NetCTLpan: pan-specific MHC class I pathway epitope predictions

Abstract

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/.

Fig. 1

ROC curves for a pooled data set from the HLA-A*0101, HLA-B*4402, and HLA-B*5101 alleles. The source proteins for all three alleles were cut into overlapping peptides of the size of the given ligand, and all peptides except the given ligands were taken as negative. The data set contained 31 HLA-A*0101, 50 HLA-B*4402, and 29 HLA-B*5101 ligands, and the predictions were made using the NetCTLpan method. The black curve shows the ROC curve for the combined data set. The other three curves show the allele-specific sensitivity (fraction of ligands identified) as a function of the overall specificity for each of the three alleles. The insert shows the curves for the full range of specificities

Fig. 2

Weights on proteasomal cleavage and TAP transport efficiency related to AUC_x fraction. The smaller the included fraction, the higher the contribution of proteasomal cleavage and TAP transport efficiency to a high performance. Optimal weights on proteasomal cleavage and TAP were found by optimizing the average AUC_x value on the SYF training data set. The dotted line indicates the AUC_0.1 fraction

Fig. 3

Performance comparison in terms of ROC curves for NetCTLpan and NetMHCpan. The true positive rate is shown as a function of the false positive rate. The figure is based on the SYF training set. The shaded area shows the area under the curve used to calculate the AUC_0.1. The insert shows the complete curves