Evaluation of MHC Class I Peptide Binding Prediction Servers: Applications for Vaccine Research

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Evaluation of MHC Class I Peptide Binding Prediction Servers: Applications for Vaccine Research

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Title: Evaluation of MHC Class I Peptide Binding Prediction Servers: Applications for Vaccine Research
Author: Ray, Surajit; Tongchusak, Songsak; Lin, Hong Huang; Reinherz, Ellis Leonard; Brusic, Vladimir

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Citation: Lin, Hong Huang, Surajit Ray, Songsak Tongchusak, Ellis L. Reinherz, and Vladimir Brusic. 2008. Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research. BMC Immunology 9: 8.
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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.
Published Version: doi:10.1186/1471-2172-9-8
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2323361/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:5310900
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