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A computational approach to design a polyvalent vaccine against human respiratory syncytial virus
Meng, J., Stobart, C. C., Hotard, A. L. & Moore, M. L. An overview of respiratory syncytial virus. PLoS Pathog. 10(4), e1004016. https://doi.org/10.1371/journal.ppat.1004016 (2014).
Clark, C. M. & Guerrero-Plata, A. Respiratory syncytial virus vaccine approaches: A current overview. Curr. Clin. Microbiol. Rep. 4(4), 202–207. https://doi.org/10.1007/s40588-017-0074-6 (2017).
Killikelly, A. et al. Respiratory syncytial virus: Overview of the respiratory syncytial virus vaccine candidate pipeline in Canada. Can. Commun. Dis. Rep. 46(4), 56. https://doi.org/10.14745/ccdr.v46i04a01 (2020).
Vandini, S., Biagi, C. & Lanari, M. Respiratory syncytial virus: The influence of serotype and genotype variability on clinical course of infection. Int. J. Mol. Sci. 18(8), 1717. https://doi.org/10.3390/ijms18081717 (2017).
Bianchini, S. et al. Role of respiratory syncytial virus in pediatric pneumonia. Microorganisms. 8(12), 2048. https://doi.org/10.3390/microorganisms8122048 (2020).
Agoti, C. N. et al. Genomic analysis of respiratory syncytial virus infections in households and utility in inferring who infects the infant. Sci. Rep. 9(1), 1–4. https://doi.org/10.1038/s41598-019-46509-w (2019).
Karron, R. A. et al. Evaluation of two live, cold-passaged, temperature-sensitive respiratory syncytial virus vaccines in chimpanzees and in human adults, infants, and children. J. Infect. Dis. 176(6), 1428–1436. https://doi.org/10.1086/514138 (1997).
Hurwitz, J. L. Respiratory syncytial virus vaccine development. Expert Rev. Vaccines. 10(10), 1415–1433. https://doi.org/10.1586/erv.11.120 (2011).
Griffiths, C., Drews, S. J. & Marchant, D. J. Respiratory syncytial virus: Infection, detection, and new options for prevention and treatment. Clin. Microbiol. Rev. 30(1), 277–319. https://doi.org/10.1128/CMR.00010-16 (2017).
Jordan, R. et al. Antiviral efficacy of a respiratory syncytial virus (RSV) fusion inhibitor in a bovine model of RSV infection. Antimicrob. Agents Chemother. 59(8), 4889–4900. https://doi.org/10.1128/AAC.00487-15 (2015).
Collins, P. L., Fearns, R. & Graham, B. S. Respiratory syncytial virus: Virology, reverse genetics, and pathogenesis of disease. In Challenges and Opportunities for Respiratory Syncytial Virus Vaccines 3–38 (Springer, 2013). https://doi.org/10.1007/978-3-642-38919-1_1.
Lu, B., Ma, C. H., Brazas, R. & Jin, H. The major phosphorylation sites of the respiratory syncytial virus phosphoprotein are dispensable for virus replication in vitro. J. Virol. 76(21), 10776–10784. https://doi.org/10.1128/JVI.76.21.10776-10784.2002 (2002).
Khan, M. T. et al. Immunoinformatics and molecular dynamics approaches: Next generation vaccine design against West Nile virus. PLoS ONE 16(6), e0253393. https://doi.org/10.1371/journal.pone.0253393 (2021).
Lobanov, M. Y., Bogatyreva, N. S. & Galzitskaya, O. V. Radius of gyration as an indicator of protein structure compactness. Mol. Biol. 42(4), 623–628. https://doi.org/10.1134/S0026893308040195 (2008).
Pettersen, E. F. et al. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Sci. 30(1), 70–82. https://doi.org/10.1002/pro.3943 (2021).
Zhang, L. Multi-epitope vaccines: A promising strategy against tumors and viral infections. Cell. Mol. Immunol. 15(2), 182–184. https://doi.org/10.1038/cmi.2017.92 (2018).
