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Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

Reche, Pedro A and Glutting, John-Paul and Zhang, Hong and Reinherz, Ellis L (2004) Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics, 56 (6). pp. 405-19. ISSN 0093-7711

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Abstract

We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.


Item Type:Article
Uncontrolled Keywords:Epitopes; Major histocompatibility complex; Prediction; Profile; Proteasome
Subjects:Medical sciences > Medicine > Immunology
Sciences > Computer science > Bioinformatics
ID Code:9334
Deposited On:14 Aug 2009 11:09
Last Modified:30 Sep 2009 10:50

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