. Almeida Da Silva PE, Palomino JC. Molecular basis and mechanisms of drug resistance in Mycobacterium tuberculosis: classical and new drugs. J Antimicrob Chemother 2011;66:1417-1430. https://doi.org/10.1093/jac/dkr173
  9. Sharma R, Rajput VS, Jamal S, Grover A, Grover S. An immunoinformatics approach to design a multi-epitope vaccine against Mycobacterium tuberculosis exploiting secreted exosome proteins. Sci Rep 2021;11:13836.
  10. Cohen KA, Manson AL, Desjardins CA, Abeel T, Earl AM. Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges. Genome Med 2019;11:45.
  11. Dookie N, Rambaran S, Padayatchi N, Mahomed S, Naidoo K. Evolution of drug resistance in Mycobacterium tuberculosis: a review on the molecular determinants of resistance and implications for personalized care. J Antimicrob Chemother 2018;73: 1138-1151. https://doi.org/10.1093/jac/dkx506
  12. Buchy P, Ascioglu S, Buisson Y, Datta S, Nissen M, Tambyah PA, et al. Impact of vaccines on antimicrobial resistance. Int J Infect Dis 2020;90:188-196. https://doi.org/10.1016/j.ijid.2019.10.005
  13. Bloom DE, Black S, Salisbury D, Rappuoli R. Antimicrobial resistance and the role of vaccines. Proc Natl Acad Sci U S A 2018;115:12868-12871. https://doi.org/10.1073/pnas.1717157115
  14. Xing Z, Charters TJ. Heterologous boost vaccines for bacillus Calmette-Guerin prime immunization against tuberculosis. Expert Rev Vaccines 2007;6:539-546. https://doi.org/10.1586/14760584.6.4.539
  15. Parida SK, Kaufmann SH. Novel tuberculosis vaccines on the horizon. Curr Opin Immunol 2010;22:374-384. https://doi.org/10.1016/j.coi.2010.04.006
  16. Horvath CN, Xing Z. Immunization strategies against pulmonary tuberculosis: considerations of T cell geography. Adv Exp Med Biol 2013;783:267-278. https://doi.org/10.1007/978-1-4614-6111-1_14
  17. Hussey G, Hawkridge T, Hanekom W. Childhood tuberculosis: old and new vaccines. Paediatr Respir Rev 2007;8:148-154. https://doi.org/10.1016/j.prrv.2007.04.009
  18. Zhang W, Zhang Y, Zheng H, Pan Y, Liu H, Du P, et al. Genome sequencing and analysis of BCG vaccine strains. PLoS One 2013;8:e71243.
  19. Singh SP, Mishra BN. Major histocompatibility complex linked databases and prediction tools for designing vaccines. Hum Immunol 2016;77:295-306. https://doi.org/10.1016/j.humimm.2015.11.012
  20. Hanekom M, Gey van Pittius NC, McEvoy C, Victor TC, Van Helden PD, Warren RM. Mycobacterium tuberculosis Beijing genotype: a template for success. Tuberculosis (Edinb) 2011;91: 510-523. https://doi.org/10.1016/j.tube.2011.07.005
  21. Mendez-Samperio P. Novel vaccination strategies and approaches against human tuberculosis. Scand J Immunol 2019;90:e12774.
  22. Marciano BE, Huang CY, Joshi G, Rezaei N, Carvalho BC, Allwood Z, et al. BCG vaccination in patients with severe combined immunodeficiency: complications, risks, and vaccination policies. J Allergy Clin Immunol 2014;133:1134-1141. https://doi.org/10.1016/j.jaci.2014.02.028
  23. Barkai G, Somech R, Stauber T, Barziali A, Greenberger S. Bacille Calmette-Guerin (BCG) complications in children with severe combined immunodeficiency (SCID). Infect Dis (Lond) 2019; 51:585-592. https://doi.org/10.1080/23744235.2019.1628354
  24. Nunes-Santos CJ, Rosenzweig SD. Bacille Calmette-Guerin complications in newly described primary immunodeficiency diseases: 2010-2017. Front Immunol 2018;9:1423.
  25. Sable SB, Posey JE, Scriba TJ. Tuberculosis vaccine development: progress in clinical evaluation. Clin Microbiol Rev 2019;33: e00100-00119. https://doi.org/10.1128/CMR.00100-19
  26. Soundarya JS, Ranganathan UD, Tripathy SP. Current trends in tuberculosis vaccine. Med J Armed Forces India 2019;75:18-24. https://doi.org/10.1016/j.mjafi.2018.12.013
  27. Martin C, Aguilo N, Marinova D, Gonzalo-Asensio JJ. Update on TB vaccine pipeline. Appl Sci 2020;10:2632.
  28. 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;8:9044.
