title: Mature Epitope Density--a strategy for target selection based on immunoinformatics and exported prokaryotic proteins.
abstract: BACKGROUND
  Current immunological bioinformatic approaches focus on the prediction of allele-specific epitopes capable of triggering immunogenic activity. The prediction of major histocompatibility complex (MHC) class I epitopes is well studied, and various software solutions exist for this purpose. However, currently available tools do not account for the concentration of epitope products in the mature protein product and its relation to the reliability of target selection.
  
  
  RESULTS
  We developed a computational strategy based on measuring the epitope's concentration in the mature protein, called Mature Epitope Density (MED). Our method, though simple, is capable of identifying promising vaccine targets. Our online software implementation provides a computationally light and reliable analysis of bacterial exoproteins and their potential for vaccines or diagnosis projects against pathogenic organisms. We evaluated our computational approach by using the Mycobacterium tuberculosis (Mtb) H37Rv exoproteome as a gold standard model. A literature search was carried out on 60 out of 553 Mtb's predicted exoproteins, looking for previous experimental evidence concerning their possible antigenicity. Half of the 60 proteins were classified as highest scored by the MED statistic, while the other half were classified as lowest scored. Among the lowest scored proteins, ~13% were confirmed as not related to antigenicity or not contributing to the bacterial pathogenicity, and 70% of the highest scored proteins were confirmed as related. There was no experimental evidence of antigenic or pathogenic contributions for three of the highest MED-scored Mtb proteins. Hence, these three proteins could represent novel putative vaccine and drug targets for Mtb. A web version of MED is publicly available online at http://med.mmci.uni-saarland.de/.
  
  
  CONCLUSIONS
  The software presented here offers a practical and accurate method to identify potential vaccine and diagnosis candidates against pathogenic bacteria by "reading" results from well-established reverse vaccinology software in a novel way, considering the epitope's concentration in the mature portion of the protein.
authors.forename: Anderson R
authors.initials: AR
authors.lastname: Santos
authors.forename: Vanessa Bastos
authors.initials: VB
authors.lastname: Pereira
authors.forename: Eudes
authors.initials: E
authors.lastname: Barbosa
authors.forename: Jan
authors.initials: J
authors.lastname: Baumbach
authors.forename: Josch
authors.initials: J
authors.lastname: Pauling
authors.forename: Richard
authors.initials: R
authors.lastname: Röttger
authors.forename: Meritxell Zurita
authors.initials: MZ
authors.lastname: Turk
authors.forename: Artur
authors.initials: A
authors.lastname: Silva
authors.forename: Anderson
authors.initials: A
authors.lastname: Miyoshi
authors.forename: Vasco
authors.initials: V
authors.lastname: Azevedo
mesh_terms: D000483:Alleles; D000942:Antigens, Bacterial / Q000737:chemistry / Q000276:immunology; D001426:Bacterial Proteins / Q000276:immunology* / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D000939:Epitopes / Q000737:chemistry* / Q000276:immunology*; D008285:Major Histocompatibility Complex / Q000235:genetics; D009169:Mycobacterium tuberculosis / Q000737:chemistry / Q000276:immunology; D021381:Protein Transport; D012372:ROC Curve; D012984:Software; D014612:Vaccines / Q000737:chemistry / Q000276:immunology*
chemical_list: D000942:Antigens, Bacterial; D001426:Bacterial Proteins; D000939:Epitopes; D014612:Vaccines