Evaluation of different generic in silico methods for predicting HLA class I binding peptide vaccine candidates using a reverse approach |
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Authors: | Uthaman Gowthaman Sathi Babu Chodisetti Pankaj Parihar Javed N Agrewala |
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Institution: | (1) Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India; |
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Abstract: | Since CD8+ T cell response is crucial to combat intracellular infections and cancer, identification of class I HLA binding peptides
is of immense clinical value. The experimental identification of such peptides is protracted and laborious. Exploiting in
silico tools to discover such peptides is an attractive alternative. However, this approach needs a thorough assessment before
its elaborate application. We have adopted a reverse approach to evaluate the reliability of eight different servers (inclusive
of 55 predictors) by exploiting experimentally proven data. A comprehensive data set of more than 960 peptides was employed
to test the efficacy of the programs. We have validated commonly used strategies to predict peptides that bind to seven most
prevalent HLA class I alleles. We conclude that four of the eight servers are more adept in predictions. Although the overall
predictions for class I MHC binders were superior to class II MHC binders, individual predictors for different alleles belonging
to the same program were highly variable in their efficiencies. We have also addressed whether a consensus approach can yield
better prediction efficiency. We observed that combining the results from different in silico programs could not increase
the efficiency significantly. |
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