Consensus classification of human leukocyte antigen class II proteins

Class II human leukocyte antigens (HLA II) are proteins involved in the human immunological adaptive response by binding and exposing some pre-processed, non-self peptides in the extracellular domain in order to make them recognizable by the CD4+ T lymphocytes. However, the understanding of HLA–pept...

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Veröffentlicht in:Immunogenetics (New York) 2013-02, Vol.65 (2), p.97-105
Hauptverfasser: Saha, Indrajit, Mazzocco, Giovanni, Plewczynski, Dariusz
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Plewczynski, Dariusz
description Class II human leukocyte antigens (HLA II) are proteins involved in the human immunological adaptive response by binding and exposing some pre-processed, non-self peptides in the extracellular domain in order to make them recognizable by the CD4+ T lymphocytes. However, the understanding of HLA–peptide binding interaction is a crucial step for designing a peptide-based vaccine because the high rate of polymorphisms in HLA class II molecules creates a big challenge, even though the HLA II proteins can be grouped into supertypes, where members of different class bind a similar pool of peptides. Hence, first we performed the supertype classification of 27 HLA II proteins using their binding affinities and structural-based linear motifs to create a stable group of supertypes. For this purpose, a well-known clustering method was used, and then, a consensus was built to find the stable groups and to show the functional and structural correlation of HLA II proteins. Thus, the overlap of the binding events was measured, confirming a large promiscuity within the HLA II–peptide interactions. Moreover, a very low rate of locus-specific binding events was observed for the HLA-DP genetic locus, suggesting a different binding selectivity of these proteins with respect to HLA-DR and HLA-DQ proteins. Secondly, a predictor based on a support vector machine (SVM) classifier was designed to recognize HLA II-binding peptides. The efficiency of prediction was estimated using precision, recall (sensitivity), specificity, accuracy, F-measure, and area under the ROC curve values of random subsampled dataset in comparison with other supervised classifiers. Also the leave-one-out cross-validation was performed to establish the efficiency of the predictor. The availability of HLA II–peptide interaction dataset, HLA II-binding motifs, high-quality amino acid indices, peptide dataset for SVM training, and MATLAB code of the predictor is available at http://sysbio.icm.edu.pl/HLA .
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Moreover, a very low rate of locus-specific binding events was observed for the HLA-DP genetic locus, suggesting a different binding selectivity of these proteins with respect to HLA-DR and HLA-DQ proteins. Secondly, a predictor based on a support vector machine (SVM) classifier was designed to recognize HLA II-binding peptides. The efficiency of prediction was estimated using precision, recall (sensitivity), specificity, accuracy, F-measure, and area under the ROC curve values of random subsampled dataset in comparison with other supervised classifiers. Also the leave-one-out cross-validation was performed to establish the efficiency of the predictor. 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subjects Algorithms
Allergology
Amino acids
Antigen presentation
Binding Sites
Biomedical and Life Sciences
Biomedicine
CD4 antigen
Cell Biology
Classification
Cluster Analysis
Computational Biology - methods
Datasets
Gene Function
Gene loci
Gene polymorphism
Histocompatibility antigen HLA
Histocompatibility Antigens Class II - classification
Histocompatibility Antigens Class II - genetics
Histocompatibility Antigens Class II - metabolism
Human Genetics
Humans
Immunology
Leukocytes
Lymphocytes
Lymphocytes T
Original Paper
Peptides
Peptides - metabolism
Phylogeny
Polymorphism
Protein Binding - immunology
Proteins
Reproducibility of Results
Structure-function relationships
Support vector machines
T cell receptors
Vaccines
title Consensus classification of human leukocyte antigen class II proteins
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