  29. de Sarom A, Kumar Jaiswal A, Tiwari S, de Castro Oliveira L, Barh D, Azevedo V, et al. Putative vaccine candidates and drug targets identified by reverse vaccinology and subtractive genomics approaches to control Haemophilus ducreyi, the causative agent of chancroid. J R Soc Interface 2018;15:20180032.
  30. Mahmud A, Khan MT, Iqbal A. Identification of novel drug targets for humans and potential vaccine targets for cattle by subtractive genomic analysis of Brucella abortus strain 2308. Microb Pathog 2019;137:103731.
  31. Khan MT, Mahmud A, Iqbal A, Hoque SF, Hasan M. Subtractive genomics approach towards the identification of novel therapeutic targets against human Bartonella bacilliformis. Inf Med Inlocked 2020;20:100385.
  32. Khan MT, Islam MJ, Parihar A, Islam R, Jerin TJ, Dhote R, et al. Immunoinformatics and molecular modeling approach to design universal multi-epitope vaccine for SARS-CoV-2. Inform Med Unlocked 2021;24:100578.
  33. Khan MT, Islam R, Jerin TJ, Mahmud A, Khatun S, Kobir A, et al. Immunoinformatics and molecular dynamics approaches: next generation vaccine design against West Nile virus. PLoS One 2021;16:e0253393.
  34. Parvizpour S, Pourseif MM, Razmara J, Rafi MA, Omidi Y. Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. Drug Discov Today 2020;25:1034-1042. https://doi.org/10.1016/j.drudis.2020.03.006
  35. Kazi A, Chuah C, Majeed ABA, Leow CH, Lim BH, Leow CY. Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design. Pathog Glob Health 2018;112:123-131. https://doi.org/10.1080/20477724.2018.1446773
  36. Calis JJ, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD, Sette A, et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 2013;9:e1003266.
  37. Zhang L. Multi-epitope vaccines: a promising strategy against tumors and viral infections. Cell Mol Immunol 2018;15:182-184. https://doi.org/10.1038/cmi.2017.92
  38. Zhu S, Feng Y, Rao P, Xue X, Chen S, Li W, et al. Hepatitis B virus surface antigen as delivery vector can enhance Chlamydia trachomatis MOMP multi-epitope immune response in mice. Appl Microbiol Biotechnol 2014;98:4107-4117. https://doi.org/10.1007/s00253-014-5517-x
  39. Jiang P, Cai Y, Chen J, Ye X, Mao S, Zhu S, et al. Evaluation of tandem Chlamydia trachomatis MOMP multi-epitopes vaccine in BALB/c mice model. Vaccine 2017;35:3096-3103. https://doi.org/10.1016/j.vaccine.2017.04.031
  40. Pumchan A, Krobthong S, Roytrakul S, Sawatdichaikul O, Kondo H, Hirono I, et al. Novel chimeric multiepitope vaccine for streptococcosis disease in Nile Tilapia (Oreochromis niloticus Linn.). Sci Rep 2020;10:603.
  41. Caro-Gomez E, Gazi M, Goez Y, Valbuena G. Discovery of novel cross-protective Rickettsia prowazekii T-cell antigens using a combined reverse vaccinology and in vivo screening approach. Vaccine 2014;32:4968-4976. https://doi.org/10.1016/j.vaccine.2014.06.089
  42. Lin X, Chen S, Xue X, Lu L, Zhu S, Li W, et al. Chimerically fused antigen rich of overlapped epitopes from latent membrane protein 2 (LMP2) of Epstein-Barr virus as a potential vaccine and diagnostic agent. Cell Mol Immunol 2016;13:492-501. https://doi.org/10.1038/cmi.2015.29
  43. Yusufu M, Shalitanati A, Yu H, Moming A, Li Y, Deng F, et al. Immune responses in mice induced by multi-epitope DNA vaccine and protein vaccine of Crimean-Congo hemorrhagic fever virus. Preprint at: https://doi.org/10.1101/719724 (2019).
  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.
  45. Jasenosky LD, Scriba TJ, Hanekom WA, Goldfeld AE. T cells and adaptive immunity to Mycobacterium tuberculosis in humans. Immunol Rev 2015;264:74-87. https://doi.org/10.1111/imr.12274
  46. Lewinsohn DA, Lewinsohn DM, Scriba TJ. Polyfunctional CD4(+) T cells as targets for tuberculosis vaccination. Front Immunol 2017;8:1262.
