Vaccine Development Through Reverse Vaccinology Using Artificial Intelligence and Machine Learning Approach

Bhakta, Swarnav

doi:10.1016/b978-0-323-85844-1.00006-4

Cited by 1 publication

(1 citation statement)

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“…Designing a potential vaccine against S. sonnei is an effective way to prevent this disease. The availability of mass genomic and proteomic data, and recent advancements in the fields of immunoinformatics and bioinformatics have greatly facilitated the design of safe and highly effective vaccines against particular pathogens [23]. In the present study, vaccinomics-based immunoinformatics strategies were employed to identify potential vaccine candidate proteins in S. sonnei proteome, followed by designing a multi-epitope-based vaccine construct using reverse vaccinology techniques.…”

Section: Introductionmentioning

confidence: 99%

Vaccinomics-aided next-generation novel multi-epitope-based vaccine engineering against multidrug resistant Shigella Sonnei: Immunoinformatics and chemoinformatics approaches

Aiman,

Ahmad,

Khan

et al. 2023

PLoS ONE

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Shigella sonnei is a gram-negative bacterium and is the primary cause of shigellosis in advanced countries. An exceptional rise in the prevalence of the disease has been reported in Asia, the Middle East, and Latin America. To date, no preventive vaccine is available against S. sonnei infections. This pathogen has shown resistances towards both first- and second-line antibiotics. Therefore, an effective broad spectrum vaccine development against shigellosis is indispensable. In the present study, vaccinomics-aided immunoinformatics strategies were pursued to identify potential vaccine candidates from the S. sonnei whole proteome data. Pathogen essential proteins that are non-homologous to human and human gut microbiome proteome set, are feasible candidates for this purpose. Three antigenic outer membrane proteins were prioritized to predict lead epitopes based on reverse vaccinology approach. Multi-epitope-based chimeric vaccines was designed using lead B- and T-cell epitopes combined with suitable linker and adjuvant peptide sequences to enhance immune responses against the designed vaccine. The SS-MEVC construct was prioritized based on multiple physicochemical, immunological properties, and immune-receptors docking scores. Immune simulation analysis predicted strong immunogenic response capability of the designed vaccine construct. The Molecular dynamic simulations analysis ensured stable molecular interactions of lead vaccine construct with the host receptors. In silico restriction and cloning analysis predicted feasible cloning capability of the SS-MEVC construct within the E. coli expression system. The proposed vaccine construct is predicted to be more safe, effective and capable of inducing robust immune responses against S. sonnei infections and may be worthy of examination via in vitro/in vivo assays.

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Section: Introductionmentioning

confidence: 99%