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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Abbreviations
- HLA:
-
Human leukocyte antigen
- MHC:
-
Major histocompatibility complex
- SVM:
-
Support vector machine
- ANN:
-
Artificial neural networks
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Acknowledgments
The authors are thankful to Dr. Balvinder Singh, Institute of Microbial Technology, Chandigarh for valuable suggestions and discussions and Priya Gowthaman for her help in manuscript preparation. UG is a recipient of Junior Research Fellowship of Department of Biotechnology. CSB is a recipient of Junior Research Fellowship of Council of Scientific and Industrial Research. The authors also wish to thank Department of Biotechnology and Council of Scientific and Industrial Research, India for financial support.
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U. Gowthaman and S. B. Chodisetti have contributed equally.
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726_2010_579_MOESM1_ESM.pdf
Variability in the prediction efficiency for different alleles in a single program. Matthew’s correlation coefficients for different alleles from a single program is depicted as bar diagrams for Rankpep (A), ComPred (B), NetMHC (C), NetMHCPan (D), ARB-Matrix (E), IEDB-ANN (F), SVMHC-SYFPEITHI (G), SVMHC-MHCPep (H) (PDF 33 kb)
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Gowthaman, U., Chodisetti, S.B., Parihar, P. et al. Evaluation of different generic in silico methods for predicting HLA class I binding peptide vaccine candidates using a reverse approach. Amino Acids 39, 1333–1342 (2010). https://doi.org/10.1007/s00726-010-0579-2
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DOI: https://doi.org/10.1007/s00726-010-0579-2