Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein
Tóm tắt
Haemophilus influenzae is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of Haemophilus influenzae. Hence, the purpose of this research is to employ bioinformatics and immunoinformatics approaches to design a Multi-Epitope Vaccine candidate employing the pathogenic cell division protein FtsN that specifically combat all the Haemophilus influenzae strains. The current research focuses on developing subunit vaccine in contrast to vaccines generated from the entire pathogen. This will be accomplished by combining multiple bioinformatics and immunoinformatics approaches. As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein–protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the Toll-like receptor 4 protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. In addition, it is necessary to conduct experiments and research in the laboratory to demonstrate that the vaccine that has been developed is immunogenic and protective.
Tài liệu tham khảo
Bjellqvist B et al (1993) The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis 14(1):1023–1031. https://doi.org/10.1002/elps.11501401163
Bui H-H, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A (2006) Predicting population coverage of T cell epitope-based diagnostics and vaccines. BMC Bioinform 7:153. https://doi.org/10.1186/1471-2105-7-153
Butler DF, Myers AL (2018) Changing epidemiology of Haemophilus influenzae in children. Infect Dis Clin 32(1):119–128. https://doi.org/10.1016/j.idc.2017.10.005
Calis JJA et al (2013) Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 9(10):10. https://doi.org/10.1371/journal.pcbi.1003266
Chen D et al (2018) Microbial virulence, molecular epidemiology and pathogenic factors of fluoroquinolone-resistant Haemophilus influenzae infections in Guangzhou, China. Ann Clin Microbiol Antimicrob 17(1):41. https://doi.org/10.1186/s12941-018-0290-9
Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci Publ Protein Soc. 2(9):9. https://doi.org/10.1002/pro.5560020916
Cripps AW, Foxwell R, Kyd J (2002) Challenges for the development of vaccines against Haemophilus influenzae and Neisseria meningitidis. Curr Opin Immunol 14(5):553–557. https://doi.org/10.1016/S0952-7915(02)00373-4
Designing a chimeric subunit vaccine for influenza virus, based on HA2, M2e and CTxB: a bioinformatics study|BMC Molecular and Cell Biology|Full Text. https://bmcmolcellbiol.biomedcentral.com/articles/https://doi.org/10.1186/s12860-020-00334-6 Accessed 20 Sep 2022
Dhanda SK, Vir P, Raghava GPS (2013a) Designing of interferon-gamma inducing MHC class-II binders. Biol Direct 8:30. https://doi.org/10.1186/1745-6150-8-30
Dhanda SK, Gupta S, Vir P, Raghava GPS (2013b) Prediction of IL4 inducing peptides. Clin Dev Immunol 2013:263952. https://doi.org/10.1155/2013/263952
Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform 8(1):1. https://doi.org/10.1186/1471-2105-8-4
Fleischmann RD et al (1995) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269(5223):496–512. https://doi.org/10.1126/science.7542800
Gasteiger E et al (2005) Protein identification and analysis tools on the ExPASy server. In: Walker JM (ed) The proteomics protocols handbook. Humana Press, Totowa, pp 571–607. https://doi.org/10.1385/1-59259-890-0:571
Geourjon C, Deléage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci CABIOS 11(6):6. https://doi.org/10.1093/bioinformatics/11.6.681
Gilsdorf JR (1987) Haemophilus influenzae non-type b infections in children. Am J Dis Child 141(10):1063–1065. https://doi.org/10.1001/archpedi.141.10.1063
Grote A et al (2005) JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. https://doi.org/10.1093/nar/gki376
Gupta S et al (2013) In silico approach for predicting toxicity of peptides and proteins. PLoS ONE 8(9):9. https://doi.org/10.1371/journal.pone.0073957
Haemophilus influenzae infections in the H. influenzae type b conjugate vaccine era | J Clin Microbiol. https://journals.asm.org/doi/https://doi.org/10.1128/JCM.05476-11 accessed 20 Sep 2022
