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Vaccines Targeting Numerous Coronavirus Antigens, Ensuring Broader Global Population Coverage: Multi-epitope and Multi-patch Vaccines

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Vaccine Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2410))

Abstract

Coronaviruses are causative agents of different zoonosis including SARS, MERS, or COVID-19 in humans. The high transmission rate of coronaviruses, the time-consuming development of efficient anti-infectives and vaccines, the possible evolutionary adaptation of the virus to conventional vaccines, and the challenge to cover broad human population worldwide are the major reasons that made it challenging to avoid coronaviruses outbreaks. Although, a plethora of different approaches are being followed to design and develop vaccines against coronaviruses, most of them target subunits, full-length single, or only a very limited number of proteins. Vaccine targeting multiple proteins or even the entire proteome of the coronavirus is yet to come. In the present chapter, we will be discussing multi-epitope vaccine (MEV) and multi-patch vaccine (MPV) approaches to design and develop efficient and sustainably successful strategies against coronaviruses. MEV and MPV utilize highly conserved, potentially immunogenic epitopes and antigenic patches, respectively, and hence they have the potential to target large number of coronavirus proteins or even its entire proteome, allowing us to combat the challenge of its evolutionary adaptation. In addition, the large number of human leukocyte antigen (HLA) alleles targeted by the chosen specific epitopes enables MEV and MPV to cover broader global population.

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Declaration: Patents filed: IN202011037585, IN202011037939, PCT/IN2021/050841.References

  1. Ye Z-W, Yuan S, Yuen K-S et al (2020) Zoonotic origins of human coronaviruses. Int J Biol Sci 16:1686–1697. https://doi.org/10.7150/ijbs.45472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ksiazek TG, Erdman D, Goldsmith CS et al (2003) A novel coronavirus associated with severe acute respiratory syndrome. N Engl J Med 348:1953–1966. https://doi.org/10.1056/nejmoa030781

    Article  CAS  PubMed  Google Scholar 

  3. Kim J II, Kim YJ, Lemey P et al (2016) The recent ancestry of Middle East respiratory syndrome coronavirus in Korea has been shaped by recombination. Sci Rep 6:18825. https://doi.org/10.1038/srep18825

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese center for disease control and prevention. JAMA 323:1239–1242. https://doi.org/10.1001/jama.2020.2648

    Article  CAS  PubMed  Google Scholar 

  5. Chen N, Zhou M, Dong X et al (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395:507–513. https://doi.org/10.1016/S0140-6736(20)30211-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. He WQ, Chen SB, Liu XQ et al (2003) Death risk factors of severe acute respiratory syndrome with acute respiratory distress syndrome. Zhongguo Wei Zhong Bing Ji Jiu Yi Xue 15:336–337

    PubMed  Google Scholar 

  7. Zhong NS, Zheng BJ, Li YM et al (2003) Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People’s Republic of China, in February, 2003. Lancet 362:1353–1358. https://doi.org/10.1016/S0140-6736(03)14630-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Leung TF, Wong GWK, Hon KLE, Fok TF (2003) Severe acute respiratory syndrome (SARS) in children: epidemiology, presentation and management. Paediatr Respir Rev 4:334–339. https://doi.org/10.1016/S1526-0542(03)00088-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cotten M, Watson SJ, Kellam P et al (2013) Transmission and evolution of the Middle East respiratory syndrome coronavirus in Saudi Arabia: a descriptive genomic study. Lancet 382:1993–2002. https://doi.org/10.1016/S0140-6736(13)61887-5

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ki M (2015) 2015 MERS outbreak in Korea: hospital-to-hospital transmission. Epidemiol Health 37:e2015033. https://doi.org/10.4178/epih/e2015033

    Article  PubMed  PubMed Central  Google Scholar 

  11. WHO (n.d.) Coronavirus disease 2019 (COVID-19). Situation report. WHO, Geneva

    Google Scholar 

  12. Zhou P, Yang XL, Wang XG et al (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579:270–273. https://doi.org/10.1038/s41586-020-2012-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lu R, Zhao X, Li J et al (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395:565–574. https://doi.org/10.1016/S0140-6736(20)30251-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhu H, Wei L, Niu P (2020) The novel coronavirus outbreak in Wuhan, China. Glob Heal Res Policy 5:6. https://doi.org/10.1186/s41256-020-00135-6

