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Journal article · Ahead of Print article

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

From

Department of Bio and Health Informatics, Technical University of Denmark1

Immunoinformatics and Machine Learning, Department of Bio and Health Informatics, Technical University of Denmark2

Universidad Nacional de San Martin3

University of Copenhagen4

La Jolla Institute for Allergy & Immunology5

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 www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2. This article is protected by copyright. All rights reserved.

Language: English
Publisher: John Wiley and Sons Inc.
Year: 2018
Pages: 394-406
ISSN: 13652567 , 00192805 and 09534954
Types: Journal article and Ahead of Print article
DOI: 10.1111/imm.12889
ORCIDs: Jensen, Kamilla Kjærgaard , 0000-0002-8036-2647 , Nielsen, Morten , 0000-0001-8363-1999 and Marcatili, Paolo

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