Design an Efficient Multi-Epitope Peptide Vaccine Candidate Against SARS-CoV-2: An in silico Analysis

References

• Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506. doi:10.1016/S0140-6736(20)30183-531986264
   PubMed Web of Science ®Google Scholar
• Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet. 2020;395(10223):470–473. doi:10.1016/S0140-6736(20)30185-931986257
   PubMed Web of Science ®Google Scholar
• 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. doi:10.1016/S0140-6736(20)30251-832007145
   PubMed Web of Science ®Google Scholar
• Ahmed SF, Quadeer AA, McKay MR. Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies. Viruses. 2020;12(3):254. doi:10.3390/v12030254
   PubMed Web of Science ®Google Scholar
• 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):270–3.32015507
   PubMed Web of Science ®Google Scholar
• Stein RA. The 2019 Coronavirus: learning curves, lessons, and the weakest link. Int J Clin Pract. 2020;74:e13488.32052918
   PubMed Web of Science ®Google Scholar
• Wan Y, Shang J, Graham R, Baric RS, Li F. Receptor recognition by novel coronavirus from Wuhan: an analysis based on decade-long structural studies of SARS. J. Virol. 2020;94. doi:10.1128/JVI.00127-20
   Web of Science ®Google Scholar
• Li F. Structure, function, and evolution of coronavirus spike proteins. Annu Rev Virol. 2016;3:237–261. doi:10.1146/annurev-virology-110615-04230127578435
   PubMed Web of Science ®Google Scholar
• Bosch BJ, van der Zee R, de Haan CA, Rottier PJ. 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. doi:10.1128/JVI.77.16.8801-8811.200312885899
   PubMed Web of Science ®Google Scholar
• 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. doi:10.1126/science.abb250732075877
   PubMed Web of Science ®Google Scholar
• Nelson GW, Stohlman SA, Tahara SM. High affinity interaction between nucleocapsid protein and leader/intergenic sequence of mouse hepatitis virus RNA. J Gen Virol. 2000;81(1):181–188. doi:10.1099/0022-1317-81-1-18110640556
   PubMed Web of Science ®Google Scholar
• Stohlman S, Baric R, Nelson G, Soe L, Welter L, Deans R. Specific interaction between coronavirus leader RNA and nucleocapsid protein. J Virol. 1988;62(11):4288–4295. doi:10.1128/JVI.62.11.4288-4295.19882845141
   PubMed Web of Science ®Google Scholar
• Cong Y, Ulasli M, Schepers H, et al. Nucleocapsid protein recruitment to replication-transcription complexes plays a crucial role in coronaviral life cycle. J Virol. 2020;94(4).
   Web of Science ®Google Scholar
• Kang S, Yang M, Hong Z, et al. Crystal structure of SARS-CoV-2 nucleocapsid protein RNA binding domain reveals potential unique drug targeting sites. bioRxiv. 2020.
   Google Scholar
• Li CK-F, Wu H, Yan H, et al. T cell responses to whole SARS coronavirus in humans. J Immunol. 2008;181(8):5490–5500. doi:10.4049/jimmunol.181.8.549018832706
   PubMed Web of Science ®Google Scholar
• Westerbeck JW, Machamer CE. The infectious bronchitis coronavirus envelope protein alters Golgi pH to protect the spike protein and promote the release of infectious virus. J Virol. 2019;93(11):e00015–00019. doi:10.1128/JVI.00015-1930867314
   PubMed Web of Science ®Google Scholar
• Gupta MK, Vemula S, Donde R, Gouda G, Behera L, Vadde R. In-silico approaches to detect inhibitors of the human severe acute respiratory syndrome coronavirus envelope protein ion channel. J Biomol Struct Dyn. 2020;1–17.
   PubMed Web of Science ®Google Scholar
• Song Z, Yang Y, Wang L, et al. EIF4A2 interacts with the membrane protein of transmissible gastroenteritis coronavirus and plays a role in virus replication. Res Vet Sci. 2019;123:39–46. doi:10.1016/j.rvsc.2018.12.00530583231
   PubMed Web of Science ®Google Scholar
• Control ECfDPa. COVID-19; 2020 Available from: http://mdanderson.libanswers.com/faq/26219. AccessedJuly 21,2020.