    Article  Google Scholar 

  15. Dong Y, Dai T, Wei Y et al (2020) A systematic review of SARS-CoV-2 vaccine candidates. Signal Transduct Target Ther 5:237. https://doi.org/10.1038/s41392-020-00352-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Jin Z, Du X, Xu Y et al (2020) Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature 582:289–293. https://doi.org/10.1038/s41586-020-2223-y

    Article  CAS  PubMed  Google Scholar 

  17. Wrapp D, Wang N, Corbett KS et al (2020) Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science (80- ) 367:1260–1263. https://doi.org/10.1126/science.abb2507

    Article  CAS  Google Scholar 

  18. Yin W, Mao C, Luan X et al (2020) Structural basis for inhibition of the RNA-dependent RNA polymerase from SARS-CoV-2 by remdesivir. Science (80- ) 368:1499–1504. https://doi.org/10.1126/science.abc1560

    Article  CAS  Google Scholar 

  19. Gordon DE, Jang GM, Bouhaddou M et al (2020) A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583:459–468. https://doi.org/10.1038/s41586-020-2286-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Prajapat M, Sarma P, Shekhar N et al (2020) Drug for corona virus: a systematic review. Indian J Pharmacol 52:56. https://doi.org/10.4103/ijp.IJP_115_20

    Article  PubMed  PubMed Central  Google Scholar 

  21. Li F, Li W, Farzan M, Harrison SC (2005) Structure of SARS coronavirus spike receptor-binding domain complexed with receptor. Science 309:1864–1868. https://doi.org/10.1126/science.1116480

    Article  CAS  PubMed  Google Scholar 

  22. Hughes CE, Benson RA, Bedaj M, Maffia P (2016) Antigen-presenting cells and antigen presentation in tertiary lymphoid organs. Front Immunol 7:481. https://doi.org/10.3389/fimmu.2016.00481

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Antoniou AN, Powis SJ, Elliott T (2003) Assembly and export of MHC class I peptide ligands. Curr Opin Immunol 15:75–81. https://doi.org/10.1016/s0952-7915(02)00010-9

    Article  CAS  PubMed  Google Scholar 

  24. Gillim-Ross L, Subbarao K (2006) Emerging respiratory viruses: challenges and vaccine strategies. Clin Microbiol Rev 19:614–636. https://doi.org/10.1128/CMR.00005-06

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Li Y-D, Chi W-Y, Su J-H et al (2020) Coronavirus vaccine development: from SARS and MERS to COVID-19. J Biomed Sci 27:104. https://doi.org/10.1186/s12929-020-00695-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. WHO (n.d.) List of candidate vaccines developed against SARS-CoV. WHO, Geneva

    Google Scholar 

  27. WHO (n.d.) List of candidate vaccines developed against MERS-CoV. WHO, Geneva

    Google Scholar 

  28. WHO (n.d.) Draft landscape and tracker of COVID-19 candidate vaccines. WHO, Geneva

    Google Scholar 

  29. CDC (n.d.) COVID-19 vaccine Emergency Use Authorization (EUA) fact sheets for recipients and caregivers. CDC, Atlanta, GA. https://www.cdc.gov/vaccines/covid-19/eua/index.html

  30. FDA (n.d.) Fact sheet for recipients and caregivers, Emergency Use Authorization (EUA) of the pfizer-biontech covid-19 vaccine to prevent coronavirus disease 2019 (Covid-19), in individuals 16 years of age and older. FDA, Silver Spring, MD. https://www.fda.gov/media/144414

  31. FDA (n.d.) Fact sheet for recipients and caregivers, Emergency Use Authorization (EUA) of the moderna covid-19 vaccine to prevent coronavirus disease 2019 (Covid-19), in individuals 18 years of age and older. FDA, Silver Spring, MD. https://www.modernatx.com/covid19vaccine-eua/eua-fact-sheet-recipients.pdf

  32. CDC (n.d.) COVID-19 AstraZeneca vaccine EUA fact sheet for recipients. CDC, Atlanta, GA. https://www.cdc.gov/vaccines/covid-19/eua/astrazeneca.html

  33. FDA (n.d.) Fact sheet for recipients and caregivers, Emergency Use Authorization (EUA) of the janssen covid-19 vaccine to prevent coronavirus disease 2019 (Covid-19), in individuals 18 years of age and older. FDA, Silver Spring, MD