  47. Maglione PJ, Xu J, Chan J. B cells moderate inflammatory progression and enhance bacterial containment upon pulmonary challenge with Mycobacterium tuberculosis. J Immunol 2007; 178:7222-7234. https://doi.org/10.4049/jimmunol.178.11.7222
  48. Kozakiewicz L, Phuah J, Flynn J, Chan J. The role of B cells and humoral immunity in Mycobacterium tuberculosis infection. Adv Exp Med Biol 2013;783:225-250. https://doi.org/10.1007/978-1-4614-6111-1_12
  49. Melly G, Purdy GE. MmpL proteins in physiology and pathogenesis of M. tuberculosis. Microorganisms 2019;7:70.
  50. Szekely R, Cole ST. Mechanistic insight into mycobacterial MmpL protein function. Mol Microbiol 2016;99:831-834. https://doi.org/10.1111/mmi.13306
  51. Wells RM, Jones CM, Xi Z, Speer A, Danilchanka O, Doornbos KS, et al. Discovery of a siderophore export system essential for virulence of Mycobacterium tuberculosis. PLoS Pathog 2013;9: e1003120.
  52. Jones CM, Niederweis M. Mycobacterium tuberculosis can utilize heme as an iron source. J Bacteriol 2011;193:1767-1770. https://doi.org/10.1128/JB.01312-10
  53. Domenech P, Reed MB, Barry CE 3rd. Contribution of the Mycobacterium tuberculosis MmpL protein family to virulence and drug resistance. Infect Immun 2005;73:3492-3501. https://doi.org/10.1128/IAI.73.6.3492-3501.2005
  54. Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 1998;393:537-544. https://doi.org/10.1038/31159
  55. Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 2010;26:680-682. https://doi.org/10.1093/bioinformatics/btq003
  56. Uddin R, Azam SS, Wadood A, Khan W, Farooq U, Khan A. Computational identification of potential drug targets against Mycobacterium leprae. Med Chem Res 2016;25:473-481. https://doi.org/10.1007/s00044-016-1501-6
  57. Gupta SK, Sarita S, Gupta MK, Pant KK, Seth PK. Definition of potential targets in Mycoplasma pneumoniae through subtractive genome analysis. J Antivir Antiretrovir 2010;2:38-41.
  58. Luo H, Lin Y, Gao F, Zhang CT, Zhang R. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res 2014;42:D574-D580. https://doi.org/10.1093/nar/gkt1131
  59. Butt AM, Tahir S, Nasrullah I, Idrees M, Lu J, Tong Y. Mycoplasma genitalium: a comparative genomics study of metabolic pathways for the identification of drug and vaccine targets. Infect Genet Evol 2012;12:53-62. https://doi.org/10.1016/j.meegid.2011.10.017
  60. Mondal SI, Ferdous S, Jewel NA, Akter A, Mahmud Z, Islam MM, et al. Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach. Adv Appl Bioinform Chem 2015;8:49-63. https://doi.org/10.2147/AABC.S88522
  61. Murali S, Jahageerdar S, Kumar S, Krishna G. Computational identification and screening of natural compounds as drug targets against the fish pathogen, Pseudomonas fluorescens. Int J Curr Microbiol App Sci 2017;6:3521-3535. https://doi.org/10.20546/ijcmas.2017.611.413
  62. Rahman A, Noore S, Hasan A, Ullah R, Rahman H, Hossain A, et al. Identification of potential drug targets by subtractive genome analysis of Bacillus anthracis A0248: an in silico approach. Comput Biol Chem 2014;52:66-72. https://doi.org/10.1016/j.compbiolchem.2014.09.005
  63. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res 2018;46:D754-D761. https://doi.org/10.1093/nar/gkx1098
  64. Hossain M, Chowdhury DU, Farhana J, Akbar MT, Chakraborty A, Islam S, et al. Identification of potential targets in Staphylococcus aureus N315 using computer aided protein data analysis. Bioinformation 2013;9:187-192. https://doi.org/10.6026/97320630009187
  65. Trivedi G, Georrge JJ. Identification of novel drug targets and its inhibitor from essential genes of human pathogenic gam positive bacteria. In: 9th National Level Science Symposium, 2016 Feb 14, Rajkot, India. pp 314-319.
  66. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30. https://doi.org/10.1093/nar/28.1.27
  67. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 2007;35:W182-W185. https://doi.org/10.1093/nar/gkm321
  68. Yu CS, Chen YC, Lu CH, Hwang JK. Prediction of protein subcellular localization. Proteins 2006;64:643-651. https://doi.org/10.1002/prot.21018
  69. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010;26:1608-1615. https://doi.org/10.1093/bioinformatics/btq249
  70. Bhasin M, Garg A, Raghava GP. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005;21: 2522-2524. https://doi.org/10.1093/bioinformatics/bti309
  71. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 2018;46:D1074-D1082. https://doi.org/10.1093/nar/gkx1037
  72. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook (Walker JM, ed.). Dordrecht: Springer, 2005. pp. 571-607.