   Google Scholar
• Pang J, Wang MX, Ang IYH, et al. Potential rapid diagnostics, vaccine and therapeutics for 2019 novel coronavirus (2019-nCoV): a systematic review. J Clin Med. 2020;9(3):623. doi:10.3390/jcm9030623
   PubMed Web of Science ®Google Scholar
• ul Qamar MT, Alqahtani SM, Alamri MA, Chen -L-L. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. J Pharm Anal. 2020.
   PubMed Web of Science ®Google Scholar
• Robson B. Computers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus. Comput Biol Med. 2020;103670.32209231
   PubMed Web of Science ®Google Scholar
• Amanat F, Krammer F. SARS-CoV-2 vaccines: status report. Immunity. 2020;52(4):583–589. doi:10.1016/j.immuni.2020.03.00732259480
   PubMed Web of Science ®Google Scholar
• Prompetchara E, Ketloy C, Palaga T. Immune responses in COVID-19 and potential vaccines: lessons learned from SARS and MERS epidemic. Asian Pac J Allergy Immunol. 2020;38(1):1–9. doi:10.12932/AP-200220-077232105090
   PubMed Web of Science ®Google Scholar
• Yazdani Z, Rafiei A, Valadan R, Ashrafi H, Pasandi M, Kardan M. Designing a potent L1 protein-based HPV peptide vaccine; a bioinformatics approach. Comput Biol Chem. 2020;85:107209. doi:10.1016/j.compbiolchem.2020.10720932120301
   PubMed Web of Science ®Google Scholar
• Ahmad B, Ashfaq UA, Rahman M-U, Masoud MS, Yousaf MZ. Conserved B and T cell epitopes prediction of ebola virus glycoprotein for vaccine development: an immuno-informatics approach. Microb Pathog. 2019;132:243–253. doi:10.1016/j.micpath.2019.05.01031075428
   PubMed Web of Science ®Google Scholar
• Shahid F, Ashfaq UA, Javaid A, Khalid H. Immunoinformatics guided rational design of a next generation multi epitope based peptide (MEBP) vaccine by exploring Zika virus proteome. Infect Genet Evol. 2020;80:104199. doi:10.1016/j.meegid.2020.10419931962160
   PubMed Web of Science ®Google Scholar
• Srivastava S, Kamthania M, Singh S, Saxena AK, Sharma N. Structural basis of development of multi-epitope vaccine against middle east respiratory syndrome using in silico approach. Infect Drug Resist. 2018;11:2377. doi:10.2147/IDR.S17511430538505
   PubMed Web of Science ®Google Scholar
• Ul Qamar MT, Saleem S, Ashfaq UA, Bari A, Anwar F, Alqahtani S. 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. doi:10.1186/s12967-019-2116-831703698
   PubMed Web of Science ®Google Scholar
• Ul Qamar MT, Rehman A, Ashfaq UA, et al. Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: immunoinformatics and in silico approaches. BioRxiv. 2020.
   Google Scholar
• Behbahani M. In silico Design of novel Multi-epitope recombinant Vaccine based on Coronavirus surface glycoprotein. BioRxiv. 2020.
   Google Scholar
• Kalita P, Padhi A, Zhang KY, Tripathi T. Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2. Microb Pathog. 2020;145:104236. doi:10.1016/j.micpath.2020.10423632376359
   PubMed Web of Science ®Google Scholar
• Zhang Q, Wang P, Kim Y, et al. Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res. 2008;36(suppl_2):W513–W518. doi:10.1093/nar/gkn25418515843
   PubMed Web of Science ®Google Scholar
• Vita R, Mahajan S, Overton JA, et al. The immune epitope database (IEDB): 2018 update. Nucleic Acids Res. 2019;47(D1):D339–D343. doi:10.1093/nar/gky100630357391
   PubMed Web of Science ®Google Scholar
• Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics. 2016;32(4):511–517. doi:10.1093/bioinformatics/btv63926515819
   PubMed Web of Science ®Google Scholar
• Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Res. 2008;36(suppl_2):W509–W512. doi:10.1093/nar/gkn20218463140
   PubMed Web of Science ®Google Scholar
• Lundegaard C, Nielsen M, Lund O. The validity of predicted T-cell epitopes. Trends Biotechnol. 2006;24(12):537–538. doi:10.1016/j.tibtech.2006.10.00117045685
   PubMed Web of Science ®Google Scholar
• Lundegaard C, Lund O, Nielsen M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics. 2008;24(11):1397–1398. doi:10.1093/bioinformatics/btn12818413329