    Google Scholar 

  34. WHO (n.d.) Lists two additional COVID-19 vaccines for emergency use and COVAX roll-out. WHO, Geneva

    Google Scholar 

  35. WHO (n.d.) Recommendation AstraZeneca/SKBio - COVID-19 vaccine (ChAdOx1-S [recombinant]). WHO, Geneva. https://extranet.who.int/pqweb/vaccines/covid-19-vaccine-chadox1-s-recombinant

  36. WHO (n.d.) Recommendation Serum Institute of India Pvt Ltd - COVID-19 vaccine (ChAdOx1-S [recombinant]) - COVISHIELD. WHO, Geneva. https://extranet.who.int/pqweb/vaccines/covid-19-vaccine-chadox1-s-recombinant-covishield

  37. WHO (n.d.) Issues its first emergency use validation for a COVID-19 vaccine and emphasizes need for equitable global access. WHO, Geneva. https://www.who.int/news/item/31-12-2020-who-issues-its-first-emergency-use-validation-for-a-covid-19-vaccine-and-emphasizes-need-for-equitable-global-access

  38. Srivastava S, Kamthania M, Kumar Pandey R et al (2019) Design of novel multi-epitope vaccines against severe acute respiratory syndrome validated through multistage molecular interaction and dynamics. J Biomol Struct Dyn 37:4345–4360. https://doi.org/10.1080/07391102.2018.1548977

    Article  CAS  PubMed  Google Scholar 

  39. Srivastava S, Kamthania M, Singh S et al (2018) Structural basis of development of multi-epitope vaccine against Middle East respiratory syndrome using in silico approach. Infect Drug Resist 11:2377–2391. https://doi.org/10.2147/IDR.S175114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Srivastava S, Verma S, Kamthania M et al (2020) Structural basis for designing multiepitope vaccines against COVID-19 infection: in silico vaccine design and validation. JMIR Bioinforma Biotechnol 1:e19371. https://doi.org/10.2196/19371

    Article  Google Scholar 

  41. Srivastava S (2020) A novel method for designing multi-patch vaccine and diagnostic kit. Patent No. 202011037585

    Google Scholar 

  42. Srivastava S (2020) A novel multi-patch vaccine design to combat SARS-CoV-2 and a method to prepare thereof. Patent No. 202011037939

    Google Scholar 

  43. Srivastava S, Verma S, Kamthania M et al (2020) Computationally validated SARS-CoV-2 CTL and HTL multi-patch vaccines, designed by reverse epitomics approach, show potential to cover large ethnically distributed human population worldwide. J Biomol Struct Dyn:1–20. https://doi.org/10.1080/07391102.2020.1838329

  44. Wolicki J (2019) Principles of vaccination. In: Pink book webinar series

    Google Scholar 

  45. Haijema BJ, Volders H, Rottier PJM (2004) Live, attenuated coronavirus vaccines through the directed deletion of group-specific genes provide protection against feline infectious peritonitis. J Virol 78:3863–3871. https://doi.org/10.1128/jvi.78.8.3863-3871.2004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen W-H, Hotez PJ, Bottazzi ME (2020) Potential for developing a SARS-CoV receptor-binding domain (RBD) recombinant protein as a heterologous human vaccine against coronavirus infectious disease (COVID)-19. Hum Vaccin Immunother 16:1239–1242. https://doi.org/10.1080/21645515.2020.1740560

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Keech C, Albert G, Cho I et al (2020) Phase 1-2 trial of a SARS-CoV-2 recombinant spike protein nanoparticle vaccine. N Engl J Med 383:2320–2332. https://doi.org/10.1056/NEJMoa2026920

    Article  CAS  PubMed  Google Scholar 

  48. Yu J, Tostanoski LH, Peter L et al (2020) DNA vaccine protection against SARS-CoV-2 in rhesus macaques. Science 369:806–811. https://doi.org/10.1126/science.abc6284

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Smith TRF, Patel A, Ramos S et al (2020) Immunogenicity of a DNA vaccine candidate for COVID-19. Nat Commun 11:2601. https://doi.org/10.1038/s41467-020-16505-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Corbett KS, Edwards DK, Leist SR et al (2020) SARS-CoV-2 mRNA vaccine design enabled by prototype pathogen preparedness. Nature 586:567–571. https://doi.org/10.1038/s41586-020-2622-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhang N-N, Li X-F, Deng Y-Q et al (2020) A Thermostable mRNA Vaccine against COVID-19. Cell 182:1271–1283.e16. https://doi.org/10.1016/j.cell.2020.07.024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Abdelmageed MI, Abdelmoneim AH, Mustafa MI et al (2020) Design of a multiepitope-based peptide vaccine against the E protein of human COVID-19: an immunoinformatics approach. Biomed Res Int 2020:1–12. https://doi.org/10.1155/2020/2683286