  73. Shanmugham B, Pan A. Identification and characterization of potential therapeutic candidates in emerging human pathogen Mycobacterium abscessus: a novel hierarchical in silico approach. PLoS One 2013;8:e59126.
  74. Raman K, Yeturu K, Chandra N. targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2008;2:109.
  75. Doytchinova IA, Flower DR. Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties. Vaccine 2007;25:856-866. https://doi.org/10.1016/j.vaccine.2006.09.032
  76. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001;305:567-580. https://doi.org/10.1006/jmbi.2000.4315
  77. Nalamolu RM, Pasala C, Katari SK, Amineni U. Discovery of common putative drug targets and vaccine candidates for Mycobacterium tuberculosis sp. J Drug Deliv Ther 2019;9:67-71. https://doi.org/10.22270/jddt.v9i2-s.2603
  78. Sridhar S, Dash P, Guruprasad K. Comparative analyses of the proteins from Mycobacterium tuberculosis and human genomes: identification of potential tuberculosis drug targets. Gene 2016;579:69-74. https://doi.org/10.1016/j.gene.2015.12.054
  79. Arifuzzaman M, Maeda M, Itoh A, Nishikata K, Takita C, Saito R, et al. Large-scale identification of protein-protein interaction of Escherichia coli K-12. Genome Res 2006;16:686-691. https://doi.org/10.1101/gr.4527806
  80. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47: D607-D613. https://doi.org/10.1093/nar/gky1131
  81. Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. UniProtKB/Swiss-Prot. Methods Mol Biol 2007;406:89-112. https://doi.org/10.1007/978-1-59745-535-0_4
  82. Usmani SS, Kumar R, Bhalla S, Kumar V, Raghava GPS. In silico tools and databases for designing peptide-based vaccine and drugs. Adv Protein Chem Struct Biol 2018;112:221-263. https://doi.org/10.1016/bs.apcsb.2018.01.006
  83. Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics 2007;8:424.
  84. Wang P, Sidney J, Kim Y, Sette A, Lund O, Nielsen M, et al. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics 2010;11:568.
  85. Saha S, Raghava GP. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 2006;65:40-48. https://doi.org/10.1002/prot.21078
  86. 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:W24-W29. https://doi.org/10.1093/nar/gkx346
  87. Dimitrov I, Flower DR, Doytchinova I. AllerTOP: a server for in silico prediction of allergens. BMC Bioinformatics 2013;14 Suppl 6:S4.
  88. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Open Source Drug Discovery Consortium, et al. In silico approach for predicting toxicity of peptides and proteins. PLoS One 2013;8:e73957.
  89. Dhanda SK, Vir P, Raghava GP. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct 2013;8:30.
  90. Bui HH, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics 2006;7:153.
  91. Pandey RK, Ali M, Ojha R, Bhatt TK, Prajapati VK. Development of multi-epitope driven subunit vaccine in secretory and membrane protein of Plasmodium falciparum to convey protection against malaria infection. Vaccine 2018;36:4555-4565. https://doi.org/10.1016/j.vaccine.2018.05.082
  92. Lee SJ, Shin SJ, Lee MH, Lee MG, Kang TH, Park WS, et al. A potential protein adjuvant derived from Mycobacterium tuberculosis Rv0652 enhances dendritic cells-based tumor immunotherapy. PLoS One 2014;9:e104351.
  93. Khan MT, Mahmud A, Hasan M, Azim KF, Begum MK, Akter A, et al. Proteome exploration of Legionella pneumophila for identifying novel therapuetics: a hierarchical subtractive genomics and reverse vaccinology approach. Preprint at: https://doi.org/10.1101/ 2020.02.03.922864 (2020).
  94. Rahman MS, Hoque MN, Islam MR, Akter S, Rubayet Ul Alam A, Siddique MA, et al. Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2 etiologic agent of global pandemic COVID-19: an in silico approach. PeerJ 2020;8:e9572.
  95. Saha S, Raghava GP. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res 2006;34: W202-W209. https://doi.org/10.1093/nar/gkl343
  96. Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 2004;32: W526-W531. https://doi.org/10.1093/nar/gkh468
  97. Heo L, Park H, Seok C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res 2013;41:W384-W388. https://doi.org/10.1093/nar/gkt458
  98. 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. https://doi.org/10.1107/S0021889892009944
  99. Luthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356:83-85. https://doi.org/10.1038/356083a0
  100. 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:W407-W410. https://doi.org/10.1093/nar/gkm290
  101. Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Res 2004;32:W96-W99. https://doi.org/10.1093/nar/gkh354
  102. Krieger E, Darden T, Nabuurs SB, Finkelstein A, Vriend G. Making optimal use of empirical energy functions: force-field parameterization in crystal space. Proteins 2004;57:678-683. https://doi.org/10.1002/prot.20251
  103. Krieger E, Nielsen JE, Spronk CA, Vriend G. Fast empirical pKa prediction by Ewald summation. J Mol Graph Model 2006;25:481-486. https://doi.org/10.1016/j.jmgm.2006.02.009
  104. 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:e9862.
