دورية أكاديمية
أعرض في EDS

Immunoinformatics and structural aided approach to develop multi-epitope based subunit vaccine against Mycobacterium tuberculosis.

التفاصيل البيبلوغرافية
العنوان: Immunoinformatics and structural aided approach to develop multi-epitope based subunit vaccine against Mycobacterium tuberculosis.
المؤلفون: Sethi G; Department of Predictive Toxicology, Korea Institute of Toxicology (KIT), Daejeon, Republic of Korea.; Animal Model Research Group, Korea Institute of Toxicology, 30 Baehak 1-gil, Jeonguep, Jeollabuk-do, 56212, Republic of Korea., Varghese RP; Department of Bioinformatics, Pondicherry University, Puducherry, 605014, India., Lakra AK; Translational Health Science and Technology Institute, Faridabad, Haryana, 121001, India., Nayak SS; Department of Bioinformatics, Pondicherry University, Puducherry, 605014, India., Krishna R; Department of Bioinformatics, Pondicherry University, Puducherry, 605014, India. ramadaskr@gmail.com., Hwang JH; Animal Model Research Group, Korea Institute of Toxicology, 30 Baehak 1-gil, Jeonguep, Jeollabuk-do, 56212, Republic of Korea. jeongho.hwang@kitox.re.kr.
المصدر: Scientific reports [Sci Rep] 2024 Jul 10; Vol. 14 (1), pp. 15923. Date of Electronic Publication: 2024 Jul 10.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Epitopes, T-Lymphocyte*/immunology , Immunoinformatics*/methods , Mycobacterium tuberculosis*/immunology , Tuberculosis Vaccines*/immunology , Vaccines, Subunit*/immunology, Humans ; Antigens, Bacterial/immunology ; Epitopes, B-Lymphocyte/immunology ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Toll-Like Receptor 2/immunology ; Tuberculosis/prevention & control ; Tuberculosis/immunology
مستخلص: Tuberculosis is a highly contagious disease caused by Mycobacterium tuberculosis (Mtb), which is one of the prominent reasons for the death of millions worldwide. The bacterium has a substantially higher mortality rate than other bacterial diseases, and the rapid rise of drug-resistant strains only makes the situation more concerning. Currently, the only licensed vaccine BCG (Bacillus Calmette-Guérin) is ineffective in preventing adult pulmonary tuberculosis prophylaxis and latent tuberculosis re-activation. Therefore, there is a pressing need to find novel and safe vaccines that provide robust immune defense and have various applications. Vaccines that combine epitopes from multiple candidate proteins have been shown to boost immunity against Mtb infection. This study applies an immunoinformatic strategy to generate an adequate multi-epitope immunization against Mtb employing five antigenic proteins. Potential B-cell, cytotoxic T lymphocyte, and helper T lymphocyte epitopes were speculated from the intended proteins and coupled with 50 s ribosomal L7/L12 adjuvant, and the vaccine was constructed. The vaccine's physicochemical profile demonstrates antigenic, soluble, and non-allergic. In the meantime, docking, molecular dynamics simulations, and essential dynamics analysis revealed that the multi-epitope vaccine structure interacted strongly with Toll-like receptors (TLR2 and TLR3). MM-PBSA analysis was performed to ascertain the system's intermolecular binding free energies accurately. The immune simulation was applied to the vaccine to forecast its immunogenic profile. Finally, in silico cloning was used to validate the vaccine's efficacy. The immunoinformatics analysis suggests the multi-epitope vaccine could induce specific immune responses, making it a potential candidate against Mtb. However, validation through the in-vivo study of the developed vaccine is essential to assess its efficacy and immunogenicity profile, which will assure active protection against Mtb.
(© 2024. The Author(s).)
References: Kyu, H. H. et al. Global, regional, and national burden of tuberculosis, 1990–2016: results from the Global Burden of Diseases, Injuries, and Risk Factors 2016 study. Lancet. Infect. Dis 18, 1329–1349 (2018). (PMID: 10.1016/S1473-3099(18)30625-X)
Sandhu, G. K. Tuberculosis: Current situation, challenges and overview of its control programs in India. J. Glob. Infect. Dis. 3, 143–150 (2011). (PMID: 21731301312502710.4103/0974-777X.81691)
Bagcchi, S. WHO’s global tuberculosis report 2022. Lancet Microbe 4, e20 (2023). (PMID: 3652151210.1016/S2666-5247(22)00359-7)
Annual Reports:: Central TB Division. https://tbcindia.gov.in/index1.php?lang=1&level=1&sublinkid=4160&lid=2807 .