    Article  CAS  Google Scholar 

  53. Yarmarkovich M, Warrington JM, Farrel A, Maris JM (2020) Identification of SARS-CoV-2 vaccine epitopes predicted to induce long-term population-scale immunity. Cell Rep Med 1:100036. https://doi.org/10.1016/j.xcrm.2020.100036

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Grifoni A, Sidney J, Zhang Y et al (2020) A sequence homology and bioinformatic approach can predict candidate targets for immune responses to SARS-CoV-2. Cell Host Microbe 27:671–680.e2. https://doi.org/10.1016/j.chom.2020.03.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Grifoni A, Sidney J, Zhang Y et al (2020) Candidate targets for immune responses to 2019-novel coronavirus (nCoV): sequence homology- and bioinformatic-based predictions. SSRN:3541361. https://doi.org/10.2139/ssrn.3541361

  56. Abdelmageed MI, Abdelmoneim AH, Mustafa MI et al (2020) Design of a multiepitope-based peptide vaccine against the E protein of human COVID-19: an immunoinformatics approach. Biomed Res Int 2020:2683286. https://doi.org/10.1155/2020/2683286

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Singh A, Thakur M, Sharma LK, Chandra K (2020) Designing a multi-epitope peptide based vaccine against SARS-CoV-2. Sci Rep 10:16219. https://doi.org/10.1038/s41598-020-73371-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Liu G, Carter B, Bricken T et al (2020) Computationally optimized SARS-CoV-2 MHC class I and II vaccine formulations predicted to target human haplotype distributions. Cell Syst 11:131–144.e6. https://doi.org/10.1016/j.cels.2020.06.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Bhattacharya M, Sharma AR, Patra P et al (2020) Development of epitope-based peptide vaccine against novel coronavirus 2019 (SARS-COV-2): immunoinformatics approach. J Med Virol 92:618–631. https://doi.org/10.1002/jmv.25736

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Fast E, Altman RB, Chen B (2020) Potential T-cell and B-cell epitopes of 2019-nCoV. BioRxiv:955484

    Google Scholar 

  61. Gupta E, Mishra RK, Niraj RRK (2020) Identification of potential vaccine candidates against SARS-CoV-2, a step forward to fight novel coronavirus 2019-nCoV: a reverse vaccinology approach. BioRxiv:039198

    Google Scholar 

  62. Wang Q, Zhang L, Kuwahara K et al (2016) Immunodominant SARS coronavirus epitopes in humans elicited both enhancing and neutralizing effects on infection in non-human primates. ACS Infect Dis 2:361–376. https://doi.org/10.1021/acsinfecdis.6b00006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Duits LA, Nibbering PH, van Strijen E et al (2003) Rhinovirus increases human beta-defensin-2 and -3 mRNA expression in cultured bronchial epithelial cells. FEMS Immunol Med Microbiol 38:59–64. https://doi.org/10.1016/S0928-8244(03)00106-8

    Article  CAS  PubMed  Google Scholar 

  64. Yang D, Biragyn A, Kwak LW, Oppenheim JJ (2002) Mammalian defensins in immunity: more than just microbicidal. Trends Immunol 23:291–296. https://doi.org/10.1016/s1471-4906(02)02246-9

    Article  CAS  PubMed  Google Scholar 

  65. Kohlgraf KG, Pingel LC, Dietrich DE, Brogden KA (2010) Defensins as anti-inflammatory compounds and mucosal adjuvants. Future Microbiol 5:99–113. https://doi.org/10.2217/fmb.09.104

    Article  CAS  PubMed  Google Scholar 

  66. MacDonald AJ, Cao L, He Y et al (2005) rOv-ASP-1, a recombinant secreted protein of the helminth Onchocercavolvulus, is a potent adjuvant for inducing antibodies to ovalbumin, HIV-1 polypeptide and SARS-CoV peptide antigens. Vaccines 23:3446–3452. https://doi.org/10.1016/j.vaccine.2005.01.098

