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 |
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Pages (from-to) | 787-798 |
Number of pages | 12 |
Journal | Proteomics |
Volume | 8 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2008 Feb |
Keywords
- Bioinformatics
- Liquid chromatography-tandem mass spectrometry
- Neural networks
- Peptide
- QSRR
ASJC Scopus subject areas
- Biochemistry
- Molecular Biology