Hoover DM, Wu Z, Tucker K, Lu W, Lubkowski J (2003) Antimicrobial characterization of human beta-defensin 3 derivatives. Antimicrob Agents Chemother 47(9):9. https://doi.org/10.1128/AAC.47.9.2804-2809.2003
Huska B, Kubinec C, Sadarangani M, Ulanova M (2022) Seroprevalence of IgG and IgM antibodies to Haemophilus influenzae type a in Canadian children. Vaccine 40(8):1128–1134. https://doi.org/10.1016/j.vaccine.2022.01.029
Immunoinformatics Approach for Epitope-Based Peptide Vaccine Design and Active Site Prediction against Polyprotein of Emerging Oropouche Virus. https://www.hindawi.com/journals/jir/2018/6718083/ accessed 20 Sep 2022
Jalalvand F, Riesbeck K (2018) Update on non-typeable Haemophilus influenzae-mediated disease and vaccine development. Expert Rev Vaccines 17(6):503–512. https://doi.org/10.1080/14760584.2018.1484286
Kilian M, Thomsen B, Petersen TE, Bleeg H (1983) Molecular biology of Haemophilus influenzae IgA1 proteases. Mol Immunol 20(9):1051–1058. https://doi.org/10.1016/0161-5890(83)90046-9
Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. https://doi.org/10.1093/nar/gkh468
Ko J, Park H, Heo L, Seok C (2012) GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. https://doi.org/10.1093/nar/gks493
Kringelum JV, Lundegaard C, Lund O, Nielsen M (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol 8(12):12. https://doi.org/10.1371/journal.pcbi.1002829
Kurcinski M, Oleniecki T, Ciemny MP, Kuriata A, Kolinski A, Kmiecik S (2019) CABS-flex standalone: a simulation environment for fast modeling of protein flexibility. Bioinformatics 35(4):694–695. https://doi.org/10.1093/bioinformatics/bty685
Kuriata A et al (2018) CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures. Nucleic Acids Res 46(W1):W1. https://doi.org/10.1093/nar/gky356
Laskowski RA (2009) PDBsum new things. Nucleic Acids Res. https://doi.org/10.1093/nar/gkn860
Livingston B et al (2002) A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes. J Immunol 168(11):5499–5506. https://doi.org/10.4049/jimmunol.168.11.5499
López-López N, Gil-Campillo C, Díez-Martínez R, Garmendia J (2021) Learning from –omics strategies applied to uncover Haemophilus influenzae host-pathogen interactions: current status and perspectives. Comput Struct Biotechnol J 19:3042–3050. https://doi.org/10.1016/j.csbj.2021.05.026
Lovell SC et al (2003) Structure validation by Calpha geometry: phi, psi and Cbeta deviation. Proteins 50(3):3. https://doi.org/10.1002/prot.10286
Mahram A, Herbordt MC (2010) Fast and accurate NCBI BLASTP: acceleration with multiphase FPGA-based prefiltering. In: Proceedings of the 24th ACM International Conference on Supercomputing, New York, NY, USA, 2010, pp. 73–82. https://doi.org/10.1145/1810085.1810099
McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinforma Oxf Engl. 16(4):4. https://doi.org/10.1093/bioinformatics/16.4.404
Mirzaei R et al (2020) The importance of intracellular bacterial biofilm in infectious diseases. Microb Pathog 147:104393. https://doi.org/10.1016/j.micpath.2020.104393
Mugunthan SP, Harish MC (2021) Multi-epitope-Based vaccine designed by targeting cytoadherence proteins of Mycoplasma gallisepticum. ACS Omega 6(21):13742–13755. https://doi.org/10.1021/acsomega.1c01032
Nagpal G et al (2017) Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci Rep 7:42851. https://doi.org/10.1038/srep42851
Nain Z, Karim MM, Sen MK, Adhikari UK (2020) Structural basis and designing of peptide vaccine using PE-PGRS family protein of Mycobacterium ulcerans—an integrated vaccinomics approach. Mol Immunol 120:146–163. https://doi.org/10.1016/j.molimm.2020.02.009
NCBI Resource Coordinators (2013) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 41(D1):D8–D20. https://doi.org/10.1093/nar/gks1189
Pandey R et al (2016) Exploring dual inhibitory role of febrifugine analogues against Plasmodium utilizing structure based virtual screening and molecular dynamic simulation. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2016.1161560
