title: Prediction of supertype-specific HLA class I binding peptides using support vector machines.
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/.
authors.affiliation: Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.
authors.forename: Guang Lan
authors.initials: GL
authors.lastname: Zhang
authors.forename: Ivana
authors.initials: I
authors.lastname: Bozic
authors.forename: Chee Keong
authors.initials: CK
authors.lastname: Kwoh
authors.forename: J Thomas
authors.initials: JT
authors.lastname: August
authors.forename: Vladimir
authors.initials: V
authors.lastname: Brusic
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005544:Forecasting; D015395:Histocompatibility Antigens Class I / Q000276:immunology*; D009856:Oncogene Proteins, Viral / Q000276:immunology; D050725:Papillomavirus E7 Proteins / Q000276:immunology; D010455:Peptides / Q000276:immunology* / Q000378:metabolism; D011485:Protein Binding; D012680:Sensitivity and Specificity; D012986:Software Validation
chemical_list: C118860:E6 protein, Human papillomavirus type 6; D015395:Histocompatibility Antigens Class I; D009856:Oncogene Proteins, Viral; D050725:Papillomavirus E7 Proteins; D010455:Peptides