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

研究成果: Article査読

46 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)3312-3317
ページ数6
ジャーナルJournal of Proteome Research
5
12
DOI
出版ステータスPublished - 2006 12 1

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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