title: Consensus classification of human leukocyte antigen class II proteins.
abstract: 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                                    .
authors.affiliation: Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Warsaw, Poland.
authors.forename: Indrajit
authors.initials: I
authors.lastname: Saha
authors.forename: Giovanni
authors.initials: G
authors.lastname: Mazzocco
authors.forename: Dariusz
authors.initials: D
authors.lastname: Plewczynski
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D000949:Histocompatibility Antigens Class II / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D006801:Humans; D010455:Peptides / Q000378:metabolism; D010802:Phylogeny; D011485:Protein Binding / Q000276:immunology; D015203:Reproducibility of Results
chemical_list: D000949:Histocompatibility Antigens Class II; D010455:Peptides