Figure 1.
Schematic Illustration of the NetMHCIIpan Method.
(A) The HLA-DR pseudo sequence is constructed from polymorphic HLA-DR residues in potential contact with a bound peptide. (B) Position specific scoring matrix (PSSM) and peptide core alignment (shown in red) is made for each allele using the SMM-align method [26]. N and C terminal peptide flanking regions, PFR, are identified as the up to three amino acids flanking the peptide-binding core. (C) Suboptimal peptides are presented to the NetMHCpan method with binding values normalized to the optimal peptide score (for the peptide shown in red) as described in Materials and Methods. (D) The NetMHCIIpan method is trained integrating data from all alleles. Input to the artificial neural network training includes the peptide core, composition and length of the N and C terminal PFR, length of the source peptide as well as the normalized binding affinity value (for details see Materials and Methods).
Table 1.
Leave-One-Molecule-Out Benchmark Results in Terms of the AUC and Pearson's Correlation Values.
Figure 2.
Predictive Performance in Terms of the Pearson's Correlation of the LOO Pan-Specific Method as a Function of the Distance to Its Nearest Neighbor HLA-DR Allele.
The nearest neighbor distance is estimated as described in Materials and Methods.
Figure 3.
Cross-Validation Benchmark Evaluation.
The predictive performance of the pan-specific, SMM-align, and TEPITOPE methods compared in terms of the Pearson's correlation and AUC values averaged over the 11 alleles covered by the TEPITOPE method, respectively (data for the individual alleles is given in Table S2).
Table 2.
Prospective Validation Using an Hitherto Uncharacterized HLA Molecule.
Figure 4.
Prediction of Endogenously Presented Peptides.
The benchmark data set consists of 584 HLA-DR restricted ligands covering 28 HLA-DR alleles downloaded from the SYFPEITHI database as described in the text. For alleles not covered by the TEPITOPE method, the closest allele covered by the TEPITOPE method as identified by sequence similarity between the HLA pseudo-sequences is used. TEPITOPE Alleles give the average AUC performance over the 17 alleles covered by the TEPITOPE method, and non-TEPITOPE Alleles give the average AUC performance over the 11 alleles not covered by the TEPITOPE method (data for the individual alleles is given in Table S3).
Table 3.
Identification of Peptide Binding Cores.
Figure 5.
HLA-DR Clustering from NetMHCIIpan Predictions.
The figure shows the clustering for 76 representative HLA-DR alleles. The tree was generated using the neighbor-joining algorithm from HLA distance matrices as described in the text. The circles are guides to the eye highlighting the suggested 12 HLA-DR supertypes.
Figure 6.
Strategy for Effective and Rational Coverage of the MHC Polymorphism and Specificity.
(A) The pan-specific MHC class II prediction method is used to identify MHC alleles with novel binding specificities. These alleles have a predicted binding motif that is distant to all MHC class II molecules previously described. Subsequently, immunoassays are developed describing their binding specificity and data is fed back into a retraining of the pan-specific method. (B) Next, peptides with un-characterized binding affinity (high information peptides) are identifies, experimentally assayed and fed back into the retraining.