Epitopes screening and vaccine molecular design of PEDV S protein based on immunoinformatics

Zhao, Y., Zhou, C., Guo, B., Yang, X. & Wang, H. Pyrococcus furiosus Argonaute-mediated porcine epidemic diarrhea virus detection. Appl. Microbiol. Biotechnol. 108(1), 137. https://doi.org/10.1007/s00253-023-12919-0 (2024).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, Z., Ma, Z., Li, Y., Gao, S. & Xiao, S. Porcine epidemic diarrhea virus: Molecular mechanisms of attenuation and vaccines. Microb. Pathog. 149, 104553. https://doi.org/10.1016/j.micpath.2020.104553 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun, Y. et al. Porcine epidemic diarrhea virus in Asia: An alarming threat to the global pig industry. Infect. Genet. Evol. 70, 24–26. https://doi.org/10.1016/j.meegid.2019.02.013 (2019).Article 
PubMed 

Google Scholar 
Sekhon, S. S. et al. Porcine epidemic diarrhea (PED) infection, diagnosis and vaccination: A mini review. Toxicol. Environ. Health Sci. 8(5), 277–289. https://doi.org/10.1007/s13530-016-0287-8 (2016).Article 
PubMed 

Google Scholar 
Qin, Z. et al. The oral inactivated porcine epidemic diarrhea virus presenting in the intestine induces mucosal immunity in mice with alginate-chitosan microcapsules. Animals (Basel) 13(5), 889. https://doi.org/10.3390/ani13050889 (2023).Article 
PubMed 

Google Scholar 
Hou, Y. & Wang, Q. Emerging highly virulent porcine epidemic diarrhea virus: Molecular mechanisms of attenuation and rational design of live attenuated vaccines. Int. J. Mol. Sci. 20(21), 5478. https://doi.org/10.3390/ijms20215478 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wei, M. Z. et al. Overview of the recent advances in porcine epidemic diarrhea vaccines. Vet. J. 304, 106097. https://doi.org/10.1016/j.tvjl.2024.106097 (2024).Article 
PubMed 

Google Scholar 
Escalera, A. et al. The impact of S2 mutations on Omicron SARS-CoV-2 cell surface expression and fusogenicity. Emerg. Microbes Infect. 13(1), 2297553. https://doi.org/10.1080/22221751.2023.2297553 (2024).Article 
CAS 
PubMed 

Google Scholar 
Muñoz-Gómez, M. J. et al. Immune response against the SARS-CoV-2 spike protein in cancer patients after COVID-19 vaccination during the Omicron wave: A prospective study. J. Infect. Public Health 17(7), 102473. https://doi.org/10.1016/j.jiph.2024.102473 (2024).Article 
PubMed 

Google Scholar 
Golob, J. L., Lugogo, N., Lauring, A. S. & Lok, A. S. SARS-CoV-2 vaccines: A triumph of science and collaboration. JCI Insight 6(9), e149187. https://doi.org/10.1172/jci.insight.149187 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Sahu, L. K. & Singh, K. Cross-variant proof predictive vaccine design based on SARS-CoV-2 spike protein using immunoinformatics approach. Beni Suef Univ. J. Basic Appl. Sci. 12(1), 5. https://doi.org/10.1186/s43088-023-00341-4 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Pan, J. et al. An intranasal multivalent epitope-based nanoparticle vaccine confers broad protection against divergent influenza viruses. ACS Nano 17(14), 13474–13487. https://doi.org/10.1021/acsnano.3c01829 (2023).Article 
CAS 
PubMed 

Google Scholar 
Andreatta, M. et al. An automated benchmarking platform for MHC class II binding prediction methods. Bioinformatics 34(9), 1522–1528. https://doi.org/10.1093/bioinformatics/btx820 (2018).Article 
CAS 
PubMed 

