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Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

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References

  • Peng G-W, He J-F, Lin, J-Y, et al. Epidemiological study on severe acute respiratory syndrome in Guangdong province. Zhonghua liuxingbingxue zazhi. 2003;24(5): 350–352
  • Zaki AM, van Boheemen S, Bestebroer TM, et al. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N Engl J Med. 2012;367(19):1814–1820.
  • Zhou P, Yang X-L, Wang X-G, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579(7798):270–273.
  • Lu R, Zhao X, Li J, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet. 2020;395(10224):565–574.
  • Wrapp D, Wang N, Corbett KS, et al. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science. 2020;367(6483):1260–1263.
  • Guan WJ, Ni ZY, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708–1720.
  • Guo YR, Cao QD, Hong ZS, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status. Mil Med Res. 2020;7(1):11.
  • Zhang L, Liu Y. Potential interventions for novel coronavirus in China: a systematic review. J Med Virol. 2020;92(5):479–490.
  • Perlman S, Netland J. Coronaviruses post-SARS: update on replication and pathogenesis. Nature Rev Microbiol. 2009;7(6):439–450.
  • Hamre D, Procknow JJ. A new virus isolated from the human respiratory tract. Pro Soc Exp Biol Med. 1966;121(1):190–193.
  • Almeida JD, Tyrrell DAJ. The morphology of three previously uncharacterized human respiratory viruses that grow in organ culture. J Gen Virol. 1967;1(2):175–178.
  • van der Hoek L. Human coronaviruses: what do they cause? Antivir Ther. 2007;12(4):651–658.
  • Li W, Shi Z, Yu M, et al. Bats are natural reservoirs of SARS-like coronaviruses. Science. 2005;310(5748):676.
  • van der Hoek L, Pyrc K, Jebbink MF, et al. Identification of a new human coronavirus. Nat Med. 2004;10(4):368–373.
  • Woo PCY, Lau SKP, and Chu CM, et al. Characterization and complete genome sequence of a novel coronavirus, Coronavirus HKU1 from patients with pneumonia. Journal of Virology. 2005;79(2):884.
  • de Groot RJ, Baker SC, Baric RS, et al. Middle East respiratory syndrome coronavirus (MERS-CoV): announcement of the Coronavirus study group. J Virol. 2013;87(14):7790–7792.
  • Chafekar A, Fielding BC. MERS-CoV: understanding the latest human coronavirus threat. Viruses. 2018;10(2):93.
  • Cui J, Li F, Shi Z-L. Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol. 2019;17(3):181–192.
  • Mohsenzadegan M, Saebi F, Yazdani M, et al. Autoantibody against new gene expressed in prostate protein is traceable in prostate cancer patients. Biomark Med. 2018;12(10):1125–1138.
  • Pizza M, Scarlato V, Masignani V, et al. Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science. 2000;287(5459):1816–1820.
  • Petrovsky N, Aguilar JC. Vaccine adjuvants: current state and future trends. Immunol Cell Biol. 2004;82(5):488–496.
  • Thompson AL, Staats HF. Cytokines: the future of intranasal vaccine adjuvants. Clin Dev Immunol. 2011, 2011;289597.
  • Sesardic D. Synthetic peptide vaccines. J Med Microbiol. 1993;39(4):241–242.
  • Fujita Y, Taguchi H. Current status of multiple antigen-presenting peptide vaccine systems: application of organic and inorganic nanoparticles. Chem Cent J. 2011;5(1):48.
  • Bijker MS, Melief CJM, Offringa R, et al. Design and development of synthetic peptide vaccines: past, present and future. Expert Rev Vaccines. 2007;6(4):591–603.
  • Lin SY-H, Cheng C-W, Su EC-Y. Prediction of B-cell epitopes using evolutionary information and propensity scales. BMC Bioinformatics. 2013;14(2):S10.
  • Toussaint NC, Maman Y, Kohlbacher O, et al. Universal peptide vaccines – optimal peptide vaccine design based on viral sequence conservation. Vaccine. 2011;29(47):8745–8753.
  • Purcell AW, McCluskey J, Rossjohn J. More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov. 2007;6(5):404–414.
