American Journal of Microbiological Research
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American Journal of Microbiological Research. 2018, 6(3), 94-114
DOI: 10.12691/ajmr-6-3-5
Open AccessArticle

Multi Epitopes Vaccine Prediction against Severe Acute Respiratory Syndrome (SARS) Coronavirus Using Immunoinformatics Approaches

Yassir A. Almofti1, , Khoubieb Ali Abd-elrahman2, Sahar Abd Elgadir Gassmallah3 and Mohammed Ahmed Salih4

1Department of Biochemistry and Molecular Biology, College of Veterinary Medicine, University of Bahri, Khartoum, Sudan

2Department of pharmaceutical technology, College of Pharmacy, University of Medical Science and Technology (MUST) Khartoum, Sudan

3Department of Medical laboratory, Sudan University of Science and Technology, Khartoum, Sudan

4Department of Bioinformatics, Africa city of Technology, Khartoum, Sudan

Pub. Date: July 21, 2018

Cite this paper:
Yassir A. Almofti, Khoubieb Ali Abd-elrahman, Sahar Abd Elgadir Gassmallah and Mohammed Ahmed Salih. Multi Epitopes Vaccine Prediction against Severe Acute Respiratory Syndrome (SARS) Coronavirus Using Immunoinformatics Approaches. American Journal of Microbiological Research. 2018; 6(3):94-114. doi: 10.12691/ajmr-6-3-5

Abstract

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.

Keywords:
SARS NCBI IEDB Insilico prediction Immunoinformatics

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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