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Computer-Aided Design of a Novel Poly-Epitope Protein in Fusion with an Adjuvant as a Vaccine Candidate Against Leptospirosis

Author(s):

Ehsan Rashidian*, Ali Forouharmehr, Narges Nazifi , Amin Jaydari and Nemat Shams   Pages 1 - 11 ( 11 )

Abstract:


Background: Leptospirosis is a prevalent zoonotic disease caused by Leptospira interrogans bacterium. Despite the importance of this disease, traditional strategies including attenuated and inactivated vaccines have not been able to prevent leptospirosis.

Objective: Hence, this study was designed to develop a novel poly-epitope fusion protein vaccine against leptospirosis.

Results: To do so, the best epitopes of OmpA, LipL45, OmpL1, LipL41 and LipL21 proteins were predicted. Then, the best predicted epitopes were applied to assemble IFN-γ, MHC I binding, B cell, MHC II binding fragments, and heparin-binding hemagglutinin adhesion was used as a molecular adjuvant. After designing the vaccine, the most important features of it, including physicochemical parameters, protein structures and protein-protein interaction were evaluated. Finally, the nucleotide sequence of the designed vaccine was used for codon adaptation. The results showed that the designed vaccine was a stable protein with antigenicity of 0.913 which could dock to its receptor. The results also suggested that the nucleotide sequence of the designed vaccine could be expressed in prokaryotic system.

Conclusion: Based on results of this study, it can be concluded that our designed vaccine can be a promising candidate to control leptospirosis.

Keywords:

Poly-epitope fusion protein Vaccine, Immunogenic protein, Leptospirosis, Epitope prediction, Bioinformatic, vaccine

Affiliation:

Lorestan University, Veterinary Medicine, Lorestan University, Kamalvand region, Khorramabad city, Lorestan province, Lorestan University, Animal science Department , Animal science Department, Ferdowsi University of Mashhad, Veterinary Medicine Department, Lorestan University, Veterinary Medicine Department, Lorestan University



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