Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles
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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.
KeywordsEpitopes Major histocompatibility complex Prediction Profile Proteasome
This manuscript was supported by NIH grant AI50900 and the Molecular Immunology Foundation. We wish to acknowledge the insightful comments and corrections provided by Drs Esther Lafuente, Robert Mallis, and Weimin Zhong.
- Hammer J, Bono E, Gallazzi F, Belunis C, Nagy Z, Sinigaglia F (1994) Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning. J Exp Med 267:1258–1267Google Scholar
- Shannon CE (1948) The mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656Google Scholar
- Stolcke A (2002) SRILM—an extensible language modeling toolkit. In: Ohala TMNJJ, Derwing BL, Hodge MM, Wiebe GE (eds) Proceedings of the International Conference of Spoken Language Processing. Center for Spoken Language Research, Boulder, pp 901–904Google Scholar
- Stryhn A, Pederson LO, Romme T, Holm A, Buus S (1996) Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding. Eur J Immunol 26:1911–1918PubMedGoogle Scholar
- Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Sinigaglia F, Hammer J (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nature Biotech 17:555–561Google Scholar
- Swain MT, Brooks AJ, Kemp GJL (2001) An automated approach to modelling class II MHC alleles and predicting peptide binding. Proceedings of the IEEE International Symposium on Bio-Informatics and Biomedical Engineering. IEEE Computer Society, New York, pp 81–88Google Scholar
- Thompson JD, Higgins DG, Gibson TJ (1994a) CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weigh matrix choice. Nucleic Acids Res 2:4673–4680Google Scholar
- Toes RE, Nussbaum AK, Degermann S, Schirle M, Emmerich NP, Kraft M, Laplace C, Zwinderman A, Dick TP, Muller J, Schonfisch B, Schmid C, Fehling HJ, Stevanovic S, Rammensee HG, Schild H (2001) Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products. J Exp Med 194:1–12CrossRefPubMedGoogle Scholar