   PubMed Web of Science ®Google Scholar
• Nielsen M, Lundegaard C, Worning P, et al. Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003;12(5):1007–1017. doi:10.1110/ps.023940312717023
   PubMed Web of Science ®Google Scholar
• Buus S, Lauemøller S, Worning P, et al. Sensitive quantitative predictions of peptide‐MHC binding by a ‘Query by Committee’artificial neural network approach. Tissue Antigens. 2003;62(5):378–384. doi:10.1034/j.1399-0039.2003.00112.x14617044
   PubMedGoogle Scholar
• Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinform. 2005;6(1):132. doi:10.1186/1471-2105-6-132
   PubMed Web of Science ®Google Scholar
• Kim Y, Sidney J, Pinilla C, Sette A, Peters B. Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC Bioinform. 2009;10(1):394. doi:10.1186/1471-2105-10-394
   PubMed Web of Science ®Google Scholar
• 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(1):2. doi:10.1186/1745-7580-4-218221540
   PubMedGoogle Scholar
• Moutaftsi M, Peters B, Pasquetto V, et al. A consensus epitope prediction approach identifies the breadth of murine T CD8+-cell responses to vaccinia virus. Nat Biotechnol. 2006;24(7):817–819. doi:10.1038/nbt121516767078
   PubMed Web of Science ®Google Scholar
• Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. 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. doi:10.4049/jimmunol.170089328978689
   PubMed Web of Science ®Google Scholar
• Nielsen M, Andreatta M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med. 2016;8(1):33. doi:10.1186/s13073-016-0288-x27029192
   PubMed Web of Science ®Google Scholar
• Hoof I, Peters B, Sidney J, et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics. 2009;61(1):1. doi:10.1007/s00251-008-0341-z19002680
   PubMed Web of Science ®Google Scholar
• 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. 2007;2(8):e796. doi:10.1371/journal.pone.000079617726526
   PubMed Web of Science ®Google Scholar
• Karosiene E, Lundegaard C, Lund O, Nielsen M. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics. 2012;64(3):177–186. doi:10.1007/s00251-011-0579-822009319
   PubMed Web of Science ®Google Scholar
• 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. doi:10.1093/bioinformatics/btp13719297351
   PubMed Web of Science ®Google Scholar
• 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 Immunol. 2016;197(4):1517–1524. doi:10.4049/jimmunol.160058227402703
   PubMed Web of Science ®Google Scholar
• Bhasin M, Raghava G. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine. 2004;22(23–24):3195–3204. doi:10.1016/j.vaccine.2004.02.00515297074
   PubMed Web of Science ®Google Scholar
• Nielsen M, Lund O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinform. 2009;10(1):296. doi:10.1186/1471-2105-10-296
   PubMed Web of Science ®Google Scholar
• Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinform. 2007;8(1):238. doi:10.1186/1471-2105-8-238
   PubMed Web of Science ®Google Scholar
• 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. doi:10.1038/985810385319
   PubMed Web of Science ®Google Scholar
• Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, Nielsen M. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics. 2015;67(11–12):641–650. doi:10.1007/s00251-015-0873-y26416257
   PubMed Web of Science ®Google Scholar
• Reche PA, Glutting J-P, Zhang H, Reinherz EL. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics. 2004;56(6):405–419. doi:10.1007/s00251-004-0709-715349703
   PubMed Web of Science ®Google Scholar
• Emini EA, Hughes JV, Perlow D, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol. 1985;55(3):836–839. doi:10.1128/JVI.55.3.836-839.19852991600
   PubMed Web of Science ®Google Scholar
• Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45(W1):W24–W29. doi:10.1093/nar/gkx34628472356
   PubMed Web of Science ®Google Scholar
• Sarwar MA, Rehman A, Ferzund J. Database Search, Alignment Viewer and Genomics Analysis Tools: big Data for Bioinformatics. Int J Comput Sci Netw. 2016;14(12):317.
