NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

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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 seattle seahawks socks 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 silbrade 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.Conclusion 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.

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