Shinoda Kosaku, Sugimoto Masahiro, Tomita Masaru, Ishihama Yasushi
Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
Proteomics. 2008 Feb;8(4):787-98. doi: 10.1002/pmic.200700692.
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.
高效液相色谱（HPLC）中的保留时间可为各种分析物的鉴定提供有价值的信息，而预测肽的保留情况对于基于液相色谱-质谱联用（LC-MS）的蛋白质组学中肽/蛋白质的鉴定很有用。诸如人工神经网络和支持向量机等能够解决非线性问题的信息学方法，使得对肽（包括大分子聚合物）直至5 kDa的定量结构-保留关系进行准确建模成为可能，而经典线性模型无法应用于此，同时也实现了全蛋白质组范围的肽保留预测。全蛋白质组保留预测和精确的质量信息有助于在复杂的蛋白质组样品中鉴定肽。在本综述中，我们阐述了可靠信息学方法的最新进展及其在“自下而上”鸟枪法蛋白质组学中肽保留特性方面的应用。我们还描述了保留时间标准化和应用的未来前景。
