Bioimpacts. 2019;9(3):131-144.
doi: 10.15171/bi.2019.18
  Abstract View: 115
  PDF Download: 2

Original Research

A multi-method and structure-based in silico vaccine designing against Echinococcus granulosus through investigating enolase protein

Abstract

Introduction: Hydatid disease is a ubiquitous parasitic zoonotic disease that causes different medical, economic and serious public health problems in some parts of the world. The causal organism is a multi-stage parasite named Echinococcus granulosus whose life cycle is dependent on two types of mammalian hosts, definitive and intermediate hosts. Methods: In this study, enolase, as a key functional enzyme in the metabolism of E. granulosus (EgEnolase), was targeted through a comprehensive in silico modeling analysis and for designing a host-specific multi-epitope vaccine. Three-dimensional (3D) structure of enolase was modeled using MODELLER v9.18 software. The B-cell epitopes (BEs) were predicted based on the multi-method approach and via some authentic online predictors. ClusPro v2.0 server was used for docking-based T-helper epitope prediction. The 3D structure of the vaccine construct was modeled using the RaptorX server. The designed vaccine was evaluated for its immunogenicity, physicochemical properties, and allergenicity. The codon optimization of the vaccine sequence was performed based on the codon usage table of E. coli K12. Finally, the energy minimization and molecular docking were implemented for simulation the vaccine binding affinity to the TLR-2 and TLR-4 receptors and the complex stability. Results: The designed multi-epitope vaccine was found to induce anti-EgEnolase immunity which may have the potential to prevent the survival and proliferation of E. granulosus into the definitive host. This step-by-step immunoinformatics approach could be considered as a rational platform for designing vaccines against such multi-stage parasites. Conclusion: This multi-epitope vaccine is proposed to serve as a promising preventive anti-echinococcosis agent.
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Submitted: 29 Oct 2018
Revised: 27 Nov 2018
Accepted: 04 Dec 2018
First published online: 08 Mar 2019
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