Immunogenetics

, Volume 61, Issue 3, pp 241–246

Identification of naturally processed ligands in the C57BL/6 mouse using large-scale mass spectrometric peptide sequencing and bioinformatics prediction

Authors

    • Department of PathologyUniversity of Utah
    • ARUP Institute for Clinical & Experimental Pathology®
  • Hernando Escobar
    • Department of PathologyUniversity of Utah
  • David K. Crockett
    • ARUP Institute for Clinical & Experimental Pathology®
  • Eduardo Reyes-Vargas
    • Department of PathologyUniversity of Utah
  • Peter E. Jensen
    • Department of PathologyUniversity of Utah
    • ARUP Institute for Clinical & Experimental Pathology®
Brief Communication

DOI: 10.1007/s00251-009-0360-4

Cite this article as:
Delgado, J.C., Escobar, H., Crockett, D.K. et al. Immunogenetics (2009) 61: 241. doi:10.1007/s00251-009-0360-4
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Abstract

Most major histocompatibility complex (MHC) class I–peptide-binding motifs are currently defined on the basis of quantitative in vitro MHC–peptide-binding assays. This information is used to develop bioinformatics-based tools to predict the binding of peptides to MHC class I molecules. To date few studies have analyzed the performance of these bioinformatics tools to predict the binding of peptides determined by sequencing of naturally processed peptides eluted directly from MHC class I molecules. In this study, we performed large-scale sequencing of endogenous peptides eluted from H2Kb and H2Db molecules expressed in spleens of C57BL/6 mice. Using sequence data from 281 peptides, we identified novel preferred anchor residues located in H2Kb and H2Db-associated peptides that refine our knowledge of these H2 class I peptide-binding motifs. The analysis comparing the performance of three bioinformatics methods to predict the binding of these peptides, including artificial neural network, stabilized matrix method, and average relative binding, revealed that 61% to 94% of peptides eluted from H2Kb and H2Db molecules were correctly classified as binders by the three algorithms. These results suggest that bioinformatics tools are reliable and efficient methods for binding prediction of naturally processed MHC class I ligands.

Keywords

MHCMotifsC57BL/6Mass spectrometryBioinformatics

Supplementary material

251_2009_360_MOESM1_ESM.doc (362 kb)
ESM 1 (DOC 370 kb)

Copyright information

© Springer-Verlag 2009