Sanchez-Trincado, J. L., Gomez-Perosanz, M. & Reche, P. A. Fundamentals and methods for T-and B-cell epitope prediction. J. Immunol. Res. https://doi.org/10.1155/2017/2680160 (2017).
van Schaik, S. M. et al. Role of interferon gamma in the pathogenesis of primary respiratory syncytial virus infection in BALB/c mice. J. Med. Virol. 62(2), 257–266. https://doi.org/10.1002/1096-9071(200010)62:2%3c257::AID-JMV19%3e3.0.CO;2-M (2000).
Turner, M. D., Nedjai, B., Hurst, T. & Pennington, D. J. Cytokines and chemokines: At the crossroads of cell signalling and inflammatory disease. Biochim. Biophys. Acta Mol. Cell Res. 1843(11), 2563–2582. https://doi.org/10.1016/j.bbamcr.2014.05.014 (2014).
Chang, H. D. & Radbruch, A. The pro-and anti-inflammatory potential of interleukin-12. Ann. N. Y. Acad. Sci. 1109(1), 40–46. https://doi.org/10.1196/annals.1398.006 (2007).
Brown, M. A. & Hural, J. Functions of IL-4 and control of its expression. Crit. Rev. Immunol. 17(1), 1–32. https://doi.org/10.1615/critrevimmunol.v17.i1.10 (1997).
Luckheeram, R. V., Zhou, R., Verma, A. D. & Xia, B. CD4+ T cells: Differentiation and functions. Clin. Dev. Immunol. https://doi.org/10.1155/2012/925135 (2012).
Mohammadi, Y., Nezafat, N., Negahdaripour, M., Eskandari, S. & Zamani, M. In silico design and evaluation of a novel mRNA vaccine against BK virus: A reverse vaccinology approach. Immunol. Res. 29, 1–20. https://doi.org/10.1007/s12026-022-09351-3 (2022).
Hajighahramani, N. et al. Computational design of a chimeric epitope-based vaccine to protect against Staphylococcus aureus infections. Mol. Cell Probes. 46, 101414. https://doi.org/10.1016/j.mcp.2019.06.004 (2019).
Bagheri, A., Nezafat, N., Eslami, M., Ghasemi, Y. & Negahdaripour, M. Designing a therapeutic and prophylactic candidate vaccine against human papillomavirus through vaccinomics approaches. Infect. Genet. Evol. 95, 105084. https://doi.org/10.1016/j.meegid.2021.105084 (2021).
Abinaya, R. V. & Viswanathan, P. Biotechnology-based therapeutics. In Translational Biotechnology 27–52 (Academic Press, 2021). https://doi.org/10.1016/B978-0-12-821972-0.00019-8.
Funderburg, N. et al. Human β-defensin-3 activates professional antigen-presenting cells via Toll-like receptors 1 and 2. Proc. Natl. Acad. Sci. 104(47), 18631–18635. https://doi.org/10.1073/pnas.0702130104 (2007).
Judge, C. J. et al. HBD-3 induces NK cell activation, IFN-γ secretion and mDC dependent cytolytic function. Cell. Immunol. 297(2), 61–68. https://doi.org/10.1016/j.cellimm.2015.06.004 (2015).
Negahdaripour, M. et al. Structural vaccinology considerations for in silico designing of a multi-epitope vaccine. Infect. Genet. Evol. 58, 96–109. https://doi.org/10.1016/j.meegid.2017.12.008 (2018).
Štěpánová, S. & Kašička, V. Application of capillary electromigration methods for physicochemical measurements. In Capillary Electromigration Separation Methods 547–591 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-809375-7.00024-1.
Kyte, J. & Doolittle, R. F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1), 105–132. https://doi.org/10.1016/0022-2836(82)90515-0 (1982).
Chang, K. Y. & Yang, J. R. Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS ONE https://doi.org/10.1371/journal.pone.0070166 (2013).
Hamasaki-Katagiri, N. et al. The importance of mRNA structure in determining the pathogenicity of synonymous and non-synonymous mutations in haemophilia. Haemophilia 23(1), e8-17. https://doi.org/10.1111/hae.13107 (2017).
Craig, D. B. & Dombkowski, A. A. Disulfide by design 2.0: A web-based tool for disulfide engineering in proteins. BMC Bioinform. 14(1), 346. https://doi.org/10.1186/1471-2105-14-346 (2013).
Dombkowski, A. A. & Crippen, G. M. Disulfide recognition in an optimized threading potential. Protein Eng. 13(10), 679–689. https://doi.org/10.1093/protein/13.10.679 (2000).