   Google Scholar
• Dhanda SK, Vir P, Raghava GP. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct. 2013;8(1):30. doi:10.1186/1745-6150-8-3024304645
   PubMed Web of Science ®Google Scholar
• Magnan CN, Baldi P. SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics. 2014;30(18):2592–2597. doi:10.1093/bioinformatics/btu35224860169
   PubMed Web of Science ®Google Scholar
• Agostini F, Cirillo D, Livi CM, Ponti RD, Tartaglia GG. ccSOL omics: a webserver for large-scale prediction of endogenous and heterologous solubility in E. coli. Bioinformatics. 2014;30(20):2975–2977. doi:10.1093/bioinformatics/btu42024990610
   PubMed Web of Science ®Google Scholar
• Doytchinova IA, Flower DR. Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties. Vaccine. 2007;25(5):856–866. doi:10.1016/j.vaccine.2006.09.03217045707
   PubMed Web of Science ®Google Scholar
• Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server In: Walker J.M. (eds) The Proteomics Protocols Handbook. Springer; 2005:571–607.
   Google Scholar
• Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10(6):845. doi:10.1038/nprot.2015.05325950237
   PubMed Web of Science ®Google Scholar
• Ko J, Park H, Heo L, Seok C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. 2012;40(W1):W294–W297. doi:10.1093/nar/gks49322649060
   PubMed Web of Science ®Google Scholar
• Lovell SC, Davis IW, Arendall WB III, et al. Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins. 2003;50(3):437–450. doi:10.1002/prot.1028612557186
   PubMed Web of Science ®Google Scholar
• Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993;2(9):1511–1519. doi:10.1002/pro.55600209168401235
   PubMed Web of Science ®Google Scholar
• Lill MA, Danielson ML. Computer-aided drug design platform using PyMOL. J Comput Aided Mol. 2011;25(1):13–19. doi:10.1007/s10822-010-9395-8
   PubMed Web of Science ®Google Scholar
• Ponomarenko J, Bui -H-H, Li W, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinform. 2008;9(1):514. doi:10.1186/1471-2105-9-514
   PubMed Web of Science ®Google Scholar
• Vajda S, Yueh C, Beglov D, et al. New additions to the ClusPro server motivated by CAPRI. Proteins. 2017;85(3):435–444. doi:10.1002/prot.2521927936493
   PubMed Web of Science ®Google Scholar
• Kozakov D, Hall DR, Xia B, et al. The ClusPro web server for protein–protein docking. Nat Protoc. 2017;12(2):255. doi:10.1038/nprot.2016.16928079879
   PubMed Web of Science ®Google Scholar
• Kozakov D, Beglov D, Bohnuud T, et al. How good is automated protein docking? Proteins. 2013;81(12):2159–2166. doi:10.1002/prot.2440323996272
   PubMed Web of Science ®Google Scholar
• Mathews DH, Disney MD, Childs JL, Schroeder SJ, Zuker M, Turner DH. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc Natl Acad Sci. 2004;101(19):7287–7292. doi:10.1073/pnas.040179910115123812
   PubMed Web of Science ®Google Scholar
• Mizel SB, Honko AN, Moors MA, Smith PS, West AP. Induction of macrophage nitric oxide production by Gram-negative flagellin involves signaling via heteromeric Toll-like receptor 5/Toll-like receptor 4 complexes. J Immunol. 2003;170(12):6217–6223. doi:10.4049/jimmunol.170.12.621712794153
   PubMed Web of Science ®Google Scholar
• Moors MA, Li L, Mizel SB. Activation of interleukin-1 receptor-associated kinase by gram-negative flagellin. Infect Immun. 2001;69(7):4424–4429. doi:10.1128/IAI.69.7.4424-4429.200111401982
   PubMed Web of Science ®Google Scholar
• Didierlaurent A, Ferrero I, Otten LA, et al. Flagellin promotes myeloid differentiation factor 88-dependent development of Th2-type response. J Immunol. 2004;172(11):6922–6930. doi:10.4049/jimmunol.172.11.692215153511
   PubMed Web of Science ®Google Scholar
• Lee SE, Kim SY, Jeong BC, et al. A bacterial flagellin, Vibrio vulnificus FlaB, has a strong mucosal adjuvant activity to induce protective immunity. Infect Immun. 2006;74(1):694–702. doi:10.1128/IAI.74.1.694-702.200616369026