  • Heegaard PMH, Dedieu L, Johnson N, et al. Adjuvants and delivery systems in veterinary vaccinology: current state and future developments. Arch Virol. 2011;156(2):183–202.
  • Schutze MP, Leclerc C, Jolivet M, et al. Carrier-induced epitopic suppression, a major issue for future synthetic vaccines. J Immunol. 1985;135(4):2319.
  • Di John D, Torres J, Murillo J, et al. Effect of priming with carrier on response to conjugate vaccine. Lancet. 1989;334(8677):1415–1418.
  • Herzenberg LA, Tokuhisa T, Herzenberg LA. Carrier-priming leads to hapten-specific suppression. Nature. 1980;285(5767):664–667.
  • Carvalho TF, Haddad JPA, Paixão TA, et al. Meta-analysis and advancement of Brucellosis vaccinology. PLOS ONE. 2016;11(11): e0166582.
  • Gardy JL, Lynn DJ, Brinkman FSL, et al. Enabling a systems biology approach to immunology: focus on innate immunity. Trends Immunol. 2009;30(6):249–262.
  • Tomar N, De RK. Immunoinformatics: an integrated scenario. Immunology. 2010;131(2):153–168.
  • Brusic V, Petrovsky N. Immunoinformatics and its relevance to understanding human immune disease. Expert Rev Clin Immunol. 2005;1(1):145–157.
  • Kazi A, Chuah C, Majeed ABA, et al. Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design. Pathog Glob Health. 2018;112(3):123–131.
  • Davies MN, Flower DR. Harnessing bioinformatics to discover new vaccines. Drug Discov Today. 2007;12(9):389–395.
  • De Groot AS, Berzofsky JA. From genome to vaccine — new immunoinformatics tools for vaccine design. Methods. 2004;4(34):425–428.
  • Hemmati M, Raoufi E, Fallahi H. Predicting candidate epitopes on ebola virus for possible vaccine development. In: S.I O, editor. Advances in Ebola control. London, UK: InTechOpen; 2017. p. 98.
  • Raoufi E, Hemmati M, Eftekhari S, et al. Epitope prediction by novel immunoinformatics approach: a state-of-the-art review. Int J Pept Res Ther. 2020;26(2):1155–1163.
  • Backert L, Kohlbacher O. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Med. 2015;7(1):119.
  • Mahapatra SR, Sahoo S, Dehury B, et al. Designing an efficient multi-epitope vaccine displaying interactions with diverse HLA molecules for an efficient humoral and cellular immune response to prevent COVID-19 infection. Expert Rev Vaccines. 2020;19(9):871–885.
  • Dey J, Mahapatra SR, Singh P, et al. B and T cell epitope-based peptides predicted from clumping factor protein of Staphylococcus aureus as vaccine targets. Microb Pathog. 2021;160:105171.
  • Mahapatra SR, Dey J, Kaur T, et al. Immunoinformatics and molecular docking studies reveal a novel multi-epitope peptide vaccine against pneumonia infection. Vaccine. 2021;39(42):6221–6237.
  • Chatterjee R, Sahoo P, Mahapatra SR, et al. Development of a conserved chimeric vaccine for induction of strong immune response against Staphylococcus aureus using immunoinformatics approaches. Vaccines (Basel). 2021;9(9):1038 .
  • Mahapatra SR, Dey J, Kushwaha GS, et al. Immunoinformatic approach employing modeling and simulation to design a novel vaccine construct targeting MDR efflux pumps to confer wide protection against typhoidal Salmonella serovars. J Biomol Struct Dyn. 2021; Online ahead of print .
  • Dey J, Mahapatra SR, Lata S, et al. Exploring Klebsiella pneumoniae capsule polysaccharide proteins to design multiepitope subunit vaccine to fight against pneumonia. Expert Rev Vaccines. 2022;21(4):569–587.
  • Korber B, Fischer WM, Gnanakaran S, et al. Tracking changes in SARS-CoV-2 spike: evidence that D614G increases infectivity of the COVID-19 virus. Cell. 2020;182(4):812–827.