Shental-Bechor, D. & Levy, Y. Effect of glycosylation on protein folding: A close look at thermodynamic stabilization. Proc. Natl. Acad. Sci. 105(24), 8256–8261. https://doi.org/10.1073/pnas.0801340105 (2008).
Ojha, R. & Prajapati, V. K. Cognizance of posttranslational modifications in vaccines: A way to enhanced immunogenicity. J. Cell. Physiol. https://doi.org/10.1002/jcp.30483 (2021).
Zarling, A. L. et al. Phosphorylated peptides are naturally processed and presented by major histocompatibility complex class I molecules in vivo. J. Exp. Med. 192(12), 1755–1762. https://doi.org/10.1084/jem.192.12.1755 (2000).
Murawski, M. R. et al. Respiratory syncytial virus activates innate immunity through Toll-like receptor 2. J. Virol. 83(3), 1492–1500. https://doi.org/10.1128/JVI.00671-08 (2009).
Chang, S., Dolganiuc, A. & Szabo, G. Toll-like receptors 1 and 6 are involved in TLR2-mediated macrophage activation by hepatitis C virus core and NS3 proteins. J. Leukoc. Biol. 82(3), 479–487. https://doi.org/10.1189/jlb.0207128 (2007).
Compton, T. et al. Human cytomegalovirus activates inflammatory cytokine responses via CD14 and Toll-like receptor 2. J. Virol. 77(8), 4588–4596. https://doi.org/10.1128/JVI.77.8.4588-4596.2003 (2003).
Kurt-Jones, E. A. et al. Herpes simplex virus 1 interaction with Toll-like receptor 2 contributes to lethal encephalitis. Proc. Natl. Acad. Sci. 101(5), 1315–1320. https://doi.org/10.1073/pnas.0308057100 (2004).
Jin, B., Sun, T., Yu, X. H., Yang, Y. X. & Yeo, A. E. The effects of TLR activation on T-cell development and differentiation. Clin. Dev. Immunol. https://doi.org/10.1155/2012/836485 (2012).
Shey, R. A. et al. In-silico design of a multi-epitope vaccine candidate against onchocerciasis and related filarial diseases. Sci. Rep. 9(1), 1–18. https://doi.org/10.1038/s41598-019-40833-x (2019).
Carbone, A., Zinovyev, A. & Képes, F. Codon adaptation index as a measure of dominating codon bias. Bioinformatics 19, 2005–2015. https://doi.org/10.1093/bioinformatics/btg272 (2003).
Doytchinova, I. A. & Flower, D. R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8, 4. https://doi.org/10.1186/1471-2105-8-4 (2007).
Gasteiger, E. et al. Protein identification and analysis tools on the ExPASy server. In The Proteomics Protocols Handbook 571–607 (Humana Press, 2005). https://doi.org/10.1385/1-59259-890-0:571.
Vita, R. et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1006 (2018).
Jespersen, M. C., Peters, B., Nielsen, M. & Marcatili, P. BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 45(W1), W24–W29. https://doi.org/10.1093/nar/gkx346 (2017).
Ponomarenko, J. et al. ElliPro: A new structure-based tool for the prediction of antibody epitopes. BMC Bioinform. 9(1), 1–8. https://doi.org/10.1186/1471-2105-9-514 (2008).
Bui, H. H., Sidney, J., Li, W., Fusseder, N. & Sette, A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinform. 8(1), 361. https://doi.org/10.1186/1471-2105-8-361 (2007).
Dimitrov, I., Flower, D. R. & Doytchinova, I. AllerTOP-a server for in-silico prediction of allergens. In BMC Bioinformatics, vol. 14, no. 6, S4. (BioMed Central, 2013) https://doi.org/10.1186/1471-2105-14-S6-S4.
Dimitrov, I., Naneva, L., Doytchinova, I. & Bangov, I. AllergenFP: Allergenicity prediction by descriptor fingerprints. Bioinformatics 30(6), 846–851. https://doi.org/10.1093/bioinformatics/btt619 (2014).
Krogh, A., Larsson, B., Von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J. Mol. Biol. 305(3), 567–580. https://doi.org/10.1006/jmbi.2000.4315 (2001).
