Informatics for peptide retention properties in proteomic LC-MS

Kosaku Shinoda, Masahiro Sugimoto, Masaru Tomita, Yasushi Ishihama

Research output: Contribution to journalArticle

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

Fingerprint

Informatics
Proteomics
Peptides
Proteome
Firearms
Standardization
Support vector machines
Retention (Psychology)
Polymers
Linear Models
Neural networks
High Pressure Liquid Chromatography
Proteins

Keywords

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

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Cite this

Informatics for peptide retention properties in proteomic LC-MS. / Shinoda, Kosaku; Sugimoto, Masahiro; Tomita, Masaru; Ishihama, Yasushi.

In: Proteomics, Vol. 8, No. 4, 02.2008, p. 787-798.

Research output: Contribution to journalArticle

Shinoda, Kosaku ; Sugimoto, Masahiro ; Tomita, Masaru ; Ishihama, Yasushi. / Informatics for peptide retention properties in proteomic LC-MS. In: Proteomics. 2008 ; Vol. 8, No. 4. pp. 787-798.
@article{539240c68b6d4179af59271ec7b450de,
title = "Informatics for peptide retention properties in proteomic LC-MS",
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.",
keywords = "Bioinformatics, Liquid chromatography-tandem mass spectrometry, Neural networks, Peptide, QSRR",
author = "Kosaku Shinoda and Masahiro Sugimoto and Masaru Tomita and Yasushi Ishihama",
year = "2008",
month = "2",
doi = "10.1002/pmic.200700692",
language = "English",
volume = "8",
pages = "787--798",
journal = "Proteomics",
issn = "1615-9853",
publisher = "Wiley-VCH Verlag",
number = "4",

}

TY - JOUR

T1 - Informatics for peptide retention properties in proteomic LC-MS

AU - Shinoda, Kosaku

AU - Sugimoto, Masahiro

AU - Tomita, Masaru

AU - Ishihama, Yasushi

PY - 2008/2

Y1 - 2008/2

N2 - 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.

AB - 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.

KW - Bioinformatics

KW - Liquid chromatography-tandem mass spectrometry

KW - Neural networks

KW - Peptide

KW - QSRR

UR - http://www.scopus.com/inward/record.url?scp=40649126964&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=40649126964&partnerID=8YFLogxK

U2 - 10.1002/pmic.200700692

DO - 10.1002/pmic.200700692

M3 - Article

C2 - 18214845

AN - SCOPUS:40649126964

VL - 8

SP - 787

EP - 798

JO - Proteomics

JF - Proteomics

SN - 1615-9853

IS - 4

ER -