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

Content Provider	Springer Nature Link
Author	Delgado, Julio C. Escobar, Hernando Crockett, David K. Reyes Vargas, Eduardo Jensen, Peter E.
Copyright Year	2009
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.
Starting Page	241
Ending Page	246
Page Count	6
File Format	PDF
ISSN	00937711
Journal	Immunogenetics
Volume Number	61
Issue Number	3
e-ISSN	14321211
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-02-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	MHC Motifs C57BL/6 Mass spectrometry Bioinformatics Allergology Gene Function Cell Biology Human Genetics Immunology
Content Type	Text
Resource Type	Article
Subject	Genetics Immunology

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