NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ - PubMed (original) (raw)
NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ
Edita Karosiene et al. Immunogenetics. 2013 Oct.
Abstract
Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide-MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0 .
Figures
Fig. 1
Superimposition of HLA-DR, HLA-DP and HLA-DQ alpha chains. HLA-DR alpha chain (PDB ID: 1A6A Ghosh et al. 1995) is shown in yellow and was used as a reference chain. HLA-DP chain (PDB ID: 3LQZ Dai et al. 2010) is shown in green, HLA-DQ chain without a gap (PDB ID: 1JK8 Lee et al. 2001) is shown in orange and HLA-DQ chain with a gap (PDB ID: 1S9V Kim et al. 2004) is shown in blue. The area affected by the deletion in DQA sequence is circled
Fig. 2
Part of sequence alignments of HLA-DQ alpha chains to HLA-DR reference sequence of HLA-DRA1*0101 molecule. a Sequence alignments of HLA-DQ sequences with gaps, b demonstrates the alignment of other HLA-DQ molecules to the same reference sequence. Reference sequence and the position corresponding to the insertion are marked in bold. The alignments were visualized using ClustalW (Larkin et al. 2007)
Fig. 3
Interaction map between the peptide and MHC class II pseudo sequence. The columns give the MHC position numbering separately for alpha and beta chains and refer to HLA-DR. The rows show peptide binding core positions. Red squares marking interaction between a particular position of the peptide and MHC define contacts between corresponding two residues
Fig. 4
Comparison of the method performance when trained on perlocus data and cross-loci data. Average PCC and average AUC values for each locus are demonstrated on the left and right panel, respectively. Significant p values are given above the bars for corresponding loci. The difference in predictive performance between the per-locus and cross-loci training is significant only for HLA-DQ when measuring AUC performance values
Fig. 5
Leave-one-out results for the _NetMHCIIpan_-3.0 method in comparison with the _NN_-finder approach. Average performance measures in terms of PCC and AUC are given in the left and right panel, respectively. Significant p values are given above the bars for corresponding loci (not available for H-2 locus)
Fig. 6
Predictive performance of the _NetMHCIIpan_-3.0 method for the molecules from our data set as a function of distance to the nearest neighbour. The performance was obtained using LOO setup as explained in the “Materials and methods” section. The distance to the nearest neighbour was calculated as described by Nielsen at al. (2008). The solid line represents the least square fit for the data
Fig. 7
Functional clustering of the 72 HLA molecules from the European population. HLA-DR molecules are displayed in red, HLA-DP molecules are displayed in green and HLA-DQ molecules are shown in blue. Sequence logos showing the binding motif are presented for selected molecules representing the different specificity groups
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