Dhanda, S. K., Vir, P. & Raghava, G. P. Designing of interferon-gamma inducing MHC class-II binders. Biol. Direct. 5(8), 30. https://doi.org/10.1186/1745-6150-8-30 (2013).
Dhanda, S. K., Gupta, S., Vir, P. & Raghava, G. P. Prediction of IL4 inducing peptides. Clin. Dev. Immunol. https://doi.org/10.1155/2013/263952 (2013).
Nagpal, G. et al. Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci. Rep. 17(7), 42851. https://doi.org/10.1038/srep42851 (2017).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 (1990).
Bui, H. H. et al. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinform. 17(7), 153. https://doi.org/10.1186/1471-2105-7-153 (2006).
Thomsen, M., Lundegaard, C., Buus, S., Lund, O. & Nielsen, M. MHCcluster, a method for functional clustering of MHC molecules. Immunogenetics 65(9), 655–665. https://doi.org/10.1007/s00251-013-0714-9 (2013).
Cheng, J., Randall, A. Z., Sweredoski, M. J. & Baldi, P. SCRATCH: A protein structure and structural feature prediction server. Nucleic Acids Res. 33(Web Server issue), W72–W76. https://doi.org/10.1093/nar/gki396 (2005).
Hebditch, M., Carballo-Amador, M. A., Charonis, S., Curtis, R. & Warwicker, J. Protein-Sol: A web tool for predicting protein solubility from sequence. Bioinformatics 33(19), 3098–3100. https://doi.org/10.1093/bioinformatics/btx345 (2017).
Buchan, D. W. & Jones, D. T. The PSIPRED protein analysis workbench: 20 years on. Nucleic Acids Res. 47(W1), W402–W407. https://doi.org/10.1093/nar/gkz297 (2019).
Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292(2), 195–202. https://doi.org/10.1006/jmbi.1999.3091 (1999).
Garnier, J., Gibrat, J. F. & Robson, B. [32] GOR method for predicting protein secondary structure from amino acid sequence. In Methods in Enzymology Vol. 266 540–553 (Academic Press, 1996). https://doi.org/10.1016/S0076-6879(96)66034-0.
Geourjon, C. & Deleage, G. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 11(6), 681–684. https://doi.org/10.1093/bioinformatics/11.6.681 (1995).
Levin, J. M., Robson, B. & Garnier, J. An algorithm for secondary structure determination in proteins based on sequence similarity. FEBS Lett. 205(2), 303–308. https://doi.org/10.1016/0014-5793(86)80917-6 (1986).
Källberg, M. et al. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 7, 1511. https://doi.org/10.1038/nprot.2012.085 (2012).
Wang, S., Li, W., Zhang, R., Liu, S. & Xu, J. CoinFold: A web server for protein contact prediction and contact-assisted protein folding. Nucleic Acids Res. 44(W1), W361–W366. https://doi.org/10.1093/nar/gkw307 (2016).
Ko, J., Park, H., Heo, L. & Seok, C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. 40(W1), W294–W297. https://doi.org/10.1093/nar/gks493 (2012).
Nugent, T., Cozzetto, D. & Jones, D. T. Evaluation of predictions in the CASP10 model refinement category. Proteins Struct. Funct. Bioinform. 82, 98–111. https://doi.org/10.1002/prot.24377 (2014).
Laskowski, R. A., MacArthur, M. W. & Thornton, J. M. PROCHECK: Validation of Protein-Structure Coordinates (2006).
Wiederstein, M. & Sippl, M. J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucl. Acids Res. 35(suppl_2), W407–W410. https://doi.org/10.1093/nar/gkm290 (2007).
Dombkowski, A. A. Disulfide by Design™: A computational method for the rational design of disulfide bonds in proteins. Bioinformatics 19(14), 1852–1853. https://doi.org/10.1093/bioinformatics/btg231 (2003).
Dombkowski, A. A., Sultana, K. Z. & Craig, D. B. Protein disulfide engineering. FEBS Lett. 588(2), 206–212. https://doi.org/10.1016/j.febslet.2013.11.024 (2014).
Moin, A. T. et al. Immunoinformatics approach to design novel subunit vaccine against the Epstein–Barr virus. Microbiol. Spectr. 10(5), e0115122. https://doi.org/10.1128/spectrum.01151-22 (2022).