  105. Samad A, Meghla NS, Nain Z, Karpinski TM, Rahman MS. Immune epitopes identification and designing of a multi-epitopevaccine against bovine leukemia virus: a molecular dynamics and immune simulation approaches. Cancer Immunol Immunother 2022;71:2535-2548. https://doi.org/10.1007/s00262-022-03181-w
  106. Sarkar M, Maganti L, Ghoshal N, Dutta C. In silico quest for putative drug targets in Helicobacter pylori HPAG1: molecular modeling of candidate enzymes from lipopolysaccharide biosynthesis pathway. J Mol Model 2012;18:1855-1866. https://doi.org/10.1007/s00894-011-1204-3
  107. Hadizadeh M, Tabatabaiepour SN, Tabatabaiepour SZ, Hosseini Nave H, Mohammadi M, Sohrabi SM. Genome-wide identification of potential drug target in Enterobacteriaceae family: a homology-based method. Microb Drug Resist 2018;24:8-17. https://doi.org/10.1089/mdr.2016.0259
  108. Aguero F, Al-Lazikani B, Aslett M, Berriman M, Buckner FS, Campbell RK, et al. Genomic-scale prioritization of drug targets: the TDR Targets database. Nat Rev Drug Discov 2008;7:900-907. https://doi.org/10.1038/nrd2684
  109. Butt AM, Nasrullah I, Tahir S, Tong Y. Comparative genomics analysis of Mycobacterium ulcerans for the identification of putative essential genes and therapeutic candidates. PLoS One 2012;7:e43080.
  110. Damte D, Suh JW, Lee SJ, Yohannes SB, Hossain MA, Park SC. Putative drug and vaccine target protein identification using comparative genomic analysis of KEGG annotated metabolic pathways of Mycoplasma hyopneumoniae. Genomics 2013; 102:47-56. https://doi.org/10.1016/j.ygeno.2013.04.011
  111. Kovatcheva-Datchary P, Zoetendal EG, Venema K, de Vos WM, Smidt H. Tools for the tract: understanding the functionality of the gastrointestinal tract. Therap Adv Gastroenterol 2009;2:9-22. https://doi.org/10.1177/1756283X09337646
  112. Hooper LV, Bry L, Falk PG, Gordon JI. Host-microbial symbiosis in the mammalian intestine: exploring an internal ecosystem. Bioessays 1998;20:336-343. https://doi.org/10.1002/(SICI)1521-1878(199804)20:4<336::AID-BIES10>3.0.CO;2-3
  113. Savage DC. Gastrointestinal microflora in mammalian nutrition. Annu Rev Nutr 1986;6:155-178. https://doi.org/10.1146/annurev.nu.06.070186.001103
  114. Guarner F, Malagelada JR. Gut flora in health and disease. Lancet 2003;361:512-519. https://doi.org/10.1016/S0140-6736(03)12489-0
  115. Recanatini M, Bottegoni G, Cavalli A. In silico antitarget screening. Drug Discov Today Technol 2004;1:209-215. https://doi.org/10.1016/j.ddtec.2004.10.004
  116. Fung M, Thornton A, Mybeck K, Wu JH, Hornbuckle K, Muniz E. Evaluation of the characteristics of safety withdrawal of prescription drugs from worldwide pharmaceutical markets-1960 to 1999. Drug Inf J 2001;35:293-317. https://doi.org/10.1177/009286150103500134
  117. Fenner H. Evaluation of the efficacy and safety of NSAIDs. A new methodological approach. Scand J Rheumatol Suppl 1989;80:32-39. https://doi.org/10.3109/03009748909103710
  118. Shaw PJ, Ganey PE, Roth RA. Idiosyncratic drug-induced liver injury and the role of inflammatory stress with an emphasis on an animal model of trovafloxacin hepatotoxicity. Toxicol Sci 2010;118:7-18. https://doi.org/10.1093/toxsci/kfq168
  119. Rappuoli R. Reverse vaccinology. Curr Opin Microbiol 2000;3: 445-450. https://doi.org/10.1016/S1369-5274(00)00119-3
  120. Birhanu BT, Lee SJ, Park NH, Song JB, Park SC. In silico analysis of putative drug and vaccine targets of the metabolic pathways of Actinobacillus pleuropneumoniae using a subtractive/comparative genomics approach. J Vet Sci 2018;19:188-199. https://doi.org/10.4142/jvs.2018.19.2.188
  121. Uddin R, Siddiqui QN, Azam SS, Saima B, Wadood A. Identification and characterization of potential druggable targets among hypothetical proteins of extensively drug resistant Mycobacterium tuberculosis (XDR KZN 605) through subtractive genomics approach. Eur J Pharm Sci 2018;114:13-23. https://doi.org/10.1016/j.ejps.2017.11.014
  122. Uddin R, Zahra NU, Azam SS. Identification of glucosyl-3-phosphoglycerate phosphatase as a novel drug target against resistant strain of Mycobacterium tuberculosis (XDR1219) by using comparative metabolic pathway approach. Comput Biol Chem 2019;79:91-102. https://doi.org/10.1016/j.compbiolchem.2019.01.011
  123. Dar HA, Zaheer T, Ullah N, Bakhtiar SM, Zhang T, Yasir M, et al. Pangenome analysis of Mycobacterium tuberculosis reveals core-drug targets and screening of promising lead compounds for drug discovery. Antibiotics (Basel) 2020;9:819.