    Article  CAS  Google Scholar 

  67. Guo J, Yang Y, Xiao W et al (2015) A truncated fragment of Ov-ASP-1 consisting of the core pathogenesis-related-1 (PR-1) domain maintains adjuvanticity as the full-length protein. Vaccines 33:1974–1980. https://doi.org/10.1016/j.vaccine.2015.02.053

    Article  CAS  Google Scholar 

  68. He Y, Barker SJ, MacDonald AJ et al (2009) Recombinant Ov-ASP-1, a Th1-biased protein adjuvant derived from the helminth Onchocerca volvulus, can directly bind and activate antigen-presenting cells. J Immunol 182:4005–4016. https://doi.org/10.4049/jimmunol.0800531

    Article  CAS  PubMed  Google Scholar 

  69. Sasaki S, Amara RR, Yeow W-S et al (2002) Regulation of DNA-raised immune responses by cotransfected interferon regulatory factors. J Virol 76:6652–6659. https://doi.org/10.1128/jvi.76.13.6652-6659.2002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Wan C, Yi L, Yang Z et al (2010) The Toll-like receptor adaptor molecule TRIF enhances DNA vaccination against classical swine fever. Vet Immunol Immunopathol 137:47–53. https://doi.org/10.1016/j.vetimm.2010.04.008

    Article  CAS  PubMed  Google Scholar 

  71. Zhang J, Zeng H, Gu J et al (2020) Progress and prospects on vaccine development against SARS-CoV-2. Vaccines 8:153. https://doi.org/10.3390/vaccines8020153

    Article  CAS  PubMed Central  Google Scholar 

  72. Honda-Okubo Y, Barnard D, Ong CH et al (2015) Severe acute respiratory syndrome-associated coronavirus vaccines formulated with delta inulin adjuvants provide enhanced protection while ameliorating lung eosinophilic immunopathology. J Virol 89:2995–3007. https://doi.org/10.1128/JVI.02980-14

    Article  CAS  PubMed  Google Scholar 

  73. Hu W, Li F, Yang X et al (2004) A flexible peptide linker enhances the immunoreactivity of two copies HBsAg preS1 (21-47) fusion protein. J Biotechnol 107:83–90. https://doi.org/10.1016/j.jbiotec.2003.09.009

    Article  CAS  PubMed  Google Scholar 

  74. Chen X, Zaro JL, Shen W-C (2013) Fusion protein linkers: property, design and functionality. Adv Drug Deliv Rev 65:1357–1369. https://doi.org/10.1016/j.addr.2012.09.039

    Article  CAS  PubMed  Google Scholar 

  75. Gerbig DG Jr, Fenk CJ, Goodhart AS (2000) The dot blot ELISA: a rapid & simple experiment to demonstrate antibody-antigen interactions. Am Biol Teach 62:583–587. https://doi.org/10.2307/4450982

    Article  Google Scholar 

  76. Adams JC (1981) Heavy metal intensification of DAB-based HRP reaction product. J Histochem Cytochem 29:775. https://doi.org/10.1177/29.6.7252134

    Article  CAS  PubMed  Google Scholar 

  77. Hsu SM, Soban E (1982) Color modification of diaminobenzidine (DAB) precipitation by metallic ions and its application for double immunohistochemistry. J Histochem Cytochem 30:1079–1082. https://doi.org/10.1177/30.10.6182185

    Article  CAS  PubMed  Google Scholar 

  78. Heyduk E, Hickey R, Pozzi N, Heyduk T (2018) Peptide ligand-based ELISA reagents for antibody detection. Anal Biochem 559:55–61. https://doi.org/10.1016/j.ab.2018.08.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Pozsgay J, Szarka E, Huber K et al (2016) Synthetic peptide-based ELISA and ELISpot assay for identifying autoantibody epitopes. Methods Mol Biol 1352:223–233. https://doi.org/10.1007/978-1-4939-3037-1_17

    Article  CAS  PubMed  Google Scholar 

  80. Roque ACA, Lowe CR (2008) Affinity chromatography. In: Affinity chromatography. Humana Press, Totowa, NJ, pp 1–23

    Google Scholar 

  81. Irvine GB (2001) Determination of molecular size by size-exclusion chromatography (gel filtration). Curr Protoc Cell Biol Chapter 5:Unit 5.5. https://doi.org/10.1002/0471143030.cb0505s06

