title: Utility of characteristic QTOF MS/MS fragmentation for MHC class I peptides.
abstract: Systematic investigation of cellular process by mass spectrometric detection of peptides obtained from proteins digestion or directly from immuno-purification can be a powerful tool when used appropriately. The true sequence of these peptides is defined by the interpretation of spectral data using a variety of available algorithms. However peptide match algorithm scoring is typically based on some, but not all, of the mechanisms of peptide fragmentation. Although algorithm rules for soft ionization techniques generally fit very well to tryptic peptides, manual validation of spectra is often required for endogenous peptides such as MHC class I molecules where traditional trypsin digest techniques are not used. This study summarizes data mining and manual validation of hundreds of peptide sequences from MHC class I molecules in publically available data files. We herein describe several important features to improve and quantify manual validation for these endogenous peptides--post automated algorithm searching. Important fragmentation patterns are discussed for the studied MHC Class I peptides. These findings lead to practical rules that are helpful when performing manual validation. Furthermore, these observations may be useful to improve current peptide search algorithms or development of novel software tools.
authors.affiliation: ARUP Institute for Clinical and Experimental Pathology, Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah 84112, United States.
authors.forename: Hernando
authors.initials: H
authors.lastname: Escobar
authors.forename: Eduardo
authors.initials: E
authors.lastname: Reyes-Vargas
authors.forename: Peter E
authors.initials: PE
authors.lastname: Jensen
authors.forename: Julio C
authors.initials: JC
authors.lastname: Delgado
authors.forename: David K
authors.initials: DK
authors.lastname: Crockett
mesh_terms: D019295:Computational Biology / Q000379:methods; D057225:Data Mining; D015395:Histocompatibility Antigens Class I / Q000737:chemistry*; D010446:Peptide Fragments / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D015395:Histocompatibility Antigens Class I; D010446:Peptide Fragments