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Open Access Highly Accessed Research article

Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research

Hong Huang Lin1, Surajit Ray2, Songsak Tongchusak1, Ellis L Reinherz1 and Vladimir Brusic13*

Author affiliations

1 Cancer Vaccine Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

2 Department of Mathematics and Statistics, Boston University, Boston, MA, USA

3 School of Land, Crop and Food Sciences, University of Queensland, Brisbane, Australia

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Citation and License

BMC Immunology 2008, 9:8  doi:10.1186/1471-2172-9-8

Published: 16 March 2008

Abstract

Background

Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules.

Results

Of 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201).

Conclusion

Non-linear predictors outperform matrix-based predictors. Most predictors can be improved by non-linear transformations of their raw prediction scores. The best predictors of peptide binding are also best in prediction of T-cell epitopes. We propose a new standard for MHC-I binding prediction – a common scale for normalization of prediction scores, applicable to both experimental and predicted data. The results of this study provide assistance to researchers in selection of most adequate prediction tools and selection criteria that suit the needs of their projects.