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. 2007 Mar 30;320(1-2):143-54.
doi: 10.1016/j.jim.2006.12.011. Epub 2007 Jan 25.

Prediction of supertype-specific HLA class I binding peptides using support vector machines

Guang Lan Zhang  1 , Ivana Bozic, Chee Keong Kwoh, J Thomas August, Vladimir Brusic
Affiliations

Prediction of supertype-specific HLA class I binding peptides using support vector machines

Guang Lan Zhang et al. J Immunol Methods. .

Abstract

Experimental approaches for identifying T-cell epitopes are time-consuming, costly and not applicable to the large scale screening. Computer modeling methods can help to minimize the number of experiments required, enable a systematic scanning for candidate major histocompatibility complex (MHC) binding peptides and thus speed up vaccine development. We developed a prediction system based on a novel data representation of peptide/MHC interaction and support vector machines (SVM) for prediction of peptides that promiscuously bind to multiple Human Leukocyte Antigen (HLA, human MHC) alleles belonging to a HLA supertype. Ten-fold cross-validation results showed that the overall performance of SVM models is improved in comparison to our previously published methods based on hidden Markov models (HMM) and artificial neural networks (ANN), also confirmed by blind testing. At specificity 0.90, sensitivity values of SVM models were 0.90 and 0.92 for HLA-A2 and -A3 dataset respectively. Average area under the receiver operating curve (A(ROC)) of SVM models in blind testing are 0.89 and 0.92 for HLA-A2 and -A3 datasets. A(ROC) of HLA-A2 and -A3 SVM models were 0.94 and 0.95, validated using a full overlapping study of 9-mer peptides from human papillomavirus type 16 E6 and E7 proteins. In addition, a large-scale experimental dataset has been used to validate HLA-A2 and -A3 SVM models. The SVM prediction models were integrated into a web-based computational system MULTIPRED1, accessible at antigen.i2r.a-star.edu.sg/multipred1/.

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Figures

Fig. 1
Fig. 1
Sensitivity vs. specificity in 10-fold cross-validation on Dataset1 using SVM, ANN and HMM models.
Fig. 2
Fig. 2
Sensitivity vs. specificity in 10-fold cross-validation on Dataset2 using SVM, ANN and HMM models.
See this image and copyright information in PMC

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