  124. Gupta D, Banerjee S, Pailan S, Saha P. In silico identification and characterization of a hypothetical protein of Mycobacterium tuberculosis EAI5 as a potential virulent factor. Bioinformation 2016;12:182-191. https://doi.org/10.6026/97320630012182
  125. Nain Z, Karim MM, Sen MK, Adhikari UK. Structural basis and designing of peptide vaccine using PE-PGRS family protein of Mycobacterium ulcerans: an integrated vaccinomics approach. Mol Immunol 2020;120:146-163. https://doi.org/10.1016/j.molimm.2020.02.009
  126. Li N, Liu P, Wang L, Liu J, Yuan X, Meng W, et al. Effect of Ipr1 on expression levels of immune genes related to macrophage anti-infection of Mycobacterium tuberculosis. Int J Clin Exp Med 2015;8:3411-3419.
  127. Choi HG, Kim WS, Back YW, Kim H, Kwon KW, Kim JS, et al. Mycobacterium tuberculosis RpfE promotes simultaneous Th1- and Th17-type T-cell immunity via TLR4-dependent maturation of dendritic cells. Eur J Immunol 2015;45:1957-1971. https://doi.org/10.1002/eji.201445329
  128. Pivarcsi A, Bodai L, Rethi B, Kenderessy-Szabo A, Koreck A, Szell M, et al. Expression and function of Toll-like receptors 2 and 4 in human keratinocytes. Int Immunol 2003;15:721-730. https://doi.org/10.1093/intimm/dxg068
  129. Abel B, Thieblemont N, Quesniaux VJ, Brown N, Mpagi J, Miyake K, et al. Toll-like receptor 4 expression is required to control chronic Mycobacterium tuberculosis infection in mice. J Immunol 2002;169:3155-3162. https://doi.org/10.4049/jimmunol.169.6.3155
  130. Zare-Bidaki M, Hakimi H, Abdollahi SH, Zainodini N, Arababadi MK, Kennedy D. TLR4 in toxoplasmosis; friends or foe? Microb Pathog 2014;69-70:28-32. https://doi.org/10.1016/j.micpath.2014.03.006
  131. Afsharimoghaddam A, Soleimani M, Lashay A, Dehghani M, Sepehri Z. Controversial roles played by toll like receptor 4 in urinary bladder cancer: a systematic review. Life Sci 2016;158:31-36. https://doi.org/10.1016/j.lfs.2016.06.013
  132. Sepehri Z, Kiani Z, Kohan F, Ghavami S. Toll-like receptor 4 as an immune receptor against Mycobacterium tuberculosis: a systematic review. Lab Med 2019;50:117-129. https://doi.org/10.1093/labmed/lmy047
  133. Islam MJ, Parves MR, Mahmud S, Tithi FA, Reza MA. Assessment of structurally and functionally high-risk nsSNPs impacts on human bone morphogenetic protein receptor type IA (BMPR1A) by computational approach. Comput Biol Chem 2019;80:31-45. https://doi.org/10.1016/j.compbiolchem.2019.03.004
  134. Mahmud S, Parves MR, Riza YM, Sujon KM, Ray S, Tithi FA, et al. Exploring the potent inhibitors and binding modes of phospholipase A2 through in silico investigation. J Biomol Struct Dyn 2020;38:4221-4231. https://doi.org/10.1080/07391102.2019.1680440