Google Scholar 
Dhanda, S. K., Vir, P. & Raghava, G. P. Designing of interferon-gamma inducing MHC class-II binders. Biol. Direct 8, 30. https://doi.org/10.1186/1745-6150-8-30 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dhanda, S. K., Gupta, S., Vir, P. & Raghava, G. P. Prediction of IL4 inducing peptides. Clin. Dev. Immunol. 2013, 263952. https://doi.org/10.1155/2013/263952 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Trolle, T. et al. Automated benchmarking of peptide-MHC class I binding predictions. Bioinformatics 31(13), 2174–2181. https://doi.org/10.1093/bioinformatics/btv123 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Reynisson, B., Alvarez, B., Paul, S., Peters, B. & Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48(W1), W449–W454. https://doi.org/10.1093/nar/gkaa379 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Saha, S. & Raghava, G. P. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1), 40–48. https://doi.org/10.1002/prot.21078 (2006).Article 
CAS 
PubMed 

Google Scholar 
Buchan, D. W., Minneci, F., Nugent, T. C., Bryson, K. & Jones, D. T. Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res. 41(Web Server issue), 349–357. https://doi.org/10.1093/nar/gkt381 (2013).Article 

Google Scholar 
Calis, J. J. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 9(10), e1003266. https://doi.org/10.1371/journal.pcbi.1003266 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Gupta, S. et al. In silico approach for predicting toxicity of peptides and proteins. PLoS One 8(9), e73957. https://doi.org/10.1371/journal.pone.0073957 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bui, H. H., Sidney, J., Li, W., Fusseder, N. & Sette, A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinform. 8, 361. https://doi.org/10.1186/1471-2105-8-361 (2007).Article 
CAS 

Google Scholar 
Tahir Ul Qamar, M. et al. Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2. Infect. Dis. Poverty 9(1), 132. https://doi.org/10.1186/s40249-020-00752-w (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Cheng, J., Randall, A. Z., Sweredoski, M. J. & Baldi, P. SCRATCH: A protein structure and structural feature prediction server. Nucleic Acids Res. 33(Web Server issue), 72–76. https://doi.org/10.1093/nar/gki396 (2005).Article 
CAS 

Google Scholar 
Dimitrov, I., Bangov, I., Flower, D. R. & Doytchinova, I. AllerTOP vol 2—A server for in silico prediction of allergens. J. Mol. Model. 20(6), 2278. https://doi.org/10.1007/s00894-014-2278-5 (2014).Article 
CAS 
PubMed 

Google Scholar 
Duvaud, S. et al. Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users. Nucleic Acids Res. 49(W1), W216–W227. https://doi.org/10.1093/nar/gkab225 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ikeda, M., Arai, M., Lao, D. M. & Shimizu, T. Transmembrane topology prediction methods: A re-assessment and improvement by a consensus method using a dataset of experimentally-characterized transmembrane topologies. In Silico Biol. 2(1), 19–33 (2002).PubMed 

Google Scholar 
Karypis, G. YASSPP: Better kernels and coding schemes lead to improvements in protein secondary structure prediction. Proteins 64(3), 575–586. https://doi.org/10.1002/prot.21036 (2006).Article 
CAS 
PubMed 

Google Scholar 
Yang, J. et al. Improved protein structure prediction using predicted interresidue orientations. Proc. Natl. Acad. Sci. USA 117(3), 1496–1503. https://doi.org/10.1073/pnas.1914677117 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Heo, L., Park, H. & Seok, C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 41(Web Server issue), W384–W388. https://doi.org/10.1093/nar/gkt458 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Laskowski, R. A., Rullmannn, J. A., MacArthur, M. W., Kaptein, R. & Thornton, J. M. AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR 8(4), 477–486. https://doi.org/10.1007/BF00228148 (1996).Article 
CAS 
PubMed 

Google Scholar 
Lovell, S. C. et al. Structure validation by Calpha geometry: Phi, psi and Cbeta deviation. Proteins 50(3), 437–450. https://doi.org/10.1002/prot.10286 (2003).Article 
CAS 
PubMed 

Google Scholar 
Jiménez-García, B., Pons, C. & Fernández-Recio, J. pyDockWEB: A web server for rigid-body protein-protein docking using electrostatics and desolvation scoring. Bioinformatics 29(13), 1698–1699. https://doi.org/10.1093/bioinformatics/btt262 (2013).Article 
CAS 
PubMed 

