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

Kamilla Kjærgaard Jensen; Massimo Andreatta; Paolo Marcatili; Søren Buus; Jason A. Greenbaum; Zhen Yan; Alessandro Sette; Bjoern Peters; Morten Nielsen

doi:10.1111/imm.12889

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

Kamilla Kjærgaard Jensen, Massimo Andreatta, Paolo Marcatili, Søren Buus, Jason A. Greenbaum, Zhen Yan, Alessandro Sette, Bjoern Peters, Morten Nielsen^*

^*Corresponding author for this work

217 Citations (Scopus)

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.

Original language	English
Journal	Immunology
Volume	154
Issue number	3
Pages (from-to)	394-406
ISSN	0019-2805
DOIs	https://doi.org/10.1111/imm.12889
Publication status	Published - Jul 2018

Keywords

Affinity predictions
Immunogenic peptides
MHC binding specificity
Peptide-MHC binding
T-cell epitope

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title = "Improved methods for predicting peptide binding affinity to MHC class II molecules",

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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T1 - Improved methods for predicting peptide binding affinity to MHC class II molecules

AU - Jensen, Kamilla Kjærgaard

AU - Andreatta, Massimo

AU - Marcatili, Paolo

AU - Buus, Søren

AU - Greenbaum, Jason A.

AU - Yan, Zhen

AU - Sette, Alessandro

AU - Peters, Bjoern

AU - Nielsen, Morten

PY - 2018/7

Y1 - 2018/7

N2 - 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.

AB - 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.

KW - Affinity predictions

KW - Immunogenic peptides

KW - MHC binding specificity

KW - Peptide-MHC binding

KW - T-cell epitope

U2 - 10.1111/imm.12889

DO - 10.1111/imm.12889

M3 - Journal article

C2 - 29315598

AN - SCOPUS:85041595230

SN - 0019-2805

VL - 154

SP - 394

EP - 406

JO - Immunology

JF - Immunology

IS - 3

ER -

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

Abstract

Keywords

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