Informatics for peptide retention properties in proteomic LC-MS

Kosaku Shinoda, Masahiro Sugimoto, Masaru Tomita, Yasushi Ishihama

研究成果: Review article査読

31 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)787-798
ページ数12
ジャーナルProteomics
8
4
DOI
出版ステータスPublished - 2008 2 1

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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