Google Scholar 
Rapin, N., Lund, O., Bernaschi, M. & Castiglione, F. Computational immunology meets bioinformatics: The use of prediction tools for molecular binding in the simulation of the immune system. PLoS One 5(4), e9862. https://doi.org/10.1371/journal.pone.0009862 (2010).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Grote, A. et al. JCat: A novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 33(Web Server issue), W526–W531. https://doi.org/10.1093/nar/gki376 (2005).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Song, G., Li, R. & Cheng, M. Q. Safety, immunogenicity, and protective effective of inhaled COVID-19 vaccines: A systematic review and meta-analysis. J. Med. Virol. 96(4), e29625. https://doi.org/10.1002/jmv.29625 (2024).Article 
CAS 
PubMed 

Google Scholar 
Wang, D., Fang, L. & Xiao, S. Porcine epidemic diarrhea in China. Virus Res. 226, 7–13. https://doi.org/10.1016/j.virusres.2016.05.026 (2016).Article 
CAS 
PubMed 

Google Scholar 
Zhuang, H. et al. Molecular characterization and phylogenetic analysis of porcine epidemic diarrhea virus strains circulating in China from 2020 to 2021. BMC Vet. Res. 18(1), 392. https://doi.org/10.1186/s12917-022-03481-4 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
He, D. et al. Establishment and application of a multiplex RT-PCR to differentiate wild-type and vaccine strains of porcine epidemic diarrhea virus. J. Virol Methods 272, 113684. https://doi.org/10.1016/j.jviromet.2019.113684 (2019).Article 
CAS 
PubMed 

Google Scholar 
Yang, D. et al. Construction and immune effect evaluation of the S protein heptad repeat-based nanoparticle vaccine against porcine epidemic diarrhea virus. Virology 596, 110113. https://doi.org/10.1016/j.virol.2024.110113 (2024).Article 
CAS 
PubMed 

Google Scholar 
Choi, B., Kim, H., Choi, H. & Kang, S. Protein cage nanoparticles as delivery nanoplatforms. Adv. Exp. Med. Biol. 1064, 27–43. https://doi.org/10.1007/978-981-13-0445-3_2 (2018).Article 
CAS 
PubMed 

Google Scholar 
Palombarini, F. et al. Self-assembling ferritin-dendrimer nanoparticles for targeted delivery of nucleic acids to myeloid leukemia cells. J. Nanobiotechnol. 19(1), 172. https://doi.org/10.1186/s12951-021-00921-5 (2021).Article 
CAS 

Google Scholar 
Omoniyi, A. A. et al. In silico design and analyses of a multi-epitope vaccine against Crimean-Congo hemorrhagic fever virus through reverse vaccinology and immunoinformatics approaches. Sci. Rep. 12(1), 8736. https://doi.org/10.1038/s41598-022-12651-1 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, X. et al. Oral delivery of probiotics expressing dendritic cell-targeting peptide fused with porcine epidemic diarrhea virus COE antigen: A promising vaccine strategy against PEDV. Viruses 9(11), 312. https://doi.org/10.3390/v9110312 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kumar, K. M. et al. Immunoinformatic exploration of a multi-epitope-based peptide vaccine candidate targeting emerging variants of SARS-CoV-2. Front. Microbiol. 14, 1251716. https://doi.org/10.3389/fmicb.2023.1251716 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, B. M. et al. Key roles for phosphorylation and the coiled-coil domain in TRIM56-mediated positive regulation of TLR3-TRIF-dependent innate immunity. J. Biol. Chem. 300(5), 107249. https://doi.org/10.1016/j.jbc.2024.107249 (2024).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ma, X. et al. Nanoparticle vaccines based on the receptor binding domain (RBD) and heptad repeat (HR) of SARS-CoV-2 elicit robust protective immune responses. Immunity 53(6), 1315-1330.e9. https://doi.org/10.1016/j.immuni.2020.11.015 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Hot Topics

Related Articles