%0 Journal Article
%T NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
%A Morten Nielsen
%A Ole Lund
%J BMC Bioinformatics
%D 2009
%I BioMed Central
%R 10.1186/1471-2105-10-296
%X Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods.The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0 webcite.Major histocompatibility complex (MHC) molecules play an essential role in host-pathogen interactions determining the onset and outcome of many host immune responses. Only a small fraction of the possible peptides that can be generated from proteins of pathogenic organisms actually generate an immune response. MHC class II molecules present peptides derived from proteins taken up from the extracellular environment. They stimulate cellular and humoral immunity against pathogenic microorganisms through the actions of helper T lymphocytes. In order for a peptide to stimulate a helper T lymphocyte response, it must bind MHC II in the endocytic organelles [1].The MHC class I molecule is highly specific and binds a limited set of peptides of a narrow length distribution [2]. In contrast to this, the MHC class II molecule is highly promiscuous both with respect to composition and length of the peptide ligands [3,4]. During the last decade, large efforts have been invested in developing methods to allow for in silico screening of pathogenic organisms with the purpose of identifying peptides that will
%U http://www.biomedcentral.com/1471-2105/10/296