Informatics for peptide retention properties in proteomic LC-MS

被引:30
作者
Shinoda, Kosaku [1 ,2 ]
Sugimoto, Masahiro [1 ,3 ]
Tomita, Masaru [1 ,2 ]
Ishihama, Yasushi [1 ,4 ]
机构
[1] Keio Univ, Inst Adv Biosci, Yamagata 9970017, Japan
[2] Human Metabolome Technol, Yamagata, Japan
[3] Mitsubishi Space Software, Bioinformat Dept, Amagasaki, Hyogo, Japan
[4] Japan Sci & Technol Agcy, PRESTO, Tokyo, Japan
关键词
bioinformatics; liquid chromatography-tandem mass spectrometry; neural networks; pepticle; QSRR;
D O I
10.1002/pmic.200700692
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:787 / 798
页数:12
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