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NN-align. A neural network-based alignment algorithm for MHC class II peptide binding prediction.
Nielsen M and Lund O

Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark

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 intercellular digested proteins to cytotoxic T cells and MHC class II molecules simulate cellular and humoral immunity though presentation of extra-cellularly 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.

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. The method is evaluated on a large-scale benchmark consisting of four 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 like TEPITOPE, NetMHCIIpan and SMM-align. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy.

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

PMID: 19765293

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Morten Nielsen,