   PubMed Web of Science ®Google Scholar
• Han S, Yang K, Ozen Z, et al. Enhanced differentiation of splenic plasma cells but diminished long-lived high-affinity bone marrow plasma cells in aged mice. J Immunol. 2003;170(3):1267–1273. doi:10.4049/jimmunol.170.3.126712538685
   PubMed Web of Science ®Google Scholar
• Pino O, Martin M, Michalek SM. Cellular mechanisms of the adjuvant activity of the flagellin component FljB of Salmonella enterica Serovar Typhimurium to potentiate mucosal and systemic responses. Infect Immun. 2005;73(10):6763–6770. doi:10.1128/IAI.73.10.6763-6770.200516177354
   PubMed Web of Science ®Google Scholar
• Honko AN, Mizel SB. Mucosal administration of flagellin induces innate immunity in the mouse lung. Infect Immun. 2004;72(11):6676–6679. doi:10.1128/IAI.72.11.6676-6679.200415501801
   PubMed Web of Science ®Google Scholar
• Cunningham AF, Khan M, Ball J, et al. Responses to the soluble flagellar protein FliC are Th2, while those to FliC on Salmonella are Th1. Eur J Immunol. 2004;34(11):2986–2995. doi:10.1002/eji.20042540315384042
   PubMed Web of Science ®Google Scholar
• McSorley SJ, Ehst BD, Yu Y, Gewirtz AT. Bacterial flagellin is an effective adjuvant for CD4+ T cells in vivo. J Immunol. 2002;169(7):3914–3919. doi:10.4049/jimmunol.169.7.391412244190
   PubMed Web of Science ®Google Scholar
• Wang B-Z, Quan F-S, Kang S-M, Bozja J, Skountzou I, Compans RW. Incorporation of membrane-anchored flagellin into influenza virus-like particles enhances the breadth of immune responses. J Virol. 2008;82(23):11813–11823. doi:10.1128/JVI.01076-0818786995
   PubMed Web of Science ®Google Scholar
• López-Boado YS, Cobb LM, Deora R. Bordetella bronchiseptica flagellin is a proinflammatory determinant for airway epithelial cells. Infect Immun. 2005;73(11):7525–7534. doi:10.1128/IAI.73.11.7525-7534.200516239555
   PubMed Web of Science ®Google Scholar
• Huleatt JW, Nakaar V, Desai P, et al. Potent immunogenicity and efficacy of a universal influenza vaccine candidate comprising a recombinant fusion protein linking influenza M2e to the TLR5 ligand flagellin. Vaccine. 2008;26(2):201–214. doi:10.1016/j.vaccine.2007.10.06218063235
   PubMed Web of Science ®Google Scholar
• Skountzou I, Del Pilar Martin M, Wang B, et al. Salmonella flagellins are potent adjuvants for intranasally administered whole inactivated influenza vaccine. Vaccine. 2010;28(24):4103–4112. doi:10.1016/j.vaccine.2009.07.05819654062
   PubMed Web of Science ®Google Scholar
• Negahdaripour M, Eslami M, Nezafat N, et al. A novel HPV prophylactic peptide vaccine, designed by immunoinformatics and structural vaccinology approaches. Infect Genet Evol. 2017;54:402–416. doi:10.1016/j.meegid.2017.08.00228780192
   PubMed Web of Science ®Google Scholar
• Telusma G, Datta S, Mihajlov I, et al. Dendritic cell activating peptides induce distinct cytokine profiles. Int Immunol. 2006;18(11):1563–1573. doi:10.1093/intimm/dxl08916966494
   PubMed Web of Science ®Google Scholar
• Saenz R, Futalan D, Leutenez L, et al. TLR4-dependent activation of dendritic cells by an HMGB1-derived peptide adjuvant. J Transl Med. 2014;12(1):211. doi:10.1186/1479-5876-12-21125123824
   PubMed Web of Science ®Google Scholar
• Talebi S, Bolhassani A, Sadat SM, Vahabpour R, Agi E, Shahbazi S. Hp91 immunoadjuvant: an HMGB1-derived peptide for development of therapeutic HPV vaccines. Biomed Pharmacother. 2017;85:148–154. doi:10.1016/j.biopha.2016.11.11527930979
   PubMed Web of Science ®Google Scholar
• Milani A, Bolhassani A, Shahbazi S, Motevalli F, Sadat SM, Soleymani S. Small heat shock protein 27: an effective adjuvant for enhancement of HIV-1 Nef antigen-specific immunity. Immunol Lett. 2017;191:16–22. doi:10.1016/j.imlet.2017.09.00528917624
   PubMed Web of Science ®Google Scholar
