Utility of characteristic QTOF MS/MS fragmentation for MHC class I peptides.

J Proteome Res

ARUP Institute for Clinical and Experimental Pathology, Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah 84112, United States.

Published: May 2011

AI Article Synopsis

  • The study explores how mass spectrometry can reveal peptide sequences from proteins, highlighting the need for both automated algorithms and manual validation for accurate interpretation, especially with MHC class I molecules.
  • Traditional trypsin digestion methods may not be suitable for all peptides, necessitating the manual review of spectral data to ensure accurate peptide identification.
  • The research identifies key fragmentation patterns and proposes rules to enhance manual validation, suggesting potential improvements for peptide search algorithms and the creation of new software tools.

Article 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.

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Source
http://dx.doi.org/10.1021/pr101272kDOI Listing

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