  • Galloway SE, Paul P, MacCannell DR, et al. Emergence of SARS-CoV-2 B.1.1.7 lineage - united states, december 29, 2020-january 12, 2021. Morbidity and mortality weekly report (MMWR). 2021;70(3): 95–99
  • Hoffmann M, Kleine-Weber H, Pöhlmann S. A multibasic cleavage site in the spike protein of SARS-CoV-2 is essential for infection of human lung cells. Mol Cell. 2020;78(4):779–784.
  • Kemp SA, Collier DA, Datir RP, et al. SARS-CoV-2 evolution during treatment of chronic infection. Nature. 2021;592(7853):277–282.
  • Sapkal GN, Yadav PD, Ella R, et al. Neutralization of UK-variant VUI-202012/01 with COVAXIN vaccinated human serum. bioRxiv. 2021; Preprint .
  • Greaney AJ, Loes AN, Crawford KHD, et al. Comprehensive mapping of mutations in the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human plasma antibodies. Cell Host Microbe. 2021;29(3):463–476.e6.
  • Bian L, Gao F, Zhang J, et al. Effects of SARS-CoV-2 variants on vaccine efficacy and response strategies. Expert Rev Vaccines. 2021;20(4):365–373.
  • Chen Y, Shen H, Huang R, et al. Serum neutralising activity against SARS-CoV-2 variants elicited by CoronaVac. Lancet Infect Dis. 2021;21(8):1071–1072.
  • Maggi F, Novazzi F, Genoni A, et al. Imported SARS-CoV-2 variant P.1 in traveler returning from Brazil to Italy. Emerg Infect Dis. 2021;27(4):1249–1251.
  • Wang Z, Schmidt F, Weisblum Y, et al. mRNA vaccine-elicited antibodies to SARS-CoV-2 and circulating variants. Nature. 2021;592(7855):616–622.
  • McCallum M, Bassi J, Marco AD SARS-CoV-2 immune evasion by the B.1.427/B.1.429 variant of concern , et al. Science. 2021;373(6555):648–654 .
  • Johnson BA, Xie X, Kalveram B Furin cleavage site is key to SARS-CoV-2 pathogenesis , et al. bioRxiv. 2020; Preprint.
  • Cao Y, Wang J, Jian F, et al. Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies. Nature. 2022;602(7898):657–663.
  • McCallum M, Czudnochowski N, Rosen Laura E, et al. Structural basis of SARS-CoV-2 Omicron immune evasion and receptor engagement. Science. 2022;375(6583):864–868.
  • Peacock TP, Goldhill DH, Zhou J, et al. The furin cleavage site in the SARS-CoV-2 spike protein is required for transmission in ferrets. Nat Microbiol. 2021;6(7):899–909.
  • Elbe S, Buckland-Merrett G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Global Challenges. 2017;1(1):33–46.
  • Terry FE, Moise L, Martin RF, et al. Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases. Expert Rev Vaccines. 2015;14(1):21–35.
  • Agüero F, Al-Lazikani B, Aslett M, et al. Genomic-scale prioritization of drug targets: the TDR targets database. Nat Rev Drug Discov. 2008;7(11):900–907.
  • Doyle MA, Gasser RB, Woodcroft BJ, et al. Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes. BMC Genomics. 2010;11(1):222.
  • Spohn G, Bachmann MF. Exploiting viral properties for the rational design of modern vaccines. Expert Rev Vaccines. 2008;7(1):43–54.
  • Bachmann MF, Dyer MR. Therapeutic vaccination for chronic diseases: a new class of drugs in sight. Nat Rev Drug Discov. 2004;3(1):81–88.
  • Bosch Berend J, van der Zee R, de Haan Cornelis AM, et al. The coronavirus spike protein is a class I virus fusion protein: structural and functional characterization of the fusion core complex. J Virol. 2003;77(16):8801–8811.
  • Zhang J, Zeng H, Gu J, et al. Progress and prospects on vaccine development against SARS-CoV-2. Vaccines (Basel). 2020;8(2):153.
  • Jiang S, He Y, Liu S. SARS vaccine development. Emerg Infect Dis. 2005;11(7):1016–1020.
  • Ong E, Wang H, Wong MU, et al. Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics. 2020;36(10):3185–3191.
