Informatics for peptide retention properties in proteomic LC-MS

Kosaku Shinoda, Masahiro Sugimoto, Masaru Tomita, Yasushi Ishihama

Research output: Contribution to journalReview article

31 Citations (Scopus)

Abstract

Retention times in HPLC yield valuable information for the identification of various analytes and the prediction of peptide retention is useful for the identification of peptides/proteins in LC-MS-based proteomics. Informatics methods such as artificial neural networks and support vector machines capable of solving nonlinear problems made possible the accurate modeling of quantitative structure-retention relationships of peptides (including large polymers) up to 5 kDa to which classical linear models cannot be applied, as well as the proteome-wide prediction of peptide retention. Proteome-wide retention prediction and accurate mass-information facilitate the identification of peptides in complex proteomic samples. In this review, we address recent developments in solid informatics methods and their application to peptide-retention properties in 'bottom-up' shotgun proteomics. We also describe future prospects for the standardization and application of retention times.

Original languageEnglish
Pages (from-to)787-798
Number of pages12
JournalProteomics
Volume8
Issue number4
DOIs
Publication statusPublished - 2008 Feb 1

Keywords

  • Bioinformatics
  • Liquid chromatography-tandem mass spectrometry
  • Neural networks
  • Peptide
  • QSRR

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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