NetCTLpan: pan-specific MHC class I pathway epitope predictions |
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Authors: | Thomas Stranzl Mette Voldby Larsen Claus Lundegaard Morten Nielsen |
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Institution: | (1) Department of Systems Biology DTU, Building 208, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, 2800, Denmark |
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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 . |
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