title: Immunoinformatic evaluation of multiple epitope ensembles as vaccine candidates: E coli 536.
abstract: Epitope prediction is becoming a key tool for vaccine discovery. Prospective analysis of bacterial and viral genomes can identify antigenic epitopes encoded within individual genes that may act as effective vaccines against specific pathogens. Since B-cell epitope prediction remains unreliable, we concentrate on T-cell epitopes, peptides which bind with high affinity to Major Histacompatibility Complexes (MHC). In this report, we evaluate the veracity of identified T-cell epitope ensembles, as generated by a cascade of predictive algorithms (SignalP, Vaxijen, MHCPred, IDEB, EpiJen), as a candidate vaccine against the model pathogen uropathogenic gram negative bacteria Escherichia coli (E-coli) strain 536 (O6:K15:H31). An immunoinformatic approach was used to identify 23 epitopes within the E-coli proteome. These epitopes constitute the most promiscuous antigenic sequences that bind across more than one HLA allele with high affinity (IC50 < 50nM). The reliability of software programmes used, polymorphic nature of genes encoding MHC and what this means for population coverage of this potential vaccine are discussed.
authors.affiliation: Aston Pharmacy School, Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK.
authors.forename: Jade
authors.initials: J
authors.lastname: Rai
authors.forename: Ka In
authors.initials: KI
authors.lastname: Lok
authors.forename: Chun Yin
authors.initials: CY
authors.lastname: Mok
authors.forename: Harvinder
authors.initials: H
authors.lastname: Mann
authors.forename: Mohammed
authors.initials: M
authors.lastname: Noor
authors.forename: Pritesh
authors.initials: P
authors.lastname: Patel
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower