ReVac: a reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidates

Abstract

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.

In Silico Analyses of Extracellular Proteins of Acinetobacter baumannii as Immunogenic Candidates

“…In terms of vaccinology, an ideal or promising immunogenic candidate should be exposed on the bacterial surface (OM or extracellular proteins) for host immune system induction, have a high antigenicity value, be conserved across circulating strains, and be expressed during bacterial infection. In addition, the ideal immunogenic candidates should have an important role in bacterial pathogenesis (28). In the case of A. baumannii, most studies focused on outer membrane proteins (OMPs), such as OmpA, OmpW, and Omp22 (29), and extracellular proteins were underestimated.…”

Role of artificial intelligence in peptide vaccine design against RNA viruses

“…1 ). This utilizes bioinformatics tools to design vaccines based on the genetic sequences of the organism [ 52 ]. Multi epitope based peptide (MEBP) vaccine candidates have been designed recently using this Immunoinformatics approach for RNA viruses such as zika, nipah and SARS-COV-2 [ [53] , [54] , [55] ] by studying immunogenetics and immunology data using bioinformatics.…”

Reverse vaccinology approach for multi-epitope centered vaccine design against delta variant of the SARS-CoV-2

“…On the contrary, the development in computational biology and bioinformatics approaches has led the swift design of useful constructs, reducing the conventional laboratory-based experimentations (Sharma et al 2021 ). One of the widely used computational approaches for the development of vaccine model is “Reverse Vaccinology” (D’Mello et al 2019 ). It enables vaccine construction and designing based on the organism genome sequence information without the need to grow pathogens.…”