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This article is part of the supplement: Proceedings of the 8th International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-meeting 2012)

Open Access Research

Mature Epitope Density - A strategy for target selection based on immunoinformatics and exported prokaryotic proteins

Anderson R Santos15, Vanessa Bastos Pereira1, Eudes Barbosa12, Jan Baumbach2, Josch Pauling24, Richard Röttger24, Meritxell Zurita Turk1, Artur Silva3, Anderson Miyoshi1 and Vasco Azevedo1*

Author Affiliations

1 Molecular and Cellular Genetics Laboratory, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

2 Computational Biology Research Group, Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej, Odense M, Denmark

3 DNA Polymorphism Laboratory, Universidade Federal do Pará, Campus do Guamá, Belém, Pará, Brazil

4 Computational Systems Biology group, Max Planck Institute for Informatics, Campus E2.1, Saarbrücken, Germany

5 Faculty of Computing, Universidade Federal de Uberlândia, Campus Santa Mônica, Uberlândia, Minas Gerais, Brazil

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BMC Genomics 2013, 14(Suppl 6):S4  doi:10.1186/1471-2164-14-S6-S4

Published: 25 October 2013

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/ webcite.

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.