Improved methods for predicting peptide binding affinity to MHC class II molecules |
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| Authors: | Kamilla Kjærgaard Jensen Massimo Andreatta Paolo Marcatili Søren Buus Jason A. Greenbaum Zhen Yan Alessandro Sette Bjoern Peters Morten Nielsen |
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| Affiliation: | 1. Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark;2. Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina;3. Department of Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark;4. Bioinformatics Core Facility, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA;5. Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA;6. Department of Medicine, University of California San Diego, La Jolla, CA, USA |
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| Abstract: | 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 . |
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| Keywords: | affinity predictions immunogenic peptides MHC binding specificity peptide– MHC binding T‐cell epitope |
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