Petersen, M. T. N., Jonson, P. H. & Petersen, S. B. Amino acid neighbours and detailed conformational analysis of cysteines in proteins. Protein Eng. 12, 535–548. https://doi.org/10.1093/protein/12.7.535 (1999).
Gupta, R. & Brunak, S. Prediction of glycosylation across the human proteome and the correlation to protein function. In Pac Symp Biocomput. 310–322 (2002).
Steentoft, C. et al. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J. 32(10), 1478–1488. https://doi.org/10.1038/emboj.2013.79 (2013).
Blom, N., Gammeltoft, S. & Brunak, S. Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J. Mol. Biol. 294(5), 1351–1362. https://doi.org/10.1006/jmbi.1999.3310 (1999).
Kozakov, D. et al. The ClusPro web server for protein-protein docking. Nat. Protoc. 12(2), 255–278. https://doi.org/10.1038/nprot.2016.169 (2017).
Moin, A. T. et al. Computational designing of a novel subunit vaccine for human cytomegalovirus by employing the immunoinformatics framework. J. Biomol. Struct. Dyn. 41(3), 833–855. https://doi.org/10.1080/07391102.2021.2014969 (2023).
Pierce, B. G. et al. ZDOCK server: Interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 30(12), 1771–1773. https://doi.org/10.1093/bioinformatics/btu097 (2014).
Berendsen, H. J., van der Spoel, D. & van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91(1–3), 43–56. https://doi.org/10.1016/0010-4655(95)00042-E (1995).
Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 8(9), 3257–3273. https://doi.org/10.1021/ct300400x (2012).
Vanommeslaeghe, K. et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31(4), 671–690. https://doi.org/10.1002/jcc.21367 (2010).
Zielkiewicz, J. Structural properties of water: Comparison of the SPC, SPCE, TIP4P, and TIP5P models of water. J. Chem. Phys. 123(10), 104501. https://doi.org/10.1063/1.2018637 (2005).
Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126(1), 014101. https://doi.org/10.1063/1.2408420 (2007).
Berendsen, H. J., Postma, J. V., van Gunsteren, W. F., DiNola, A. R. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81(8), 3684–3690. https://doi.org/10.1063/1.448118 (1984).
Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 52(12), 7182–7190. https://doi.org/10.1063/1.328693 (1981).
Hess, B., Bekker, H., Berendsen, H. J. & Fraaije, J. G. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 18(12), 1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12%3c1463::AID-JCC4%3e3.0.CO;2-H (1997).
Petersen, H. G. Accuracy and efficiency of the particle mesh Ewald method. J. Chem. Phys. 103(9), 3668–3679. https://doi.org/10.1063/1.470043 (1995).
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 https://doi.org/10.1371/journal.pone.0009862 (2010).
Castiglione, F., Mantile, F., De Berardinis, P. & Prisco, A. How the interval between prime and boost injection affects the immune response in a computational model of the immune system. Comput. Math. Methods Med. https://doi.org/10.1155/2012/842329 (2012).
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. https://doi.org/10.1093/nar/gki376 (2005).
Chang, K. Y. & Yang, J. R. Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS ONE 8(8), e70166. https://doi.org/10.1371/journal.pone.0070166 (2013).
Choi, E. S., Lee, S. G., Lee, S. J. & Kim, E. Rapid detection of 6×-histidine-labeled recombinant proteins by immunochromatography using dye-labeled cellulose nanobeads. Biotech. Lett. 37(3), 627–632. https://doi.org/10.1007/s10529-014-1731-y (2015).
GSL Biotech LLC. SnapGene [Computer software]. https://www.snapgene.com/ (2021).
Araf, Y. et al. Immunoinformatic design of a multivalent peptide vaccine against mucormycosis: Targeting FTR1 protein of major causative fungi. Front. Immunol. 13, 863234. https://doi.org/10.3389/fimmu.2022.863234 (2022).
Mathews, D. H., Sabina, J., Zuker, M. & Turner, D. H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288(5), 911–940. https://doi.org/10.1006/jmbi.1999.2700 (1999).
Mathews, D. H., Turner, D. H. & Zuker, M. RNA secondary structure prediction. Curr. Protoc. Nucleic Acid Chem. 28(1), 11–12. https://doi.org/10.1002/0471142700.nc1102s28 (2007).
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