Jang, J. G. & Chung, J. H. Diagnosis and treatment of multidrug-resistant tuberculosis. Yeungnam Univ. J. Med. 37, 277–285 (2020). (PMID: 32883054760695610.12701/yujm.2020.00626)
Sharma, R., Rajput, V. S., Jamal, S., Grover, A. & Grover, S. An immunoinformatics approach to design a multi-epitope vaccine against Mycobacterium tuberculosis exploiting secreted exosome proteins. Sci. Rep. 11, 13836 (2021). (PMID: 34226593825778610.1038/s41598-021-93266-w)
Watt, J. & Liu, J. Preclinical progress of subunit and live attenuated Mycobacterium tuberculosis vaccines: A review following the first in human efficacy trial. Pharmaceutics 12, 848 (2020). (PMID: 32899930755942110.3390/pharmaceutics12090848)
Kaufmann, S. H. Novel tuberculosis vaccination strategies based on understanding the immune response. J. Intern. Med. 267(4), 337. https://doi.org/10.1111/j.1365-2796.2010.02216.x (2010). (PMID: 10.1111/j.1365-2796.2010.02216.x20433580)
Nieuwenhuizen, N. E. & Kaufmann, S. H. E. Next-generation vaccines based on Bacille Calmette-Guérin. Front. Immunol. 9, 121 (2018). (PMID: 29459859580759310.3389/fimmu.2018.00121)
Evans, T. G., Schrager, L. & Thole, J. Status of vaccine research and development of vaccines for tuberculosis. Vaccine 34, 2911–2914 (2016). (PMID: 2697307310.1016/j.vaccine.2016.02.079)
Wilkie, M. E. M. & McShane, H. TB vaccine development: Where are we and why is it so difficult?. Thorax 70, 299–301 (2015). (PMID: 2543294310.1136/thoraxjnl-2014-205202)
Méndez-Samperio, P. Global efforts in the development of vaccines for tuberculosis: Requirements for improved vaccines against Mycobacterium tuberculosis. Scand. J. Immunol. 84, 204–210 (2016). (PMID: 2745433510.1111/sji.12465)
Bibi, S. et al. In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology. Sci. Rep. 11, 1249 (2021). (PMID: 33441913780704010.1038/s41598-020-80899-6)
Gillard, P. et al. Safety and immunogenicity of the M72/AS01E candidate tuberculosis vaccine in adults with tuberculosis: A phase II randomised study. Tuberculosis 100, 118–127 (2016). (PMID: 2755341910.1016/j.tube.2016.07.005)
Kagina, B. M. N. et al. The novel tuberculosis vaccine, AERAS-402, is safe in healthy infants previously vaccinated with BCG, and induces dose-dependent CD4 and CD8T cell responses. Vaccine 32, 5908–5917 (2014). (PMID: 2521819410.1016/j.vaccine.2014.09.001)
Suliman, S. et al. Dose optimization of H56:IC31 vaccine for tuberculosis-endemic populations. A double-blind, placebo-controlled, dose-selection trial. Am. J. Respir. Crit. Care Med 199, 220–231 (2019). (PMID: 3009214310.1164/rccm.201802-0366OC)
Penn-Nicholson, A. et al. Safety and immunogenicity of the novel tuberculosis vaccine ID93 + GLA-SE in BCG-vaccinated healthy adults in South Africa: A randomised, double-blind, placebo-controlled phase 1 trial. Lancet Respir. Med. 6, 287–298 (2018). (PMID: 2959551010.1016/S2213-2600(18)30077-8)
Chatterjee, N., Ojha, R., Khatoon, N. & Prajapati, V. K. Scrutinizing Mycobacterium tuberculosis membrane and secretory proteins to formulate multiepitope subunit vaccine against pulmonary tuberculosis by utilizing immunoinformatic approaches. Int. J. Biol. Macromol. 118, 180–188 (2018). (PMID: 2992036910.1016/j.ijbiomac.2018.06.080)
Andongma, B. T. et al. In silico design of a promiscuous chimeric multi-epitope vaccine against Mycobacterium tuberculosis. Comput. Struct. Biotechnol. J. 21, 991–1004 (2023). (PMID: 36733703988314810.1016/j.csbj.2023.01.019)
Jiang, F. et al. Design and development of a multi-epitope vaccine for the prevention of latent tuberculosis infection. Med. Adv. 1, 361–382 (2023). (PMID: 10.1002/med4.40)
