In-Silico Prediction of Peptide Based Vaccine Against Zika Virus

  • Parul Sharma
  • Rajbir Kaur
  • Atul Kumar Upadhyay
  • Vikas KaushikEmail author


Zika fever is a disease caused by Zika virus which is a mosquito-borne flavivirus and infection is through the bite of mosquito from the Aedes genus. Other mode of transmission of Zika fever is blood transfusion, which is thoroughly investigated. Prominent symptoms of Zika virus fever are mild fever, skin rash, conjunctivitis, muscle and joint pain, malaise or headache. These symptoms normally last for 2–7 days. There is currently no effective and easily affordable vaccine available against Zika virus. Keeping the current status in mind we have devised our objectives to design a peptide-based vaccine against Zika Virus using in-silico approach. The NS3 and NS5 proteins of Zika virus are well known of having important role in replication of the virus that is why we have targeted these proteins for epitope prediction. Prediction and screening of epitopes was performed using IEDB tool. We have also checked the efficacy of these screened epitopes with the help of IC50 value and docking score. “VEMGEAAAI” was found to be the high affinity binding epitope for MHC Class I and MHC Class II molecule. These findings conclude that the predicted epitope could well be used as a potential vaccine candidate for formulations of vaccines against Zika virus.


ZIKV Glycoprotein Docking Simulation Ligand Epitope MHC-I MHC-II Bioinformatics and molecular dynamics 



The authors gratefully acknowledge the logistic support from Computational Biology Lab of School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India to accomplice this work. Authors acknowledge scholarly work support from PS in identification of epitopes and RK and AU in validation of epitopes.

Author Contributions

PS conceived the study under the supervision of VK. PS and RK predicted and screened the epitopes, VK and PS performed computational work. VK and AU participated in the design of the study and manuscript writing and editing. All authors read and approved the final manuscript.

Compliance with Ethical Standards

Conflict of interest

The author(s) declare(s) that they have no any competing interests.


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Domain of Bioinformatics, School of Bio-engineering and BiosciencesLovely Professional UniversityPhagwaraIndia

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