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

Open access
Jensen, Kamilla Kjærgaard, Andreatta, Massimo, Marcatili, Paolo, Buus, Søren, Greenbaum, Jason A., Yan, Zhen, Sette, Alessandro, Peters, Bjoern, Nielsen, Morten·2018·Immunology

Abstract

Summary: 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, Net MHCII and Net MHCII pan. 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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Authors
Jensen, K. K., Andreatta, M., Marcatili, P., Buus, S., Greenbaum, J. A., Yan, Z., Sette, A., Peters, B., & Nielsen, M.
Venue
Immunology
Publisher
Wiley
Published
2018-02-06
Volume
154
Issue
3
Pages
394-406
ISSN
  • 0019-2805 (ISSN-L)
  • 1365-2567
OpenAlex
W2783102765
Cited by
794
References
0
FWCI
26.13
Type
journal article
Language
en
Open access
bronze

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