• Leikina E, Delanoe-Ayari H, Melikov K, et al. Carbohydrate-binding molecules inhibit viral fusion and entry by crosslinking membrane glycoproteins. Nat Immunol. 2005;6(10):995–1001. doi:10.1038/ni124816155572
   PubMed Web of Science ®Google Scholar
• Funderburg N, Lederman MM, Feng Z, et al. Human β-defensin-3 activates professional antigen-presenting cells via Toll-like receptors 1 and 2. Proc Natl Acad Sci. 2007;104(47):18631–18635. doi:10.1073/pnas.070213010418006661
   PubMed Web of Science ®Google Scholar
• Wolk K, Kunz S, Witte E, Friedrich M, Asadullah K, Sabat R. IL-22 increases the innate immunity of tissues. Immunity. 2004;21(2):241–254. doi:10.1016/j.immuni.2004.07.00715308104
   PubMed Web of Science ®Google Scholar
• Sørensen OE, Cowland JB, Theilgaard-Mönch K, Liu L, Ganz T, Borregaard N. Wound healing and expression of antimicrobial peptides/polypeptides in human keratinocytes, a consequence of common growth factors. J Immunol. 2003;170(11):5583–5589.12759437
   PubMed Web of Science ®Google Scholar
• Ferris LK, Mburu YK, Mathers AR, et al. Human beta-defensin 3 induces maturation of human Langerhans cell–like dendritic cells: an antimicrobial peptide that functions as an endogenous adjuvant. J Invest Dermatol. 2013;133(2):460–468. doi:10.1038/jid.2012.31922951718
   PubMed Web of Science ®Google Scholar
• Joly S, Organ CC, Johnson GK, McCray PB Jr, Guthmiller JM. Correlation between β-defensin expression and induction profiles in gingival keratinocytes. Mol Immunol. 2005;42(9):1073–1084.15829297
   PubMed Web of Science ®Google Scholar
• Judge CJ, Reyes-Aviles E, Conry SJ, et al. HBD-3 induces NK cell activation, IFN-γ secretion and mDC dependent cytolytic function. Cell Immunol. 2015;297(2):61–68. doi:10.1016/j.cellimm.2015.06.00426302933
   PubMed Web of Science ®Google Scholar
• Röhrl J, Yang D, Oppenheim JJ, Hehlgans T. Human β-defensin 2 and 3 and their mouse orthologs induce chemotaxis through interaction with CCR2. J Immunol. 2010;184(12):6688–6694. doi:10.4049/jimmunol.090398420483750
   PubMed Web of Science ®Google Scholar
• Chen X, Zaro JL, Shen W-C. Fusion protein linkers: property, design and functionality. Adv Drug Deliv Rev. 2013;65(10):1357–1369. doi:10.1016/j.addr.2012.09.03923026637
   PubMed Web of Science ®Google Scholar
• Chen X, Lee H-F, Zaro JL, Shen W-C. Effects of receptor binding on plasma half-life of bifunctional transferrin fusion proteins. Mol Pharm. 2011;8(2):457–465. doi:10.1021/mp100306421291258
   PubMed Web of Science ®Google Scholar
• Mazaleuskaya L, Veltrop R, Ikpeze N, Martin-Garcia J, Navas-Martin S. Protective role of Toll-like receptor 3-induced type I interferon in murine coronavirus infection of macrophages. Viruses. 2012;4(5):901–923. doi:10.3390/v405090122754655
   PubMed Web of Science ®Google Scholar
• Campbell GR, Spector SA. Toll-like receptor 8 ligands activate a vitamin D mediated autophagic response that inhibits human immunodeficiency virus type 1. PLoS Pathog. 2012;8(11):e1003017. doi:10.1371/journal.ppat.100301723166493
   PubMed Web of Science ®Google Scholar
• Hammerbeck DM, Burleson GR, Schuller CJ, et al. Administration of a dual toll-like receptor 7 and toll-like receptor 8 agonist protects against influenza in rats. Antiviral Res. 2007;73(1):1–11. doi:10.1016/j.antiviral.2006.07.01116959331
   PubMed Web of Science ®Google Scholar
• Bhatnager R, Bhasin M, Arora J, Dang AS. Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach. J Biomol Struct Dyn. 2020;1–16. doi:10.1080/07391102.2020.1787227
   Web of Science ®Google Scholar
• Bhattacharya M, Sharma AR, Patra P, et al. Development of epitope‐based peptide vaccine against novel coronavirus 2019 (SARS‐COV‐2): immunoinformatics approach. J Med Virol. 2020;92(6):618–631. doi:10.1002/jmv.2573632108359
   PubMed Web of Science ®Google Scholar