Abstract
In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ~4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets.
Highlights
The characterization of specific molecular targets for controlling and removing bacterial infections is an important and challenging task
We developed EpitoCore, a bioinformatic strategy that integrates surfaceome and subcellular localization prediction to pangenomic characterization, and further defines conserved epitopes in core proteins
Even though there were 201 genome entries for the species “Mycobacterium avium” in the Gene Assembly Summary of National Center for Biotechnology Information (NCBI) as of November 2018, we opted to perform antigenic analysis only for proteomes derived from strains with complete genomes sequenced
Summary
The characterization of specific molecular targets for controlling and removing bacterial infections is an important and challenging task. Surface proteins are key molecules for infection initiation and are at the interface with the host immune system [1]. They are underrepresented in many experimental studies due to the fact that transmembrane proteins are heterogeneous, hydrophobic, and often detected at low abundance [2]. In silico approaches became a desirable method for mining candidate antigenic proteins. It has been largely employed to characterize single or sets of sequences of interest [3,4,5]. Reverse vaccinology (RV) approaches use bacterial genomic information to achieve large scale antigen
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