Affiliations: Dormer-Owusu BioInstitute, Inc. / Washington Adventist
University, Takoma Park, MD, USA
Note: [] Corresponding author: Anton Dormer, Washington Adventist
University, 7600 Flower Avenue, Takoma Park, MD 20912, USA. Fax: +1 425 945
9405; E-mail: [email protected]
Abstract: The application of in silico tools for the development of
T-cell vaccines is crucial. Yet, due to myriad of polymorphisms of human
T-lymphocytic antigen challenges, such therapeutic opportunities present unique
roadblocks. There is an obvious advantage in using immunoinformatics (i.e.,
significantly decreasing cost related to laboratory expenses). A previous
publication looked at random binding and nonbinding peptides in order to test
the practicality of using such in silico tools to obtain possible
immunogenic peptides. The present in silico study applied the same basic
approaches to an applicable problem that was to identify promiscuous peptide
vaccine candidates for hepatitis C virus (HCV) infection. The data sets used,
included the proteins HCV E1, E2 and P7 as the binders (non-self antigens) and
the GAD65 and ICA69, which have an association with diabetes, as non-binders
(self-antigens). The in silico tools utilized were ProPred, MHC2PRED, and
RANKPEP. The resulting differences were identifiable in each of the statistical
parameters examined. Variations in the outcomes were evident by the
dissimilarities found among the major indices of evaluation Sensitivity,
Specificity, Accuracy, Positive Predictive Value (PPV), Negative Predictive
Value (NPV) and Matthews's correlation coefficient (MCC) of the percentages of
the predicted promiscuous peptides to HLA-DRB1*0101, *0301, and *0401. The
conclusion from this study indicates that more work needs to be done in order
to enhance the predictability of programs for the identification of peptide
vaccine candidates for HCV. Such programs should not be solely relied upon
without in vitro assay verification.
