Improved methods for predicting peptide binding affinity to MHC class II molecules.
Jensen, K. K., Andreatta, M., Marcatili, P., Buus, S., Greenbaum, J. A., Yan, Z., Sette, A., Peters, B. and Nielsen, M.
Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Lyngby, Denmark.
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, 1650, San Martin, Buenos Aires, Argentina.
Department of Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen, Denmark.
Bioinformatics Core Facility, La Jolla Institute for Allergy and Immunology, La Jolla, CA, 92037, USA.
Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, 92037, USA.
University of California San Diego, Department of Medicine, La Jolla, CA, 92037, USA.
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 class 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 and This article is protected by copyright. All rights reserved.
Immunology 154(3): 394-406 (2018)