Kang, S., Kim, D., Jin, C., Ahn, H. & Lee, B. The crystal structure of AcrR from Mycobacterium tuberculosis reveals a one-component transcriptional regulation mechanism. FEBS Open Bio 9, 1713–1725 (2019). (PMID: 31369208676810610.1002/2211-5463.12710)
Pal, R., Bisht, M. K. & Mukhopadhyay, S. Secretory proteins of Mycobacterium tuberculosis and their roles in modulation of host immune responses: Focus on therapeutic targets. FEBS J. 289, 4146–4171 (2022). (PMID: 3507346410.1111/febs.16369)
Choudhary, R. K. et al. PPE antigen Rv2430c of Mycobacterium tuberculosis induces a strong B-cell response. Infect. Immun. 71, 6338–6343 (2003). (PMID: 1457365321956310.1128/IAI.71.11.6338-6343.2003)
Chen, W. et al. Mycobacterium tuberculosis PE25/PPE41 protein complex induces activation and maturation of dendritic cells and drives Th2-biased immune responses. Med. Microbiol. Immunol. 205, 119–131 (2016). (PMID: 2631885610.1007/s00430-015-0434-x)
Assis, P. A. et al. Mycobacterium tuberculosis expressing phospholipase C subverts PGE2 synthesis and induces necrosis in alveolar macrophages. BMC Microbiol. 14, 128 (2014). (PMID: 24886263405791710.1186/1471-2180-14-128)
Bakala N’Goma, J. C., Schué, M., Carrière, F., Geerlof, A. & Canaan, S. Evidence for the cytotoxic effects of Mycobacterium tuberculosis phospholipase C towards macrophages. Biochim. Biophys. Acta BBA Mol. Cell Biol. Lipids 1801, 1305–1313 (2010).
Wang, X. et al. Identification and evaluation of the novel immunodominant antigen Rv2351c from Mycobacterium tuberculosis. Emerg. Microbes Infect. 6, e48 (2017). (PMID: 28588287552031110.1038/emi.2017.34)
Shahbaaz, M., Potemkin, V., Bisetty, K., Hassan, Md. I. & Hussien, M. A. Classification and functional analyses of putative virulence factors of Mycobacterium tuberculosis: A combined sequence and structure based study. Comput. Biol. Chem. 87, 107270 (2020). (PMID: 3243811610.1016/j.compbiolchem.2020.107270)
Usmani, S. S., Kumar, R., Bhalla, S., Kumar, V. & Raghava, G. P. S. Chapter Seven—In Silico tools and databases for designing peptide-based vaccine and drugs. In Advances in Protein Chemistry and Structural Biology Vol. 112 (ed. Donev, R.) 221–263 (Academic Press, 2018).
The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021). (PMID: 10.1093/nar/gkaa1100)
Kolaskar, A. S. & Tongaonkar, P. C. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 276, 172–174 (1990). (PMID: 170239310.1016/0014-5793(90)80535-Q)
Dimitrov, I., Naneva, L., Doytchinova, I. & Bangov, I. AllergenFP: Allergenicity prediction by descriptor fingerprints. Bioinformatics 30, 846–851 (2014). (PMID: 2416715610.1093/bioinformatics/btt619)
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990). (PMID: 223171210.1016/S0022-2836(05)80360-2)
Saha, S. & Raghava, G. P. S. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins Struct. Funct. Bioinform. 65, 40–48 (2006). (PMID: 10.1002/prot.21078)
Saha, S., Bhasin, M. & Raghava, G. P. Bcipep: A database of B-cell epitopes. BMC Genom. 6, 79 (2005). (PMID: 10.1186/1471-2164-6-79)
Wang, P. et al. A systematic assessment of MHC Class II peptide binding predictions and evaluation of a consensus approach. PLOS Comput. Biol. 4, e1000048 (2008). (PMID: 18389056226722110.1371/journal.pcbi.1000048)
Wang, P. et al. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinform. 11, 568 (2010). (PMID: 10.1186/1471-2105-11-568)
Larsen, M. V. et al. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinform. 8, 424 (2007). (PMID: 10.1186/1471-2105-8-424)
Lim, W. C. & Khan, A. M. Mapping HLA-A2, -A3 and -B7 supertype-restricted T-cell epitopes in the ebolavirus proteome. BMC Genom. 19, 42 (2018). (PMID: 10.1186/s12864-017-4328-8)
Sette, A. & Sidney, J. Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism. Immunogenetics 50, 201–212 (1999). (PMID: 1060288010.1007/s002510050594)
