Abstract
The emanation use of vaccines has shown tremendous applications of computational algorithms that can be used for amelioration of health globally. Vaccine Research has become a center area of research that embarks its applications to save several lives, reduced cost of treatment, and potential inhibitor of infectious diseases. The stimulating progress of immunoinformatics approach with the concept of peptide vaccines has proven to be productive way to target unknown antigenic proteins, complex life-cycle of infectious diseases, variability of immune system response, and long term protection. This Chapter reviews the comprehensive database analysis for the construction of vaccine design targeting epitope based approach which has proven to be a very robust method for the characterization of vaccine targets for systemic models of vaccine. The design of vaccine from traditional to computational methods enables to understand the complexity of disease causing organisms and their hyper variable nature. The investigations of vaccine include rigorous methods that validate the designed vaccine to be antigenic, immunogenic, and non-allergenic and higher solubility and furthermore predicted designed vaccine should have the capability to trigger high immune responses. The docking and simulation of the predicted peptides provide insight information of the binding energy and the stability of vaccine candidates for a better accuracy.
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References
Adam H, Andrio P, Fenollosa C, Cicin-Sain D, Orozco M, Gelpi JL (2012) MDWeb and MDMoby: an integrated web-based platform for molecular dynamics simulations. Bioinformatics 28(9):1278–1279
Alessandro S, Rino R (2010) Reverse vaccinology: developing vaccines in the era of genomics. Immunity 33(4):530–541
Angus NO, Obialor WO, Ifeanyichukwu MO, Odimegwu DC, Okoyeh JN, Emechebe GO, Adejumo SA, Ibeanu GC (2020) Immunoinformatics and vaccine development: an overview. Immunotargets Ther 9:13–30
Arafat RO, Pervin T, Mia M, Hossain M, Shahnaij M, Mahmud S, Kaderi Kibria KM (2017) Vaccinomics approach for designing potential peptide vaccine by targeting Shigella spp. Serine protease autotransporter subfamily protein SigA. J Immunol Res. https://doi.org/10.1155/2017/6412353
Atanasova M, Dimitrov I, Flower DR, Doytchinova I (2013) EpiDOCK: a molecular docking-based tool for MHC class II binding prediction. Protein Eng Des Sel 26(10):631–634
Aurelien G, Zoete V, Michielin O (2011) SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucl Acids Res 39:W270–W277
Backert L, Kohlbacher O (2015) Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Med 7(1):119
Birkir R, Alvarez B, Paul S, Peters B, Nielsen M (2020) NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucl Acids Res. https://doi.org/10.1093/nar/gkaa379
Clarisa B P-d-S, Soares I d S, Rosa DS (2018) Editorial: epitope discovery and synthetic vaccine design. Front Immunol 9:826
Duhovny D, Nussinov R, Wolfson HJ (2002) Efficient unbound docking of rigid molecules. proceedings of the 2’nd workshop on algorithms in bioinformatics (WABI) Rome, Italy. Lecture Notes in Computer Science, vol 2452. Springer, pp 185–200
Fiser A, Feig M, Brooks CL, Sali A (2002) Evolution and physics in comparative protein structure modeling. Acc Chem Res. 35:413–421. https://doi.org/10.1021/ar010061h
Goodsell DS, Morris GM, Halliday RS, Huey R, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and empirical binding free energy function. J Comp Chem 19:1639–1662
Guan P, Doytchinova IA, Zygouri C, Flower DR (2003) MHCPred: bringing a quantitative dimension to the online prediction of MHC binding. Appl Bioinf 2:63–66
Hamrouni S, Bras-Gonçalves R, Kidar A, Aoun K, Chamakh-Ayari R, Petitdidier E, Messaoudi Y, Pagniez J, Lemesre JL, Meddeb-Garnaoui A (2020) Design of multi-epitope peptides containing HLA class-I and class-II-restricted epitopes derived from immunogenic Leishmania proteins, and evaluation of CD4+ and CD8+ T cell responses induced in cured cutaneous leishmaniasis subjects. PLoS Negl Trop Dis 14(3):e0008093
Huber Sietske R, van Beek J, de Jonge J, Luytjes W, van Baarle D (2014) T cell responses to viral infections – opportunities for peptide vaccination. Front Immunol 5:171
Humphrey W, Dalke A, Schulten K (1996) VMD—visual molecular dynamics. J Mol Graphics 14:33–38
Irini AD, Darren RF (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinf 8:4
James CP, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kalé L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comp Chem 26(16):1781–1802
Jens Vindahl K, Lundegaard C, Lund O, Nielsen M (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comp Biol 8(12):e1002829
Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B, Nielsen M (2018) Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154(3):394–406
Jespersen MC, Peters B, Nielsen M, Marcatili P (2017) BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucl Acids Res. https://doi.org/10.1093/nar/gkx352
