Informatics for peptide retention properties in proteomic LC-MS.
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.
Shinoda
Kosaku
K
Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
Sugimoto
Masahiro
M
Tomita
Masaru
M
Ishihama
Yasushi
Y
D000465:Algorithms; D001185:Artificial Intelligence; D002851:Chromatography, High Pressure Liquid; D019295:Computational Biology / Q000592:standards; D016571:Neural Networks, Computer; D010455:Peptides / Q000302:isolation & purification*; D040901:Proteomics / Q000379:methods*; D053719:Tandem Mass Spectrometry
D010455:Peptides