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A Computational Reverse Vaccinology Approach for the Design and Development of Multi-Epitopic Vaccine Against Avian Pathogen Mycoplasma gallisepticum

ABSTRACT: Avian mycoplasma is a bacterial disease causing chronic respiratory disease (CRD) in poultry industries with high economic losses. The eradication of this disease still remains as a challenge. A multi-epitope prophylactic vaccine aiming the antigenic proteins of Mycoplasma gallisepticum can be a capable candidate to eradicate this infection. The present study is focused to design a multi-epitope vaccine candidate consisting of cytotoxic T-cell (CTL), helper T-cell (HTL), and B-cell epitopes of antigenic proteins, using immunoinformatics strategies. The multi-epitopic vaccine was designed, and its tertiary model was predcited, which was further refined and validated by computational tools. After initial validation, molecular docking was performed between multi-epitope vaccine construct and chicken TLR-2 and 5 receptors, which predicted effective binding. The in silico results specify the structural stability, precise specificity, and immunogenic response of the designed multi-epitope vaccine, and it could be an appropriate vaccine candidate for the M. gallisepticum infection.

SUBMITTER: Mugunthan S

PROVIDER: S-EPMC8577832 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundReverse vaccinology accelerates the discovery of potential vaccine candidates (PVCs) prior to experimental validation. Current programs typically use one bacterial proteome to identify PVCs through a filtering architecture using feature prediction programs or a machine learning approach. Filtering approaches may eliminate potential antigens based on limitations in the accuracy of prediction tools used. Machine learning approaches are heavily dependent on the selection of training datasets with experimentally validated antigens (positive control) and non-protective-antigens (negative control). The use of one or few bacterial proteomes does not assess PVC conservation among strains, an important feature of vaccine antigens.ResultsWe present ReVac, which implements both a panoply of feature prediction programs without filtering out proteins, and scoring of candidates based on predictions made on curated positive and negative control PVCs datasets. ReVac surveys several genomes assessing protein conservation, as well as DNA and protein repeats, which may result in variable expression of PVCs. ReVac's orthologous clustering of conserved genes, identifies core and dispensable genome components. This is useful for determining the degree of conservation of PVCs among the population of isolates for a given pathogen. Potential vaccine candidates are then prioritized based on conservation and overall feature-based scoring. We present the application of ReVac, applied to 69 Moraxella catarrhalis and 270 non-typeable Haemophilus influenzae genomes, prioritizing 64 and 29 proteins as PVCs, respectively.ConclusionReVac's use of a scoring scheme ranks PVCs for subsequent experimental testing. It employs a redundancy-based approach in its predictions of features using several prediction tools. The protein's features are collated, and each protein is ranked based on the scoring scheme. Multi-genome analyses performed in ReVac allow for a comprehensive overview of PVCs from a pan-genome perspective, as an essential pre-requisite for any bacterial subunit vaccine design. ReVac prioritized PVCs of two human respiratory pathogens, identifying both novel and previously validated PVCs.

Project description:Crimean-Congo hemorrhagic fever (CCHF) is a viral disease caused by the Crimean-Congo hemorrhagic fever virus (CCHFV) of the Nairovirus genus. CCHF has occurred endemically in several regions of Africa, Southern Europe, and Central and Southeast Asia, with a case fatality rate of 5 to 80%. The World health organization enlisted CCHF as one of the top prioritized diseases for research and development in emergency contexts that making it a public health concern as no effective vaccine is available till date. Therefore, the present study aims to develop an effective multi-epitope subunit vaccine using immunoinformatics and reverse vaccinology approach against this virus. The B-cell and T-cell epitopes were predicted from structural and non-structural proteins, and filtered by immunogenicity, allergenicity, toxicity, conservancy, and cross-reactivity. The computational analysis revealed that the epitopes could induce an adequate immune response and had strong associations with their respective human leukocyte antigen (HLA) alleles with 98.94% of total world population coverage. Finally, the vaccine with 427 amino acids was constructed by connecting 8 cytotoxic T-lymphocytes, 4 helper T-lymphocytes, and 10 B-cell epitopes with appropriate linkers and β-defensin as an adjuvant. The antigenicity, allergenicity, solubility, and physiochemical properties of the vaccine were evaluated, followed by structural modelling, refinement, and validation. In addition, molecular docking and molecular dynamic simulations revealed a robust binding affinity and stability of the vaccine-immune receptor complex. Moreover, the codons were optimized for its higher expression in Escherichia coli (E. coli) K12 strain followed by in silico cloning. The proposed subunit vaccine developed in this study could be a potential candidate against CCHFV. However, further experimental validation is required to ensure the immunogenicity and safety profile of the proposed vaccine for combating and eradicating CCHFV.Supplementary informationThe online version contains supplementary material available at 10.1007/s10989-022-10430-0.

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A Computational Reverse Vaccinology Approach for the Design and Development of Multi-Epitopic Vaccine Against Avian Pathogen Mycoplasma gallisepticum

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