Dhanda, S. K., Vir, P. & Raghava, G. P. S. Designing of interferon-gamma inducing MHC class-II binders. Biol. Direct 8, 30 (2013). (PMID: 24304645423504910.1186/1745-6150-8-30)
Gupta, S. et al. In silico approach for predicting toxicity of peptides and proteins. PLoS ONE 8, e73957 (2013). (PMID: 24058508377279810.1371/journal.pone.0073957)
Larijani, A., Kia-Karimi, A. & Roostaei, D. Design of a multi-epitopic vaccine against Epstein–Barr virus via computer-based methods. Front. Immunol. 14, 1115345 (2023). (PMID: 369990151004318110.3389/fimmu.2023.1115345)
Gasteiger, E. et al. ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31, 3784–3788 (2003). (PMID: 1282441816897010.1093/nar/gkg563)
Doytchinova, I. A. & Flower, D. R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8, 4 (2007). (PMID: 10.1186/1471-2105-8-4)
Magnan, C. N. et al. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 26, 2936–2943 (2010). (PMID: 20934990298215110.1093/bioinformatics/btq551)
Magnan, C. N., Randall, A. & Baldi, P. SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics 25, 2200–2207 (2009). (PMID: 1954963210.1093/bioinformatics/btp386)
Dimitrov, I., Bangov, I., Flower, D. R. & Doytchinova, I. AllerTOP vol 2—A server for in silico prediction of allergens. J. Mol. Model 20, 2278 (2014). (PMID: 2487880310.1007/s00894-014-2278-5)
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). (PMID: 34265844837160510.1038/s41586-021-03819-2)
McGuffin, L. J., Bryson, K. & Jones, D. T. The PSIPRED protein structure prediction server. Bioinformatics 16, 404–405 (2000). (PMID: 1086904110.1093/bioinformatics/16.4.404)
Kouza, M., Faraggi, E., Kolinski, A. & Kloczkowski, A. The GOR method of protein secondary structure prediction and its application as a protein aggregation prediction tool. In Prediction of Protein Secondary Structure (eds Zhou, Y. et al.) 7–24 (Springer, 2017). https://doi.org/10.1007/978-1-4939-6406-2_2 . (PMID: 10.1007/978-1-4939-6406-2_2)
Källberg, M. et al. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 7, 1511–1522 (2012). (PMID: 22814390473038810.1038/nprot.2012.085)
Heo, L., Park, H. & Seok, C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 41, W384–W388 (2013). (PMID: 23737448369208610.1093/nar/gkt458)
Lovell, S. C. et al. Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins Struct. Funct. Bioinform. 50, 437–450 (2003). (PMID: 10.1002/prot.10286)
Wiederstein, M. & Sippl, M. J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35, W407–W410 (2007). (PMID: 17517781193324110.1093/nar/gkm290)
Pettersen, E. F. et al. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004). (PMID: 1526425410.1002/jcc.20084)
Huang, S.-Y. & Zou, X. Advances and challenges in protein-ligand docking. Int. J. Mol. Sci. 11, 3016–3034 (2010). (PMID: 21152288299674810.3390/ijms11083016)
Kozakov, D. et al. The ClusPro web server for protein–protein docking. Nat. Protoc. 12, 255–278 (2017). (PMID: 28079879554022910.1038/nprot.2016.169)
Pettersen, E. F. et al. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 25(13), 1605. https://doi.org/10.1002/jcc.20084 (2004). (PMID: 10.1002/jcc.2008415264254)
Laskowski, R. A., Jabłońska, J., Pravda, L., Vařeková, R. S. & Thornton, J. M. PDBsum: Structural summaries of PDB entries. Protein Sci. 27, 129–134 (2018). (PMID: 2887554310.1002/pro.3289)
Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015). (PMID: 10.1016/j.softx.2015.06.001)
Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984). (PMID: 10.1063/1.448118)
Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997). (PMID: 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H)
Miyamoto, S. & Kollman, P. A. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 13, 952–962 (1992). (PMID: 10.1002/jcc.540130805)
Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981). (PMID: 10.1063/1.328693)
Sahoo, S. et al. Impact of nsSNPs in human AIM2 and IFI16 gene: A comprehensive in silico analysis. J. Biomol. Struct. Dyn. https://doi.org/10.1080/07391102.2023.2206907 (2023). (PMID: 10.1080/07391102.2023.220690738093709)
Sethi, G., Hwang, J. H. & Krishna, R. Structure based exploration of potential lead molecules against the extracellular cysteine protease (EcpA) of Staphylococcus epidermidis: A therapeutic halt. J. Biomol. Struct. Dyn. https://doi.org/10.1080/07391102.2023.2250455 (2023). (PMID: 10.1080/07391102.2023.225045537880982)
Swain, S. S. et al. Molecular docking and simulation study for synthesis of alternative dapsone derivative as a newer antileprosy drug in multidrug therapy. J. Cell. Biochem. 119, 9838–9852 (2018). (PMID: 3012597310.1002/jcb.27304)
Bui, H.-H. et al. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinform. 7, 153 (2006). (PMID: 10.1186/1471-2105-7-153)
Carbone, A., Zinovyev, A. & Képès, F. Codon adaptation index as a measure of dominating codon bias. Bioinformatics 19, 2005–2015 (2003). (PMID: 1459470410.1093/bioinformatics/btg272)
Grote, A. et al. JCat: A novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 33, W526–W531 (2005). (PMID: 15980527116013710.1093/nar/gki376)
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 5, e9862 (2010). (PMID: 20419125285570110.1371/journal.pone.0009862)
Cyster, J. G. & Allen, C. D. C. B cell responses—Cell interaction dynamics and decisions. Cell 177, 524–540 (2019). (PMID: 31002794653827910.1016/j.cell.2019.03.016)
Ojha, R., Pandey, R. K. & Prajapati, V. K. Vaccinomics strategy to concoct a promising subunit vaccine for visceral leishmaniasis targeting sandfly and leishmania antigens. Int. J. Biol. Macromol. 156, 548–557 (2020). (PMID: 3231140010.1016/j.ijbiomac.2020.04.097)
Pollard, A. J. & Bijker, E. M. A guide to vaccinology: From basic principles to new developments. Nat. Rev. Immunol. 21, 83–100 (2021). (PMID: 3335398710.1038/s41577-020-00479-7)
Gong, W. et al. Peptide-based vaccines for tuberculosis. Front. Immunol. 13, 830497 (2022). (PMID: 35173740884175310.3389/fimmu.2022.830497)
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. 22, 13397 (2021). (PMID: 34948194870465610.3390/ijms222413397)
Gong, W., Liang, Y. & Wu, X. The current status, challenges, and future developments of new tuberculosis vaccines. Hum. Vaccins Immunother. 14, 1697–1716 (2018). (PMID: 10.1080/21645515.2018.1458806)
Gong, W. et al. Peptides-based vaccine MP3RT induced protective immunity against Mycobacterium tuberculosis infection in a humanized mouse model. Front. Immunol. 12, 666290 (2021). (PMID: 33981313810869810.3389/fimmu.2021.666290)
Al-Hawash, A. B., Zhang, X. & Ma, F. Strategies of codon optimization for high-level heterologous protein expression in microbial expression systems. Gene Rep. 9, 46–53 (2017). (PMID: 10.1016/j.genrep.2017.08.006)
Ejalonibu, M. A. et al. Drug discovery for Mycobacterium tuberculosis using structure-based computer-aided drug design approach. Int. J. Mol. Sci. 22, 13259 (2021). (PMID: 34948055870348810.3390/ijms222413259)
Trunz, B. B., Fine, P. & Dye, C. Effect of BCG vaccination on childhood tuberculous meningitis and miliary tuberculosis worldwide: A meta-analysis and assessment of cost-effectiveness. Lancet 367, 1173–1180 (2006). (PMID: 1661656010.1016/S0140-6736(06)68507-3)
Mangtani, P. et al. Protection by BCG vaccine against tuberculosis: A systematic review of randomized controlled trials. Clin. Infect. Dis. 58, 470–480 (2014). (PMID: 2433691110.1093/cid/cit790)
Ansari, M. A. et al. RD antigen based nanovaccine imparts long term protection by inducing memory response against experimental murine tuberculosis. PLoS ONE 6, e22889 (2011). (PMID: 21853054315491110.1371/journal.pone.0022889)
