Improved methods for predicting peptide binding affinity to MHC class II molecules

JENSEN, KAMILLA KJÆRGAARD; ANDREATTA, MASSIMO; MARCATILI, PAOLO; BUUS, SØREN; GREENBAUM, JASON A.; YAN, ZHEN; SETTE, ALESSANDRO; PETERS, BJOERN; NIELSEN, MORTEN

IMMUNOLOGY

WILEY-BLACKWELL PUBLISHING, INC

Año: 2018

0019-2805

Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.