  • Nieto-Torres JL, DeDiego ML, Verdiá-Báguena C, et al. Severe acute respiratory syndrome coronavirus envelope protein ion channel activity promotes virus fitness and pathogenesis. PLoS Pathog. 2014;10(5):e1004077.
  • Neuman BW, Kiss G, Kunding AH, et al. A structural analysis of M protein in coronavirus assembly and morphology. J Struct Biol. 2011;174(1):11–22.
  • Bhatnager R, Bhasin M, Arora J, et al. Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach. J Biomol Struct Dyn. 2021;39(15):5690–5705.
  • Singh JS, Qureshi IA. Multi-epitope vaccine against SARS-CoV-2 applying immunoinformatics and molecular dynamics simulation approaches. J Biomol Struct Dyn. 2022;40(7):2917–2933.
  • Rahman MS, Hoque MN, Islam MR, et al. Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach. PeerJ. 2020;8:e9572.
  • Rehman HM, Mirza MU, Saleem M, et al. A putative prophylactic solution for COVID-19: development of novel multiepitope vaccine candidate against SARS‐COV‐2 by comprehensive immunoinformatic and molecular modelling approach. Biology (Basel). 2020;9(9):296.
  • Safavi A, Kefayat A, Mahdevar E, et al. Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches. Vaccine. 2020;38(48):7612–7628.
  • Ayyagari VS, TC V, et al. Design of a multi-epitope-based vaccine targeting M-protein of SARS-CoV-2: an immunoinformatics approach. J Biomol Struct Dyn. 2022;40(7):2963–2977.
  • DeDiego ML, Nieto-Torres JL, Jiménez-Guardeño JM, et al. Severe acute respiratory syndrome coronavirus envelope protein regulates cell stress response and apoptosis. PLoS Pathog. 2011;7(10):e1002315.
  • Corse E, Machamer CE. The cytoplasmic tails of infectious bronchitis virus E and M proteins mediate their interaction. Virology. 2003;312(1):25–34.
  • Hogue BG, Machamer CE Coronavirus Structural Proteins and Virus Assembly . . Nidoviruses (Wiley). 2007;179–200.
  • Ye Y, Hogue Brenda G. Role of the coronavirus E viroporin protein transmembrane domain in virus assembly. J Virol. 2007;81(7):3597–3607.
  • Ruch Travis R, Machamer Carolyn E. The hydrophobic domain of infectious bronchitis virus E protein alters the host secretory pathway and is important for release of infectious virus. J Virol. 2011;85(2):675–685.
  • Jakhar R, Kaushik S, Gakhar SK. 3CL hydrolase-based multiepitope peptide vaccine against SARS-CoV-2 using immunoinformatics. J Med Virol. 2020;92(10):2114–2123.
  • Chaudhuri A. Comparative analysis of non structural protein 1 of SARS-CoV2 with SARS-CoV1 and MERS-CoV: An in silico study. J Mol Struct . 2021;1243:130854.
  • Joshi A, Joshi BC, M.a.-u M, et al. Epitope based vaccine prediction for SARS-COV-2 by deploying immuno-informatics approach. Inf Med Unlocked. 2020;19: 100338.
  • Enayatkhani M, Hasaniazad M, Faezi S, et al. Reverse vaccinology approach to design a novel multi-epitope vaccine candidate against COVID-19: an in silico study. J Biomol Struct Dyn. 2021;39(8):2857–2872.
  • Gonzalez-Galarza FF, McCabe A, Eduardo J M.d S, et al. Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools. Nucleic Acids Res. 2020;48(1):D783–D788.
  • London N, Movshovitz-Attias D, Schueler-Furman O. The structural basis of peptide-protein binding strategies. Structure. 2010;18(2):188–199.
  • Benson DA, Cavanaugh M, Clark K, et al. GenBank. Nucleic Acids Res. 2017;45(D1:D37–D42.
  • Shu Y, McCauley J. GISAID: global initiative on sharing all influenza data - from vision to reality. Euro surveillance: European communicable disease bulletin. 2017;22(13):30494.
  • Hunter S, Apweiler R, Attwood TK, et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 2009;37( Database issue):D211–D215.