Baldwin, S. L. et al. Prophylactic efficacy against Mycobacterium tuberculosis using ID93 and lipid-based adjuvant formulations in the mouse model. PLoS ONE 16, e0247990 (2021). (PMID: 33705411795185010.1371/journal.pone.0247990)
Harris, R. C. et al. Cost-effectiveness of routine adolescent vaccination with an M72/AS01E-like tuberculosis vaccine in South Africa and India. Nat. Commun. 13, 602 (2022). (PMID: 35105879880759110.1038/s41467-022-28234-7)
Sanches, R. C. O. et al. Immunoinformatics design of multi-epitope peptide-based vaccine against Schistosoma mansoni using transmembrane proteins as a target. Front. Immunol. 12, 621706 (2021). (PMID: 33737928796108310.3389/fimmu.2021.621706)
Vasina, D. V. et al. First-in-human trials of GamTBvac, a recombinant subunit tuberculosis vaccine candidate: Safety and immunogenicity assessment. Vaccines 7, 166 (2019). (PMID: 31683812696398010.3390/vaccines7040166)
Maharaj, L. et al. Immunoinformatics approach for multi-epitope vaccine design against P. falciparum malaria. Infect. Genet. Evol. 92, 104875 (2021). (PMID: 3390589010.1016/j.meegid.2021.104875)
Jafari Najaf Abadi, M. H. et al. In silico design and immunoinformatics analysis of a chimeric vaccine construct based on Salmonella pathogenesis factors. Microb. Pathog. 180, 106130 (2023). (PMID: 3712152410.1016/j.micpath.2023.106130)
Aslam, S. et al. Proteome based mapping and reverse vaccinology techniques to contrive multi-epitope based subunit vaccine (MEBSV) against Streptococcus pyogenes. Infect. Genet. Evol. 100, 105259 (2022). (PMID: 3523166710.1016/j.meegid.2022.105259)
Li, M. et al. Design of a multi-epitope vaccine candidate against Brucella melitensis. Sci. Rep. 12, 10146 (2022). (PMID: 35710873920298710.1038/s41598-022-14427-z)
Akhtar, N., Singh, A., Upadhyay, A. K. & Mannan, M. A. Design of a multi-epitope vaccine against the pathogenic fungi Candida tropicalis using an in silico approach. J. Genet. Eng. Biotechnol. 20, 140 (2022). (PMID: 36175808952186710.1186/s43141-022-00415-3)
Tahir ul Qamar, M. et al. Designing multi-epitope vaccine against Staphylococcus aureus by employing subtractive proteomics, reverse vaccinology and immuno-informatics approaches. Comput. Biol. Med. 132, 104389 (2021). (PMID: 3386625010.1016/j.compbiomed.2021.104389)
Shamakhi, A. & Kordbacheh, E. Immunoinformatic design of an epitope-based immunogen candidate against Bacillus anthracis. Inform. Med. Unlocked 24, 100574 (2021). (PMID: 10.1016/j.imu.2021.100574)
Albutti, A. An integrated computational framework to design a multi-epitopes vaccine against Mycobacterium tuberculosis. Sci. Rep. 11, 21929 (2021). (PMID: 34753983857866010.1038/s41598-021-01283-6)
Mubarak, A. S., Ameen, Z. S., Hassan, A. S. & Ozsahin, D. U. Enhancing tuberculosis vaccine development: A deconvolution neural network approach for multi-epitope prediction. Sci. Rep. 14, 10375 (2024). (PMID: 387107371107412210.1038/s41598-024-59291-1)
Moradi, J., Tabrizi, M., Izad, M., Mosavari, N. & Feizabadi, M. M. Designing a novel multi-epitope DNA-based vaccine against tuberculosis: In Silico approach. Jundishapur J. Microbiol. 10, e67156 (2017). (PMID: 10.5812/jjm.43950)
Al Tbeishat, H. Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications. Sci. Rep. 12, 4645 (2022). (PMID: 35301360892947110.1038/s41598-022-08506-4)
Cheng, P., Wang, L. & Gong, W. In silico analysis of peptide-based biomarkers for the diagnosis and prevention of latent tuberculosis infection. Front. Microbiol. 13, 947852 (2022). (PMID: 35836423927395110.3389/fmicb.2022.947852)
