Introduction to the Peptide Binding Problem of Computational Immunology : New Results

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

14 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)951-984
Journal / PublicationFoundations of Computational Mathematics
Volume14
Issue number5
Online published17 Sep 2013
Publication statusPublished - Oct 2014

Abstract

We attempt to establish geometrical methods for amino acid sequences. To measure the similarities of these sequences, a kernel on strings is defined using only the sequence structure and a good amino acid substitution matrix (e.g. BLOSUM62). The kernel is used in learning machines to predict binding affinities of peptides to human leukocyte antigen DR (HLA-DR) molecules. On both fixed allele (Nielsen and Lund in BMC Bioinform. 10:296, 2009) and pan-allele (Nielsen et al. in Immunome Res. 6(1):9, 2010) benchmark databases, our algorithm achieves the state-of-the-art performance. The kernel is also used to define a distance on an HLA-DR allele set based on which a clustering analysis precisely recovers the serotype classifications assigned by WHO (Holdsworth et al. in Tissue Antigens 73(2):95–170, 2009; Marsh et al. in Tissue Antigens 75(4):291–455, 2010). These results suggest that our kernel relates well the sequence structure of both peptides and HLA-DR molecules to their biological functions, and that it offers a simple, powerful and promising methodology to immunology and amino acid sequence studies.

Research Area(s)

  • String kernel, Peptide binding prediction, Reproducing kernel Hilbert space, Major histocompatibility complex, HLA DRB allele classification