Prediction of liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome usina artificial neural networks

Kosaku Shinoda, Masahiro Sugimoto, Nozomu Yachie, Naoyuki Sugiyama, Takeshi Masuda, Martin Robert, Tomoyoshi Soga, Masaru Tomita

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

We developed a computational method to predict the retention times of peptides in HPLC using artificial neural networks (ANN). We performed stepwise multiple linear regressions and selected for ANN input amino acids that significantly affected the LC retention time. Unlike conventional linear models, the trained ANN accurately predicted the retention time of peptides containing up to 50 amino acid residues. In 834 peptides, there was a strong correlation (R2= 0.928) between measured and predicted retention times. We demonstrated the utility of our method by the prediction of the retention time of 121 273 peptides resulting from LysC-digestion of the Escherichia coli proteome. Our approach is useful for the proteome-wide characterization of peptides and the identification of unknown peptide peaks obtained in proteome analysis.

Original languageEnglish
Pages (from-to)3312-3317
Number of pages6
JournalJournal of Proteome Research
Volume5
Issue number12
DOIs
Publication statusPublished - 2006 Dec 1

Keywords

  • Artificial neural networks
  • Liquid chromatography
  • Peptide identification
  • Retention time prediction
  • Stepwise multiple linear regression

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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