  • Mulder NJ, Apweiler R, Attwood TK, et al. New developments in the InterPro database. Nucleic Acids Res. 2007;35:D224–D228.
  • Apweiler R, Attwood TK, Bairoch A, et al. The InterPro database, an integrated documentation resource for protein families, domains and functional sites. Nucleic Acids Res. 2001;29(1):37–40.
  • Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000;28(1):235–242.
  • Finn RD, Bateman A, Clements J, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42( Database issue):D222–D230.
  • Bairoch A, Apweiler R, Wu CH, et al. The universal protein resource (uniprot). Nucleic Acids Res. 2005;33(1):D154–D159.
  • Pickett BE, Sadat EL, Zhang Y, et al. ViPR: an open bioinformatics database and analysis resource for virology research. Nucleic Acids Res. 2012;40( Database issue):D593–D598.
  • Stano M, Beke G, Klucar L. viruSITE-Integrated database for viral genomics. Database. 2016:2016:baw162.
  • Patiyal S, Kaur D, Kaur H, et al. A web-based platform on Coronavirus Disease-19 to maintain predicted diagnostic, drug. and vaccine candidates. Monoclonal Antibodies in Immunodiagnosis and Immunotherapy. 2020;39(6):204–216.
  • Zhu Z, Meng K, Liu G, et al. A database resource and online analysis tools for coronaviruses on a historical and global scale. Database. 2020;2020:70.
  • Ahmed SF, Quadeer AA, McKay MR. COVIDep: a web-based platform for real-time reporting of vaccine target recommendations for SARS-CoV-2. Nat Protoc. 2020;15(7):2141–2142.
  • Wu J, Chen W, Zhou J, et al. COVIEdb: a database for potential immune epitopes of coronaviruses. Front Pharmacol. 2020;11:572249.
  • Gowthaman R, Guest JD, Yin R, et al. CoV3D: a database of high resolution coronavirus protein structures. Nucleic Acids Res. 2021;49(D1:D282–D287.
  • Sahoo S, Mahapatra SR, Parida BK, et al. DBCOVP: a database of coronavirus virulent glycoproteins. Comput Biol Med. 2021;129:104131.
  • Fleri W, Salimi N, Vita R, et al. Immune Epitope Database and analysis resource. In: Ratcliffe MJH, editors. Encyclopedia of immunobiology. Academic Press: Oxford; 2016. p. 220–224.
  • Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8(1):4.
  • Gasteiger E, Hoogland C, Gattiker A, et al. Protein Identification and analysis tools on the expasy server. In: Walker JMeditors. The proteomics protocols handbook. Totowa NJ: Humana Press; 2005. p. 571–607.
  • Dimitrov I, Bangov I, Flower DR, et al. AllerTOP v.2–A server for in silico prediction of allergens. J Mol Model. 2014;20(6):2278.
  • Brusic V, Rudy G, Harrison LC. MHCPEP, a database of MHC-binding peptides. Nucleic Acids Res. 1998;26(1):368–371.
  • Rammensee HG, Bachmann J, Emmerich NPN, et al. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 1999;50(3–4):213–219.
  • Berzofsky JA, Ahlers JD, Belyakov IM. Strategies for designing and optimizing new generation vaccines. Nat Rev Immunol. 2001;1(3):209–219.
  • Brusic V, Bajic VB, Petrovsky N. Computational methods for prediction of T-cell epitopes— a framework for modelling, testing, and applications. Methods. 2004;34(4):436–443.
  • Larsen MV, Lundegaard C, Lamberth K, et al. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics. 2007;8:424.
  • Nielsen M, Lundegaard C, Blicher T, et al. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLOS ONE. 2007e796–e796; 28
  • Andreatta M, Karosiene E, Rasmussen M, et al. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics. 2015;67(11–12):641–650.
  • Dhanda SK, Vir P, Raghava GPS. Designing of interferon-gamma inducing MHC Class-II binders. Biol Direct. 2013;8:30.
  • Kringelum JV, Nielsen M, Padkjær SB, et al. Structural analysis of B-cell epitopes in antibody: protein complexes. Mol Immunol. 2013;53(1–2):24–34.
