An assessment on epitope prediction methods for protozoa genomes.
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2012
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Background: Epitope prediction using computational methods represents one of the most promising approaches
to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key
points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely
spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this
pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated
parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight
the predictive performances of several algorithms that were evaluated through the development of a MySQL
database built with the purpose of: a) evaluating individual algorithms prediction performances and their
combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under
Curve) performance and a threshold dependent method that employs a confusion matrix; b) integrating data from
experimentally validated and in silico predicted epitopes; and c) integrating the subcellular localization predictions
and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope
prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against
trypanosomatid genomes.
Results: A database-driven epitope prediction method was developed with built-in functions that were capable of:
a) removing experimental data redundancy; b) parsing algorithms predictions and storage experimental validated
and predict data; and c) evaluating algorithm performances. Results show that a better performance is achieved
when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the
combined prediction of AAP12 and BCPred12 reached an AUC value of 0.77. For T CD8+ epitope predictors, the
combined prediction of NetCTL and NetMHC reached an AUC value of 0.64. Finally, regarding the subcellular
localization prediction, the best performance is achieved when the combined prediction of Sigcleave, TargetP and
WoLF PSORT is used.
Conclusions: Our study indicates that the combination of B cells epitope predictors is the best tool for predicting
epitopes on protozoan parasites proteins. Regarding subcellular localization, the best result was obtained when the
three algorithms predictions were combined. The developed pipeline is available upon request to authors.
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RESENDE, D. M. et al. An assessment on epitope prediction methods for protozoa genomes. Bmc Bioinformatics, v. 13, p.309-322, 2012. Disponível em: <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-309>. Acesso em: 10 out. 2016.