title: NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction.
abstract: BACKGROUND
  The major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark. mniel@cbs.dtu.dk
authors.forename: Morten
authors.initials: M
authors.lastname: Nielsen
authors.forename: Ole
authors.initials: O
authors.lastname: Lund
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D000949:Histocompatibility Antigens Class II / Q000737:chemistry* / Q000378:metabolism; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism*; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D000949:Histocompatibility Antigens Class II; D010455:Peptides