    Article  CAS  PubMed  Google Scholar 

  82. Tenzer S, Peters B, Bulik S et al (2005) Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell Mol Life Sci 62:1025–1037. https://doi.org/10.1007/s00018-005-4528-2

    Article  CAS  PubMed  Google Scholar 

  83. Peters B, Bulik S, Tampe R et al (2003) Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J Immunol 171:1741–1749. https://doi.org/10.4049/jimmunol.171.4.1741

    Article  CAS  PubMed  Google Scholar 

  84. Hoof I, Peters B, Sidney J et al (2009) NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61:1–13. https://doi.org/10.1007/s00251-008-0341-z

    Article  CAS  PubMed  Google Scholar 

  85. Calis JJA, Maybeno M, Greenbaum JA et al (2013) Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 9:e1003266. https://doi.org/10.1371/journal.pcbi.1003266

    Article  PubMed  PubMed Central  Google Scholar 

  86. Wang P, Sidney J, Kim Y et al (2010) Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics 11:568. https://doi.org/10.1186/1471-2105-11-568

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Sidney J, Assarsson E, Moore C et al (2008) Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res 4:2. https://doi.org/10.1186/1745-7580-4-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Nielsen M, Lundegaard C, Lund O (2007) Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 8:238. https://doi.org/10.1186/1471-2105-8-238

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Sturniolo T, Bono E, Ding J et al (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555–561. https://doi.org/10.1038/9858

    Article  CAS  PubMed  Google Scholar 

  90. Larsen JEP, Lund O, Nielsen M (2006) Improved method for predicting linear B-cell epitopes. Immunome Res 2:2. https://doi.org/10.1186/1745-7580-2-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Chou PY, Fasman GD (1978) Prediction of the secondary structure of proteins from their amino acid sequence. Adv Enzymol Relat Areas Mol Biol 47:45–148. https://doi.org/10.1002/9780470122921.ch2

    Article  CAS  PubMed  Google Scholar 

  92. Emini EA, Hughes JV, Perlow DS, Boger J (1985) Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol 55:836–839. https://doi.org/10.1128/JVI.55.3.836-839.1985

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Karplus PA, Schulz GE (1985) Prediction of chain flexibility in proteins. Naturwissenschaften 72:212–213. https://doi.org/10.1007/BF01195768

    Article  CAS  Google Scholar 

  94. Kolaskar AS, Tongaonkar PC (1990) A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 276:172–174. https://doi.org/10.1016/0014-5793(90)80535-q

    Article  CAS  PubMed  Google Scholar 

  95. Parker JM, Guo D, Hodges RS (1986) 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 25:5425–5432. https://doi.org/10.1021/bi00367a013

    Article  CAS  PubMed  Google Scholar 

  96. 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:e1002829. https://doi.org/10.1371/journal.pcbi.1002829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Ponomarenko J, Bui H-H, Li W et al (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 9:514. https://doi.org/10.1186/1471-2105-9-514

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Beyer M, Nesterov A, Block I et al (2007) Combinatorial synthesis of peptide arrays onto a microchip. Science 318:1888. https://doi.org/10.1126/science.1149751

    Article  CAS  PubMed  Google Scholar 

  99. Sievers F, Wilm A, Dineen D et al (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539. https://doi.org/10.1038/msb.2011.75

    Article  PubMed  PubMed Central  Google Scholar 

  100. Bui H-H, Sidney J, Dinh K et al (2006) Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics 7:153. https://doi.org/10.1186/1471-2105-7-153

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Bui H-H, Sidney J, Li W et al (2007) Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics 8:361. https://doi.org/10.1186/1471-2105-8-361

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Gupta S, Kapoor P, Chaudhary K et al (2013) In silico approach for predicting toxicity of peptides and proteins. PLoS One 8:e73957. https://doi.org/10.1371/journal.pone.0073957

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22:195–201. https://doi.org/10.1093/bioinformatics/bti770

    Article  CAS  PubMed  Google Scholar 

  104. Singh S, Singh H, Tuknait A et al (2015) PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues. Biol Direct 10:73. https://doi.org/10.1186/s13062-015-0103-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. 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 31:455–461. https://doi.org/10.1002/jcc.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Abraham MJ, Murtola T, Schulz R et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  108. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225–11236. https://doi.org/10.1021/ja9621760

    Article  CAS  Google Scholar 

  109. Oldham ML, Grigorieff N, Chen J (2016) Structure of the transporter associated with antigen processing trapped by herpes simplex virus. elife 5:e21829. https://doi.org/10.7554/eLife.21829