Kaur H, Garg A, Raghava GPS (2007) PEPstr: A de novo method for tertiary structure prediction of small bioactive peptides. Protein Pept Lett 14(7):626–630
Kaur R, Arora N, Jamakhani MA, Malik S, Kumar P, Anjum F, Tripathi S, Mishra A, Prasad A (2020) Development of multi-epitope chimeric vaccine against Taenia solium by exploring its proteome: an in silico approach. Exp Rev Vaccines 19(1):105–114
Kelley L, Mezulis S, Yates C et al (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protocol 10(6):845–858
Khan F, Srivastava V, Kumar A (2017) Epitope-based peptides prediction from proteome of Enterotoxigenic E coli. Int J Peptide Res Ther 24(2):323–336
Khan F, Srivastava V, Kumar A (2018) Computational identification and characterization of potential T-Cell epitope for the utility of vaccine design against Enterotoxigenic Escherichia coli. Int J Peptide Res Ther (Springer) 25:289–302
Krawczyk K, Liu X, Baker T, Shi J, Deane CM (2014) Improving B-cell epitope prediction and its application to global antibody-antigen docking. Bioinformatics 30(16):2288–2294
Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M (2007) Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinf 8:424
Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK - a program to check the stereochemical quality of protein structures. J App Cryst 26:283–291
Li W, Joshi MD, Singhania S, Ramsey KH, Murthy AK (2014) Peptide vaccine: progress and challenges. Vaccine 2(3):515–536
Lippolis JD et al (2002) Analysis of MHC class II antigen processing by quantitation of peptides that constitute nested sets. J Immunol 169:5089–5097
Monterrubio-López GP, Ribas-Aparicio RM (2015) Identification of novel potential vaccine candidates against tuberculosis based on reverse vaccinology. Biomed Res Int 12:1–16
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 16:2785–2791
Morten K, Wang H, Wang S, Peng J, Wang Z, Lu H, Xu J (2012) Template-based protein structure modeling using the RaptorX web server. Nat Protocols 7:1511–1522
Negi SS, Braun W (2009) Automated detection of conformational epitopes using phage display peptide sequences. Bioinform Biol Insights 3:71–81
Nezafat N, Ghasemi Y, Javadi G, Khoshnoud MJ, Omidinia E (2014) A novel multi-epitope peptide vaccine against cancer: an in silico approach. Theor Biol 349:121–134
Oyarzun P, Kobe B (2015) Computer-aided design of T-cell epitope-based vaccines: addressing population coverage. Int J Immunogenet 42(5):313–321
Pahil S, Taneja N, Ansari HR, Raghava GPS (2017) In silico analysis to identify vaccine candidates common to multiple serotypes of Shigella and evaluation of their immunogenicity. PLoS One 12:8
Pandey RK, Ojha R, Aathmanathan VS, Krishnan M, Prajapati VK (2018) Immunoinformatics approaches to design a novel multiepitope subunit vaccine against HIV infection. Vaccine 36:2262–2272. https://doi.org/10.1016/j.vaccine.2018.03.042
Patronov A, Dimitrov I, Flower DR, Doytchinova I (2011) Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach. BMC Str Biol 11:32
Pierre D, Oliver K (2006) SVMHC: a server for prediction of MHC-binding peptides. Nucl Acids Res 34:W194–W197
Ponomarenko JV, Bui H, Li W, Fusseder N, Bourne PE, Sette A, Peters B (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinf 9:514
Robinson J, Halliwell JA, Hayhurst JH, Flicek P, Parham P, Marsh SGE (2015) The IPD and IMGT/HLA database: allele variant databases. Nucl Acids Res 43:D423–D431
Saha S, Raghava GPS (2004) BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. In: Nicosia G, Cutello V, Bentley PJ, Timmis J (eds) Artificial immune systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin. https://doi.org/10.1007/978-3-540-30220-9_16.
Saha S, Raghava GPS (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins Struct Funct Bioinf 65:40–48
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucl Acids Res 33:W363–W367
Singh H, Raghava GPS (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics 17(12):1236–1237
Singh H, Raghava GPS (2003) ProPred I: prediction of HLA class-I binding sites. Bioinformatics 19:1009–1014
Tomar N, De RK (2010) Immunoinformatics: an integrated scenario. Immunology 131(2):153–168
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem:455–461
Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, Wheeler DK, Sette A, Peters B (2018) The Immune epitope database (IEDB). Nucl Acids Res. https://doi.org/10.1093/nar/gky1006
Xiang Z, He Y (2009) Vaxign: a web-based vaccine target design program for reverse vaccinology. Proc Vaccinol 1(1):23–29
Xu D, Zhang Y (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80(7):1715–1735
Zhang L (2018) Multi-epitope vaccines: a promising strategy against tumors and viral infections. Cell Mol Immunol 15:182–184
Zobayer N, Hossain AA, Rahman MA (2019) A combined view of B-cell epitope features in antigens. Bioinformation 15(7):530–534
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Khan, F., Kumar, A. (2021). Vaccine Design and Immunoinformatics. In: Singh, V., Kumar, A. (eds) Advances in Bioinformatics. Springer, Singapore. https://doi.org/10.1007/978-981-33-6191-1_8
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