色谱

色谱

• 专论与综述 • 上一篇    下一篇

肽段反相色谱保留时间预测算法及其在蛋白质鉴定中的应用

刘超1,2, 王海鹏1,2, 付岩1, 袁作飞1,2, 迟浩1,2, 王乐珩1, 孙瑞祥1*, 贺思敏1*   

  1. 1. 中国科学院计算技术研究所智能信息处理重点实验室, 北京 100190; 2. 中国科学院研究生院, 北京 100049
  • 收稿日期:2010-01-26 修回日期:2010-03-26 出版日期:2010-06-28 发布日期:1980-06-25
  • 通讯作者: 孙瑞祥，贺思敏

Prediction of peptide retention time in reversed-phase liquid chromatography and its application in protein identification

LIU Chao1,2, WANG Haipeng1,2, FU Yan1, YUAN Zuofei1,2, CHI Hao1,2, WANG Leheng1, SUN Ruixiang1*, HE Simin1*   

  1. 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2010-01-26 Revised:2010-03-26 Online:2010-06-28 Published:1980-06-25
  • Contact: SUN Ruixiang, HE Simin

PDF

445

被引次数

7

摘要： 液相色谱-质谱(LC-MS)联用是当今规模化蛋白质鉴定的主流技术。肽段在反相液相色谱(RPLC)中的保留时间主要是由肽段的理化性质和LC条件(固定相、流动相)决定的。可以通过分析肽段的理化性质,并量化它们对肽段色谱行为的影响来预测保留时间。预测结果可以用于帮助提高蛋白质鉴定的数量和可信度,也可用于肽段的翻译后修饰等研究。现在已有的保留时间预测算法主要有保留系数法和机器学习法两大类,得到的预测保留时间与实际保留时间相关系数可达到0.93。随着色谱和质谱技术的不断发展,肽段色谱行为的稳定性和重现性越来越好,保留时间预测结果也越来越准确。预测肽段保留时间将成为提高蛋白质鉴定结果的重要技术手段之一。

关键词: 保留时间, 保留系数, 蛋白质, 反相液相色谱-质谱联用, 机器学习, 鉴定 , 肽, 预测

Abstract: Liquid chromatography-mass spectrometry (LC-MS) is the mainstream of high-throughput protein identification technology. Peptide retention time in reversed-phase liquid chromatography (RPLC) is mainly determined by the physicochemical properties of the peptide and the LC conditions (stationary phase and mobile phase). Retention time can be predicted by analyzing these properties and quantifying their effects on peptide chromatographic behavior. Prediction of peptide retention time in LC can be used to improve identification of peptides and post translational modifications (PTM). There are mainly two methods to predict retention time: i.e. retention coefficients and machine learning. The coefficient of determination between observed and predicted retention times can reach 0.93. With the development of LC-MS technology, retention time prediction will become an important tool to facilitate protein identification.

Key words: identification , machine learning, peptide, prediction, protein, retention coefficient, retention time, reversed-phase liquid chromatography-mass spectrometry (RPLC-MS)