Pinto M et al (2019) Insights into the population structure and pan-genome of Haemophilus influenzae. Infect Genet Evol 67:126–135. https://doi.org/10.1016/j.meegid.2018.10.025
Potts CC et al (2019) Genomic characterization of Haemophilus influenzae: a focus on the capsule locus. BMC Genomics 20(1):733. https://doi.org/10.1186/s12864-019-6145-8
Prediction of residues in discontinuous B cell epitopes using protein 3D structures—Haste Andersen—2006—Protein Science—Wiley Online Library.” https://onlinelibrary.wiley.com/doi/full/https://doi.org/10.1110/ps.062405906 accessed 20 Sep 2022
Pymol: an open-source molecular graphics tool—ScienceOpen. https://www.scienceopen.com/document?vid=4362f9a2-0b29-433f-aa65-51db01f4962f accessed 20 Sep 2022
Qamar M et al (2021) Designing multi-epitope vaccine against Staphylococcus aureus by employing subtractive proteomics, reverse vaccinology and immuno-informatics approaches. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2021.104389
Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M (2020) Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data. J Proteome Res 19(6):6. https://doi.org/10.1021/acs.jproteome.9b00874
Saadati A, Kholafazad-Kordasht H, Ehsani M, Hasanzadeh M, Seidi F, Shadjou N (2021) An innovative flexible and portable DNA based biodevice towards sensitive identification of Haemophilus influenzae bacterial genome: a new platform for the rapid and low cost recognition of pathogenic bacteria using point of care (POC) analysis. Microchem J 169:106610. https://doi.org/10.1016/j.microc.2021.106610
Saha S, Raghava GPS (2006a) AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. https://doi.org/10.1093/nar/gkl343
Saha S, Raghava GPS (2006b) Prediction of continuous B cell epitopes in an antigen using recurrent neural network. Proteins 65(1):1. https://doi.org/10.1002/prot.21078
Serwecińska L (2020) Antimicrobials and antibiotic-resistant bacteria: a risk to the environment and to public health. Water 12(12):12. https://doi.org/10.3390/w12123313
Slack M et al (2020) Haemophilus influenzae type b disease in the era of conjugate vaccines: critical factors for successful eradication. Expert Rev Vaccines 19(10):903–917. https://doi.org/10.1080/14760584.2020.1825948
Soeters HM et al (2018) Current epidemiology and trends in invasive Haemophilus influenzae disease—United States, 2009–2015. Clin Infect Dis 67(6):881–889. https://doi.org/10.1093/cid/ciy187
Suga S et al (2018) A nationwide population-based surveillance of invasive Haemophilus influenzae diseases in children after the introduction of the Haemophilus influenzae type b vaccine in Japan. Vaccine 36(38):5678–5684. https://doi.org/10.1016/j.vaccine.2018.08.029
The ClusPro web server for protein–protein docking|Nature Protocols. https://www.nature.com/articles/nprot.2016.169 accessed 20 Sep 2022
Vitovski S, Dunkin KT, Howard AJ, Sayers JR (2002) Nontypeable Haemophilus influenzae in carriage and disease a difference in IgA1 protease activity levels. JAMA 287(13):1699–1705. https://doi.org/10.1001/jama.287.13.1699
Wen S, Feng D, Chen D, Yang L, Xu Z (2020) Molecular epidemiology and evolution of Haemophilus influenzae. Infect Genet Evol 80:104205. https://doi.org/10.1016/j.meegid.2020.104205
Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. https://doi.org/10.1093/nar/gkm290
Wilson C, Mitchelmore P, Brown A (2020) Culture-independent multilocus sequence typing (MLST) screening for Haemophilus influenzae cross-infection in non-cystic fibrosis bronchiectasis (NCFB). Access Microbiol. 2(2):179. https://doi.org/10.1099/acmi.fis2019.po0173
Zhang Q et al (2008) Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res. https://doi.org/10.1093/nar/gkn254
Zhang J, Zhao X, Sun P, Gao B, Ma Z (2014) Conformational B cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering. BioMed Res Int 2014:689219. https://doi.org/10.1155/2014/689219