Consensus classification of human leukocyte antigen class II proteins

Indrajit Saha , Giovanni Mazzocco , Dariusz Plewczyński

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. © 2012 The Author(s).

Author Indrajit Saha - [Uniwersytetu Warszawskiego, Interdyscyplinarne Centrum Modelowania Matematycznego I Komputerowego]
Indrajit Saha,,
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, Giovanni Mazzocco - [Uniwersytetu Warszawskiego, Interdyscyplinarne Centrum Modelowania Matematycznego I Komputerowego]
Giovanni Mazzocco,,
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, Dariusz Plewczyński (FMIS / DIPS)
Dariusz Plewczyński,,
- Department of Information Processing Systems
Journal seriesImmunogenetics, ISSN 0093-7711, e-ISSN 1432-1211
Issue year2013
Vol65
Pages97-105
ASJC Classification1311 Genetics; 2403 Immunology
DOIDOI:10.1007/s00251-012-0665-6
Languageen angielski
Score (nominal)25
Score sourcejournalList
ScoreMinisterial score = 25.0, 04-06-2020, ArticleFromJournal
Ministerial score (2013-2016) = 25.0, 04-06-2020, ArticleFromJournal
Publication indicators Scopus Citations = 11; WoS Citations = 8; Scopus SNIP (Source Normalised Impact per Paper): 2013 = 0.768; WoS Impact Factor: 2013 = 2.488 (2) - 2013=2.466 (5)
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