  135. Jeffrey GA. An introduction to hydrogen bonding. New York: Oxford university Press, 1997.
  136. Hendsch ZS, Tidor B. Do salt bridges stabilize proteins? A continuum electrostatic analysis. Protein Sci 1994;3:211-226. https://doi.org/10.1002/pro.5560030206
  137. Bosshard HR, Marti DN, Jelesarov I. Protein stabilization by salt bridges: concepts, experimental approaches and clarification of some misunderstandings. J Mol Recognit 2004;17:1-16. https://doi.org/10.1002/jmr.657
  138. Makhatadze GI, Loladze VV, Ermolenko DN, Chen X, Thomas ST. Contribution of surface salt bridges to protein stability: guidelines for protein engineering. J Mol Biol 2003;327:1135-1148. https://doi.org/10.1016/S0022-2836(03)00233-X
  139. Kennedy DA, Read AF. Why the evolution of vaccine resistance is less of a concern than the evolution of drug resistance. Proc Natl Acad Sci U S A 2018;115:12878-12886. https://doi.org/10.1073/pnas.1717159115
  140. Relman DA, Lipsitch M. Microbiome as a tool and a target in the effort to address antimicrobial resistance. Proc Natl Acad Sci U S A 2018;115:12902-12910. https://doi.org/10.1073/pnas.1717163115
  141. Plotkin S, Robinson JM, Cunningham G, Iqbal R, Larsen S. The complexity and cost of vaccine manufacturing: an overview. Vaccine 2017;35:4064-4071. https://doi.org/10.1016/j.vaccine.2017.06.003
  142. Nguyen Thi LT, Sarmiento ME, Calero R, Camacho F, Reyes F, Hossain MM, et al. Immunoinformatics study on highly expressed Mycobacterium tuberculosis genes during infection. Tuberculosis (Edinb) 2014;94:475-481. https://doi.org/10.1016/j.tube.2014.06.004
  143. Rahmat Ullah S, Majid M, Rashid MI, Mehmood K, Andleeb S. Immunoinformatics driven prediction of multiepitopic vaccine against Klebsiella pneumoniae and Mycobacterium tuberculosis coinfection and its validation via in silico expression. Int J Pept Res Ther 2021;27:987-999. https://doi.org/10.1007/s10989-020-10144-1
  144. Prezzemolo T, Guggino G, La Manna MP, Di Liberto D, Dieli F, Caccamo N. Functional signatures of human CD4 and CD8 T cell responses to Mycobacterium tuberculosis. Front Immunol 2014;5:180.
  145. Lin PL, Flynn JL. CD8 T cells and Mycobacterium tuberculosis infection. Semin Immunopathol 2015;37:239-249. https://doi.org/10.1007/s00281-015-0490-8
  146. Stenger S, Hanson DA, Teitelbaum R, Dewan P, Niazi KR, Froelich CJ, et al. An antimicrobial activity of cytolytic T cells mediated by granulysin. Science 1998;282:121-125. https://doi.org/10.1126/science.282.5386.121
  147. Serbina NV, Liu CC, Scanga CA, Flynn JL. CD8+ CTL from lungs of Mycobacterium tuberculosis-infected mice express perforin in vivo and lyse infected macrophages. J Immunol 2000;165: 353-363. https://doi.org/10.4049/jimmunol.165.1.353
  148. Cho S, Mehra V, Thoma-Uszynski S, Stenger S, Serbina N, Mazzaccaro RJ, et al. Antimicrobial activity of MHC class I-restricted CD8+ T cells in human tuberculosis. Proc Natl Acad Sci U S A 2000;97:12210-12215. https://doi.org/10.1073/pnas.210391497
  149. Flynn JL, Chan J. Immunology of tuberculosis. Annu Rev Immunol 2001;19:93-129. https://doi.org/10.1146/annurev.immunol.19.1.93
  150. North RJ. Importance of thymus-derived lymphocytes in cell-mediated immunity to infection. Cell Immunol 1973;7:166-176. https://doi.org/10.1016/0008-8749(73)90193-7
  151. Lefford MJ. Transfer of adoptive immunity to tuberculosis in mice. Infect Immun 1975;11:1174-1181. https://doi.org/10.1128/iai.11.6.1174-1181.1975
  152. Khan TA, Mazhar H, Saleha S, Tipu HN, Muhammad N, Abbas MN. Interferon-gamma improves macrophages function against M. tuberculosis in multidrug-resistant tuberculosis patients. Chemother Res Pract 2016;2016:7295390.
