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 language | English |
|---|---|
| Pages (from-to) | 787-798 |
| Number of pages | 12 |
| Journal | Proteomics |
| Volume | 8 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2008 Feb |
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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 journal › Article
}
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 -