Identification of vaccine targets in pathogens and design of a vaccine using computational approaches

Kamal Rawal,Bilal Ahmed Abbasi,P Preeti,Trapti Sharma,Shriya Sood,Astha Mishra,Aman Dubey ,Peter J Hotez,Swarsat Kaushik Nath,Devansh Saraf,Sandeep K Gupta ,Pranjay Gupta,Kartik Mishra,Robin Sinha,Priya Kumari,Amit Chaudhary,Ulrich Strych,Shachee Singh,Preeti Subramani,Prashant Kumar Singh ,María Elena Bottazzi

doi:10.1038/s41598-021-96863-x

Abstract

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

Highlights

IntroductionComputational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information
Antigen identification is an important step in the vaccine development process
A Potential Vaccine Candidate (PVC) could be defined as the protein or corresponding DNA/RNA sequence that possesses properties of an “ideal vaccine” such as nonhomology with the host proteins to avoid the generation of a potential autoimmune response106, the lack of transmembrane regions to facilitate expression, antigenicity, adhesion-like properties, immunogenicity, a molecular weight of < 110 kDa, non-homology with the gut flora proteome, surface-exposure/secretion, and the presence of anchoring and/or secretion signals

Summary

Introduction

Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Several computational studies have analysed genomes or proteomes of individual pathogenic strains or species to predict vaccine c andidates. Several computational studies have analysed genomes or proteomes of individual pathogenic strains or species to predict vaccine c andidates5–10 In one of these studies, researchers have used the protein–protein interaction dataset and a network biology approach to prioritize vaccine targets for Borrelia burgdorferi. There is an urgent need for building pipelines or computational frameworks, to integrate diverse algorithms and databases using a single input and provide meaningful results for researchers working on vaccine development

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