  153. Geginat J, Sallusto F, Lanzavecchia A. Cytokine-driven proliferation and differentiation of human naive, central memory and effector memory CD4+ T cells. Pathol Biol (Paris) 2003;51:64-66. https://doi.org/10.1016/S0369-8114(03)00098-1
  154. Walzl G, Ronacher K, Hanekom W, Scriba TJ, Zumla A. Immunological biomarkers of tuberculosis. Nat Rev Immunol 2011;11:343-354. https://doi.org/10.1038/nri2960
  155. Rozot V, Vigano S, Mazza-Stalder J, Idrizi E, Day CL, Perreau M, et al. Mycobacterium tuberculosis-specific CD8+ T cells are functionally and phenotypically different between latent infection and active disease. Eur J Immunol 2013;43:1568-1577. https://doi.org/10.1002/eji.201243262
  156. Brighenti S, Andersson J. Local immune responses in human tuberculosis: learning from the site of infection. J Infect Dis 2012;205 Suppl 2:S316-324. https://doi.org/10.1093/infdis/jis043
  157. Flynn JL, Chan J, Triebold KJ, Dalton DK, Stewart TA, Bloom BR. An essential role for interferon gamma in resistance to Mycobacterium tuberculosis infection. J Exp Med 1993;178:2249-2254. https://doi.org/10.1084/jem.178.6.2249
  158. Nandi B, Behar SM. Regulation of neutrophils by interferon-gamma limits lung inflammation during tuberculosis infection. J Exp Med 2011;208:2251-2262. https://doi.org/10.1084/jem.20110919
  159. Chan J, Tanaka K, Carroll D, Flynn J, Bloom BR. Effects of nitric oxide synthase inhibitors on murine infection with Mycobacterium tuberculosis. Infect Immun 1995;63:736-740. https://doi.org/10.1128/iai.63.2.736-740.1995
  160. Dalton DK, Pitts-Meek S, Keshav S, Figari IS, Bradley A, Stewart TA. Multiple defects of immune cell function in mice with disrupted interferon-gamma genes. Science 1993;259:1739-1742. https://doi.org/10.1126/science.8456300
  161. Rao M, Valentini D, Poiret T, Dodoo E, Parida S, Zumla A, et al. B in TB: B cells as mediators of clinically relevant immune responses in tuberculosis. Clin Infect Dis 2015;61Suppl 3:S225-S234. https://doi.org/10.1093/cid/civ614
  162. Kringelum JV, Nielsen M, Padkjaer SB, Lund O. Structural analysis of B-cell epitopes in antibody:protein complexes. Mol Immunol 2013;53:24-34. https://doi.org/10.1016/j.molimm.2012.06.001
  163. Porada CD, Almeida-Porada G. Mesenchymal stem cells as therapeutics and vehicles for gene and drug delivery. Adv Drug Deliv Rev 2010;62:1156-1166. https://doi.org/10.1016/j.addr.2010.08.010
  164. Li X, Yang X, Jiang Y, Liu J. A novel HBV DNA vaccine based on T cell epitopes and its potential therapeutic effect in HBV transgenic mice. Int Immunol 2005;17:1293-1302. https://doi.org/10.1093/intimm/dxh305
  165. Li H, Ning P, Lin Z, Liang W, Kang K, He L, et al. Co-expression of the C-terminal domain of Yersinia enterocolitica invasin enhances the efficacy of classical swine-fever-vectored vaccine based on human adenovirus. J Biosci 2015;40:79-90. https://doi.org/10.1007/s12038-014-9495-z
  166. Xiang K, Ying G, Yan Z, Shanshan Y, Lei Z, Hongjun L, et al. Progress on adenovirus-vectored universal influenza vaccines. Hum Vaccin Immunother 2015;11:1209-1222. https://doi.org/10.1080/21645515.2015.1016674
  167. Kallel H, Kamen AA. Large-scale adenovirus and poxvirus-vectored vaccine manufacturing to enable clinical trials. Biotechnol J 2015;10:741-747. https://doi.org/10.1002/biot.201400390
  168. Farnos O, Gelaye E, Trabelsi K, Bernier A, Subramani K, Kallel H, et al. Establishing a Robust manufacturing platform for recombinant veterinary vaccines: an adenovirus-vector vaccine to control newcastle disease virus infections of poultry in sub-Saharan Africa.Vaccines (Basel) 2020;8:338.
  169. Cai X, Bai H, Zhang X. Vaccines and advanced vaccines: a landscape for advanced vaccine technology against infectious disease, COVID-19 and tumor. Preprint at: https://doi.org/10.31219/osf.io/ypgx4 (2020).
  170. Milligan ID, Gibani MM, Sewell R, Clutterbuck EA, Campbell D, Plested E, et al. Safety and immunogenicity of novel adenovirus type 26- and modified vaccinia Ankara-vectored Ebola vaccines: a randomized clinical trial. JAMA 2016;315:1610-1623. https://doi.org/10.1001/jama.2016.4218
  171. Zimmer C, Corum J, Wee SL, Kristoggersen M. Coronavirus Vaccine Tracker. New York: The New York Times, 2022. Accessed 2023 Mar 28. Available from: https://www.nytimes. com/interactive/2020/science/coronavirus-vaccine-tracker.html.