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Rapid identification of immune epitopes would be an efficient way to screen the candidates for vaccine development at the time of pandemic. This study aimed to predict the protective epitopes with bioinformatics methods and resources for vaccine development.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The genome sequence and protein sequences of SARS-CoV-2 were retrieved from the National Center for Biotechnology Information (NCBI) database. ABCpred and BepiPred servers were utilized for sequential B-cell epitope analysis. Discontinuous B-cell epitopes were predicted via DiscoTope 2.0 program. IEDB server was utilized for HLA-1 and HLA-2 binding peptides computation. Surface accessibility, antigenicity, and other important features of forecasted epitopes were characterized for immunogen potential evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 63 sequential B-cell epitopes on spike protein were predicted and 4 peptides (Spike\n                      <jats:sub>315\u2013324<\/jats:sub>\n                      , Spike\n                      <jats:sub>333\u2013338<\/jats:sub>\n                      , Spike\n                      <jats:sub>648\u2013663<\/jats:sub>\n                      , Spike\n                      <jats:sub>1064\u20131079<\/jats:sub>\n                      ) exhibited high antigenicity score and good surface accessibility. Ten residues within spike protein (Gly\n                      <jats:sup>496<\/jats:sup>\n                      , Glu\n                      <jats:sup>498<\/jats:sup>\n                      , Pro\n                      <jats:sup>499<\/jats:sup>\n                      , Thr\n                      <jats:sup>500<\/jats:sup>\n                      , Leu\n                      <jats:sup>1141<\/jats:sup>\n                      , Gln\n                      <jats:sup>1142<\/jats:sup>\n                      , Pro\n                      <jats:sup>1143<\/jats:sup>\n                      , Glu\n                      <jats:sup>1144<\/jats:sup>\n                      , Leu\n                      <jats:sup>1145<\/jats:sup>\n                      , Asp\n                      <jats:sup>1146<\/jats:sup>\n                      ) are forecasted as components of discontinuous B-cell epitopes. The bioinformatics analysis of HLA binding peptides within nucleocapsid protein produced 81 and 64 peptides being able to bind MHC class I and MHC class II molecules respectively. The peptides (Nucleocapsid\n                      <jats:sub>66\u201375<\/jats:sub>\n                      , Nucleocapsid\n                      <jats:sub>104\u2013112<\/jats:sub>\n                      ) were predicted to bind a wide spectrum of both HLA-1 and HLA-2 molecules.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>B-cell epitopes on spike protein and T-cell epitopes within nucleocapsid protein were identified and recommended for developing a protective vaccine against SARS-CoV-2.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40249-020-00713-3","type":"journal-article","created":{"date-parts":[[2020,8,2]],"date-time":"2020-08-02T01:02:26Z","timestamp":1596330146000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2"],"prefix":"10.1186","volume":"9","author":[{"given":"Hong-Zhi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Li","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin-Ling","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun-Feng","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,10]]},"reference":[{"issue":"10223","key":"713_CR1","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. 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