Jiang, F. et al. PP19128R, a multiepitope vaccine designed to prevent latent tuberculosis infection, induced immune responses in silico and in vitro assays. Vaccines 11, 856 (2023). (PMID: 371127681014584110.3390/vaccines11040856)
Shiraz, M., Lata, S., Kumar, P., Shankar, U. N. & Akif, M. Immunoinformatics analysis of antigenic epitopes and designing of a multi-epitope peptide vaccine from putative nitro-reductases of Mycobacterium tuberculosis DosR. Infect. Genet. Evol. 94, 105017 (2021). (PMID: 3433215710.1016/j.meegid.2021.105017)
Sakai, S. et al. CD4 T cell-derived IFN-γ plays a minimal role in control of pulmonary mycobacterium tuberculosis infection and must be actively repressed by PD-1 to prevent lethal disease. PLoS Pathog. 12, e1005667 (2016). (PMID: 27244558488708510.1371/journal.ppat.1005667)
Rozot, V. et al. Mycobacterium tuberculosis-specific CD8+ T cells are functionally and phenotypically different between latent infection and active disease. Eur. J. Immunol. 43, 1568–1577 (2013). (PMID: 23456989653509110.1002/eji.201243262)
Chen, X., Zaro, J. L. & Shen, W.-C. Fusion protein linkers: Property, design and functionality. Adv. Drug Deliv. Rev. 65, 1357–1369 (2013). (PMID: 2302663710.1016/j.addr.2012.09.039)
Arai, R., Ueda, H., Kitayama, A., Kamiya, N. & Nagamune, T. Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein Eng. Design Sel. 14, 529–532 (2001). (PMID: 10.1093/protein/14.8.529)
Livingston, B. et al. A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes. J. Immunol. 168, 5499–5506 (2002). (PMID: 1202334410.4049/jimmunol.168.11.5499)
Yang, Y. et al. In silico design of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations. Hum. Vaccins Immunother. 11, 795–805 (2015). (PMID: 10.1080/21645515.2015.1012017)
Yano, A. et al. An ingenious design for peptide vaccines. Vaccine 23, 2322–2326 (2005). (PMID: 1575562010.1016/j.vaccine.2005.01.031)
Maphasa, R. E., Meyer, M. & Dube, A. The macrophage response to Mycobacterium tuberculosis and opportunities for autophagy inducing nanomedicines for tuberculosis therapy. Front. Cell. Infect. Microbiol. 10, 618414 (2021). (PMID: 33628745789768010.3389/fcimb.2020.618414)
Kleinnijenhuis, J., Oosting, M., Joosten, L. A. B., Netea, M. G. & Van Crevel, R. Innate immune recognition of Mycobacterium tuberculosis. Clin. Dev. Immunol. 2011, 405310 (2011). (PMID: 21603213309542310.1155/2011/405310)
Bai, W. et al. TLR3 regulates mycobacterial RNA-induced IL-10 production through the PI3K/AKT signaling pathway. Cell. Signal. 26, 942–950 (2014). (PMID: 2446270510.1016/j.cellsig.2014.01.015)
Nayak, S. S., Sethi, G. & Ramadas, K. Design of multi-epitope based vaccine against Mycobacterium tuberculosis: A subtractive proteomics and reverse vaccinology based immunoinformatics approach. J. Biomol. Struct. Dyn. https://doi.org/10.1080/07391102.2023.2178511 (2023). (PMID: 10.1080/07391102.2023.217851138018914)
Kumari, R. S., Sethi, G. & Krishna, R. Development of multi-epitope based subunit vaccine against Mycobacterium tuberculosis using immunoinformatics approach. J. Biomol. Struct. Dyn. https://doi.org/10.1080/07391102.2023.2270065 (2023). (PMID: 10.1080/07391102.2023.227006537990487)
معلومات مُعتمدة: CRC21022 National Research Council of Science and Technology
فهرسة مساهمة: Keywords: Mycobacterium tuberculosis; Docking; In silico cloning; Molecular dynamics simulation; Multi-epitope vaccine
المشرفين على المادة: 0 (Antigens, Bacterial)
0 (Epitopes, B-Lymphocyte)
0 (Epitopes, T-Lymphocyte)
0 (Toll-Like Receptor 2)
0 (Tuberculosis Vaccines)
0 (Vaccines, Subunit)
تواريخ الأحداث: Date Created: 20240710 Date Completed: 20240710 Latest Revision: 20240801
رمز التحديث: 20240802
مُعرف محوري في PubMed: PMC11237054
DOI: 10.1038/s41598-024-66858-5
PMID: 38987613
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-024-66858-5