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.
authors.affiliation: Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
authors.forename: Kosaku
authors.initials: K
authors.lastname: Shinoda
authors.forename: Masahiro
authors.initials: M
authors.lastname: Sugimoto
authors.forename: Masaru
authors.initials: M
authors.lastname: Tomita
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Ishihama
mesh_terms: 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
chemical_list: D010455:Peptides