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<records>
  <record>
    <language>eng</language>
    <publisher>Science and Education Publishing</publisher>
    <journalTitle>American Journal of Microbiological Research</journalTitle>
    <eissn>2328-4137</eissn>
    <publicationDate>2018-07-21</publicationDate>
    <volume>6</volume>
    <issue>3</issue>
    <startPage>94</startPage>
    <endPage>114</endPage>
    <doi>10.12691/ajmr-6-3-5</doi>
    <publisherRecordId>AJMR2018635</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">Multi Epitopes Vaccine Prediction against Severe Acute Respiratory Syndrome (SARS) Coronavirus Using Immunoinformatics Approaches</title>
    <authors>
      <author>
        <name>Yassir A. Almofti</name>
        <email>yamofti99@gmail.com</email>
        <affiliationId>1</affiliationId>
      </author>
      <author>
        <name>Khoubieb Ali Abd-elrahman</name>
        <affiliationId>2</affiliationId>
      </author>
      <author>
        <name>Sahar Abd Elgadir Gassmallah</name>
        <affiliationId>3</affiliationId>
      </author>
      <author>
        <name>Mohammed Ahmed Salih</name>
        <affiliationId>4</affiliationId>
      </author>
    </authors>
    <affiliationsList>
      <affiliationName affiliationId="1">Department of Biochemistry and Molecular Biology, College of Veterinary Medicine, University of Bahri, Khartoum, Sudan</affiliationName>
      <affiliationName affiliationId="2">Department of pharmaceutical technology, College of Pharmacy, University of Medical Science and Technology (MUST) Khartoum, Sudan</affiliationName>
      <affiliationName affiliationId="3">Department of Medical laboratory, Sudan University of Science and Technology, Khartoum, Sudan</affiliationName>
      <affiliationName affiliationId="4">Department of Bioinformatics, Africa city of Technology, Khartoum, Sudan</affiliationName>
    </affiliationsList>
    <abstract language="eng">Efforts for developing vaccine against Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) is crucial in prevention of SARS re-emergence. The global outbreak of SARS was contained since 2003. However concerns remain over the possibility of future recurrences, especially with recent reports of laboratory-acquired infections and the presence of sporadic cases, raising a serious concern. SARS-CoV spike S protein (1255aa) is an important target in developing safe and effective vaccines. In this study multiple bio-informatics and immuno-informatics implementation tools from NCBI and IEDB were used for epitopes prediction from spike S protein. The predicted epitopes were further assessed for population coverage against the whole world population. Our results demonstrated that the epitopes 38-RGVYYPDEI-46, 200-YQPIDVVRD-208 and 388-VVKGDDVRQ-396 elicit and stimulate B cell since they got higher score in Emini and Kolaskar and tongaonker software. For T-cell: the epitopes 47-FRSDTLYLT-55, 195-YVYKGYQPI-203 and 880-FAMQMAYRF-888 were found to interact with both MHC-1 and MHC-II alleles. Moreover 851-MIAAYTAAL-859 showed higher affinity to MHC-1 alleles while 782-FNFSQILPD-790 interacted only with MHC-II alleles. The population coverage epitope set for MHC-1 and MHC-II predicted epitopes was 82.16% and 99.97% respectively. All predicted epitopes against T cell (MHC-I/MHC-II) demonstrated strong potentiality as promising peptides vaccine with population coverage epitope set against the whole world of 100%. Taken together eight epitopes were proposed to interact with B and T cells and act as peptide vaccine against SARS-CoV virus. In vitro and in vivo studies are recommended to prove the effectiveness of these epitopes as a peptide vaccine.</abstract>
    <fullTextUrl format="pdf">http://pubs.sciepub.com/ajmr/6/3/5/ajmr-6-3-5.pdf</fullTextUrl>
    <keywords language="eng">
      <keyword>SARS</keyword>
      <keyword>NCBI</keyword>
      <keyword>IEDB</keyword>
      <keyword>Insilico prediction</keyword>
      <keyword>Immunoinformatics</keyword>
    </keywords>
  </record>
</records>