  • Parker JM, Guo D, Hodges RS. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry. 1986;25(19):5425–5432.
  • Jespersen MC, Peters B, Nielsen M, et al. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45(W1:W24–W29.
  • Thornton JM, Edwards MS, Taylor WR, et al. Location of ‘continuous’ antigenic determinants in the protruding regions of proteins. EMBO J. 1986;5(2):409–413.
  • Ponomarenko J, Bui -H-H, Li W, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics. 2008;9:514.
  • Kringelum JV, Lundegaard C, Lund O, et al. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol. 2012;8(12):e1002829.
  • Haste Andersen P, Nielsen M, Lund O. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci. 2006;15(11):2558–2567.
  • Gupta S, Ansari HR, Gautam A, et al. Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biol Direct. 2013;8():27.
  • Bahrami AA, Payandeh Z, Khalili S, et al. Immunoinformatics: in silico approaches and computational design of a multi-epitope, immunogenic protein. Int Rev Immunol. 2019;38(6):307–322.
  • Tahir Ul Qamar M, S S, U.a A, et al. Epitope‐based peptide vaccine design and target site depiction against middle east respiratory syndrome Coronavirus: an immune-informatics study. J Transl Med. 2019;17(1):362.
  • Panda PK, Arul MN, Patel P, et al. Structure-based drug designing and immunoinformatics approach for SARS-CoV-2. Sci Adv. 2020;6(28):eabb8097.
  • Sanami S, Zandi M, Pourhossein B, et al. Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach. Int J Biol Macromol. 2020;164:871–883.
  • Ghorbani A, Zare F, Sazegari S, et al. Development of a novel platform of virus-like particle (VLP)-based vaccine against COVID-19 by exposing epitopes: an immunoinformatics approach. New Microbes New Infect. 2020;38:100786.
  • Rahman N, Ali F, Basharat Z, et al. Vaccine design from the ensemble of surface glycoprotein epitopes of SARS-CoV-2: an immunoinformatics approach. Vaccines (Basel). 2020;8(3):423.
  • Rakib A, Sami SA, Islam MA, et al. Epitope-based immunoinformatics approach on nucleocapsid protein of severe acute respiratory syndrome-Coronavirus-2. Molecules. 2020;25(21):5088.
  • Tilocca B, Soggiu A, Sanguinetti M, et al. Immunoinformatic analysis of the SARS-CoV-2 envelope protein as a strategy to assess cross-protection against COVID-19. Microbes Infect. 2020;22(4):182–187.
  • Cun Y, Li C, Shi L, et al. COVID-19 coronavirus vaccine T cell epitope prediction analysis based on distributions of HLA class I loci (HLA-A, -B, -C) across global populations. Hum Vaccin Immunother. 2021;17(4):1097–1108.
  • Ibrahim HS, Kafi SK. A computational vaccine designing approach for MERS-CoV infections. In: Tomar N, editor. Immunoinformatics. New York NY: Springer US; 2020. p. 39–145.
  • Tahir Ul Qamar M, F S, S A, et al. Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2. Infect Dis Poverty. 2020;9(1):132.
  • Khairkhah N, Aghasadeghi MR, Namvar A, et al. Design of novel multiepitope constructs-based peptide vaccine against the structural S, N and M proteins of human COVID-19 using immunoinformatics analysis. PLOS ONE. 2020;15(10):e0240577.
  • Kumar A, Kumar P, Saumya KU, et al. Exploring the SARS-CoV-2 structural proteins for multi-epitope vaccine development: an in-silico approach. Expert Rev Vaccines. 2020;19(9):887–898.
  • Waqas M, Haider A, Sufyan M, et al. Determine the potential epitope based peptide vaccine against novel SARS-CoV-2 targeting structural proteins using immunoinformatics approaches. Front Mol Biosci. 2020;7:227.
  • Akhand MRN, Azim KF, Hoque SF, et al. Genome based evolutionary lineage of SARS-CoV-2 towards the development of novel chimeric vaccine. Infect Genet Evol. 2020;85:104517.
  • Shi J, Zhang J, Li S, et al. Epitope-based vaccine target screening against highly pathogenic MERS-CoV: an in silico approach applied to emerging infectious diseases. PLOS ONE. 2015;10(12):e0144475.