    Article  PubMed  PubMed Central  Google Scholar 

  110. Nagpal G, Gupta S, Chaudhary K et al (2015) VaccineDA: prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. Sci Rep 5:12478. https://doi.org/10.1038/srep12478

    Article  PubMed  PubMed Central  Google Scholar 

  111. Saha S, Raghava GPS (2006) AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res 34:W202–W209. https://doi.org/10.1093/nar/gkl343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8:4. https://doi.org/10.1186/1471-2105-8-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Gasteiger E, Hoogland C, Gattiker A et al (2005) Protein identification and analysis tools on the ExPASy server. In: The proteomics protocols handbook. Humana Press, Totowa, NJ, pp 571–607

    Chapter  Google Scholar 

  114. Källberg M, Wang H, Wang S et al (2012) Template-based protein structure modeling using the RaptorX web server. Nat Protoc 7:1511–1522. https://doi.org/10.1038/nprot.2012.085

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Ko J, Park H, Heo L, Seok C (2012) GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res 40:W294–W297. https://doi.org/10.1093/nar/gks493

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Lovell SC, Davis IW, Arendall WB et al (2003) Structure validation by Calpha geometry: phi, psi and Cbeta deviation. Proteins 50:437–450. https://doi.org/10.1002/prot.10286

    Article  CAS  PubMed  Google Scholar 

  117. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33:W363–W367. https://doi.org/10.1093/nar/gki481

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Krieger E, Vriend G (2015) New ways to boost molecular dynamics simulations. J Comput Chem 36:996–1007. https://doi.org/10.1002/jcc.23899

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Toukmaji A, Sagui C, Board J, Darden T (2000) Efficient particle-mesh Ewald based approach to fixed and induced dipolar interactions. J Chem Phys 113:10913–10927. https://doi.org/10.1063/1.1324708

    Article  CAS  Google Scholar 

  120. Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174. https://doi.org/10.1002/jcc.20035

    Article  CAS  PubMed  Google Scholar 

  121. Morla S, Makhija A, Kumar S (2016) Synonymous codon usage pattern in glycoprotein gene of rabies virus. Gene 584:1–6. https://doi.org/10.1016/j.gene.2016.02.047

    Article  CAS  PubMed  Google Scholar 

  122. Wu X, Wu S, Li D et al (2010) Computational identification of rare codons of Escherichia coli based on codon pairs preference. BMC Bioinformatics 11:61. https://doi.org/10.1186/1471-2105-11-61

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

Funding

S. Srivastava is supported by the Indian Foundation for Fundamental Research (IFFR) for SARS-CoV-2–related research (Project name: Identification of antigenic patches from entire proteome of SARS-CoV-2 using protein microarray technology). S. D. Chatziefthymiou is supported by the DESY Strategy Fund (DSF) for SARS-CoV-2–related research (project name: Inhibitor screening and structural characterization of virulence factors from SARS-CoV-2).

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Authors and Affiliations

  1. Infection Biology Group, Indian Foundation for Fundamental Research, Raebareli, Uttar Pradesh, India

    Sukrit Srivastava

  2. Deutsches Elektronen-Synchrotron (DESY), Hamburg, Germany

    Spyros D. Chatziefthymiou

  3. Department of Structural Infection Biology, Center for Structural Systems Biology (CSSB), Helmholtz-Center for Infection Research (HZI), Hamburg, Germany

    Spyros D. Chatziefthymiou & Michael Kolbe

  4. MIN-Faculty University Hamburg, Hamburg, Germany

    Michael Kolbe

Authors
  1. Sukrit Srivastava
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  2. Spyros D. Chatziefthymiou
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  3. Michael Kolbe
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Corresponding authors

Correspondence to Sukrit Srivastava or Michael Kolbe .

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Editors and Affiliations

  1. Lankenau Institute for Medical Research, Wynnewood, PA, USA

    Sunil Thomas

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Srivastava, S., Chatziefthymiou, S.D., Kolbe, M. (2022). Vaccines Targeting Numerous Coronavirus Antigens, Ensuring Broader Global Population Coverage: Multi-epitope and Multi-patch Vaccines. In: Thomas, S. (eds) Vaccine Design. Methods in Molecular Biology, vol 2410. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1884-4_7

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  • DOI: https://doi.org/10.1007/978-1-0716-1884-4_7

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