Informatics for peptide retention properties in proteomic LC-MS

Kosaku Shinoda, Masahiro Sugimoto, Masaru Tomita, Yasushi Ishihama

Research output: Contribution to journalReview articlepeer-review

33 Citations (Scopus)


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
Issue number4
Publication statusPublished - 2008 Feb


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

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology


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