  • Khan S, Shaker B, Ahmad S, et al. Towards a novel peptide vaccine for middle east respiratory syndrome coronavirus and its possible use against pandemic COVID-19. 2Journal of Molecular Liquids 2020; 114706
  • Basu A, Sarkar A, Maulik U. Strategies for vaccine design for Corona virus using Immunoinformatics techniques. bioRxiv. 2020; Preprint .
  • Srivastava S, Kamthania M, Singh S, et al. Structural basis of development of multi-epitope vaccine against Middle East respiratory syndrome using in silico approach. Infect Drug Resist. 2018;11:2377–2391.
  • Sarkar B, Ullah MA, Johora FT, et al. Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS Coronavirus-2 (SARS-CoV-2). Immunobiology. 2020;225(3):151955.
  • Sahoo B, Kant K, Rai NK, et al. Identification of T-cell epitopes in proteins of novel human coronavirus, SARS-Cov-2 for vaccine development. Int J Appl Biol Pharm. 2020;11(2): 37–45
  • The Uniprot C. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47(D1:D506–D515.
  • Saha S, Raghava GP. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. 2006;34:W202–W209.
  • Maurer-Stroh S, Krutz NL, Kern PS, et al. AllerCatPro-Prediction of protein allergenicity potential from the protein sequence. Bioinformatics. 2019;35(17):3020–3027.
  • Dimitrov I, Naneva L, Doytchinova I, et al. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics. 2014;30(6):846–851.
  • Muh HC, Tong JC, Tammi MT. AllerHunter: a SVM-pairwise system for assessment of allergenicity and allergic cross-reactivity in proteins. PLOS ONE. 2009;4(6):e5861.
  • Samad A, Ahammad F, Nain Z, et al. Designing a multi-epitope vaccine against SARS-CoV-2: an immunoinformatics approach. J Biomol Struct Dyn. 2022; 40(1):14–30.
  • Cheng J, Randall AZ, Sweredoski MJ, et al. SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res. 2005;33(2):W72–W76.
  • Calis JJ, Maybeno M, Greenbaum JA, et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol. 2013;9(10): e1003266.
  • Bui -H-H, Sidney J, Li W, et al. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics. 2007;8(1):361.
  • Chen VB, Arendall WB 3rd, Headd JJ, et al. MolProbity: all-atom structure validation for macromolecular crystallography. Biol Crystallogr. 2010;66(1):12–21.
  • Hebditch M, Carballo-Amador MA, Charonis S, et al. Protein-Sol: a web tool for predicting protein solubility from sequence. Bioinformatics. 2017;33(19):3098–3100.
  • Gupta S, Kapoor P, Chaudhary K, et al. In silico approach for predicting toxicity of peptides and proteins. PLOS ONE. 2013;8(9):e73957.
  • Banerjee A, Santra D, Maiti S. Energetics and IC50 based epitope screening in SARS CoV-2 (COVID 19) spike protein by immunoinformatic analysis implicating for a suitable vaccine development. J Transl Med. 2020;18(1):281.
  • Bhasin M, Raghava GP. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine. 2004;22(23–24):3195–3204.
  • Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks: application to the MHC Class I system. Bioinformatics. 2016;32(4):511–517.
  • Karosiene E, Lundegaard C, Lund O, et al. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics. 2012;64(3):177–186.
  • Zhang H, Lund O, Nielsen M. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics. 2009;25(10):1293–1299.
  • Rasmussen M, Fenoy E, Harndahl M, et al. Pan-specific prediction of peptide-MHC Class I complex stability, a correlate of T cell immunogenicity. J Iimmunol. 2016;197(4):1517–1524.
  • Sidney J, Assarsson E, Moore C, et al. Quantitative peptide binding motifs for 19 human and mouse MHC Class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 2008;4:2.
  • Moutaftsi M, Peters B, Pasquetto V, et al. A consensus epitope prediction approach identifies the breadth of murine TCD8+-cell responses to vaccinia virus. Nat Biotechnol. 2006;24(7):817–819.
  • Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics. 2005;6:132.
  • Kim Y, Sidney J, Pinilla C, et al. Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC Bioinformatics. 2009;10:394.
  • Dai Y, Chen H, Zhuang S, et al. Immunodominant regions prediction of nucleocapsid protein for SARS-CoV-2 early diagnosis: a bioinformatics and immunoinformatics study. Pathog Glob Health. 2020;1–8.
  • Giguère S, Drouin A, Lacoste A, et al. MHC-NP: predicting peptides naturally processed by the MHC. J Immunol Methods. 2013;400-401:30–36.
  • Stranzl T, Larsen MV, Lundegaard C, et al. NetCTLpan: pan-specific MHC Class I pathway epitope predictions. Immunogenetics. 2010;62(6):357–368.
  • Jurtz V, Paul S, Andreatta M, et al. NetMHCpan-4.0: improved peptide-MHC Class I Interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol. 2017;199(9):3360–3368.
  • Singh H, Raghava GPS. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics. 2003;19(8):1009–1014.
  • Larsen M, Lundegaard C, Lamberth K, et al. An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur J Immunol. 2005;35:2295–2303.
  • Dhanda SK, Gupta S, Vir P, et al. Prediction of IL4 Inducing Peptides. Clin Dev Immunol. 2013;2013:263952.
  • Nagpal G, Usmani SS, Dhanda SK, et al. Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci Rep. 2017;7:42851.
  • Lata S, Bhasin M, Raghava GP. Application of machine learning techniques in predicting MHC binders. Methods Mol Biol. 2007;409:201–215.
  • Nielsen M, Andreatta M. NNAlign: a platform to construct and evaluate artificial neural network models of receptor–ligand interactions. Nucleic Acids Res. 2017;45(W1:W344–W349.
  • Sturniolo T, Bono E, Ding J, et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol. 1999;17(6):555–561.
  • Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics. 2007;8(1):238.
  • Wang P, Sidney J, Dow C, et al. A systematic assessment of MHC Class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol. 2008;4(4):e1000048.
  • Jensen KK, Andreatta M, Marcatili P, et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology. 2018;154(3):394–406.
  • Singh H, Raghava GPS. ProPred: prediction of HLA-DR binding sites. Bioinformatics. 2001;17(12):1236–1237.
  • Guan P, Doytchinova IA, Zygouri C, et al. MHCPred: a server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res. 2003;31(13):3621–3624.
  • Reche PA, Glutting JP, Reinherz EL. Prediction of MHC class I binding peptides using profile motifs. Hum Immunol. 2002;63(9):701–709.
  • Paul S, Sidney J, Sette A, et al. Tepitool: a pipeline for computational prediction of T cell epitope candidates. Curr Protoc Immunol. 2016;114:18.19.1–18.19.24.
  • Saha S, Raghava GP. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins. 2006;65(1):40–48.
  • Chou PY, Fasman GD. Prediction of the secondary structure of proteins from their amino acid sequence. Adv Enzymol Relat Areas Mol Biol. 1978;47:45–148.
  • Emini EA, Hughes JV, Perlow DS, et al. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol. 1985;55(3):836–839.
  • Karplus PA, Schulz GE. Prediction of chain flexibility in proteins. Naturwissenschaften. 1985;72(4):212–213.
  • Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990;276(1–2):172–174.
  • El-Manzalawy Y, Dobbs D, Honavar V. Predicting linear B-cell epitopes using string kernels. J Mol Recognit. 2008;21(4):243–255.
  • El-Manzalawy Y, Dobbs D, Honavar V. Predicting flexible length linear B-cell epitopes. Computational Systems Bioinformatics Conference. 2008;7:121–132.
  • de Vries SJ, Bonvin AMJJ. CPORT: a consensus interface predictor and its performance in prediction-driven docking with HADDOCK. PLOS ONE. 2011;6(3):e17695.
  • Dhanda SK, Vaughan K, Schulten V, et al. Development of a novel clustering tool for linear peptide sequences. Immunology. 2018;155(3):331–345.
  • Nielsen M, Lundegaard C, Lund O, et al. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics. 2005;57(1–2):33–41.

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