Department of Genome Sciences, University of Washington, Seattle, Washington, United States.
Anal Chem. 2010 Dec 15;82(24):10116-24. doi: 10.1021/ac102179j. Epub 2010 Nov 19.
Proteomics experiments based on Selected Reaction Monitoring (SRM, also referred to as Multiple Reaction Monitoring or MRM) are being used to target large numbers of protein candidates in complex mixtures. At present, instrument parameters are often optimized for each peptide, a time and resource intensive process. Large SRM experiments are greatly facilitated by having the ability to predict MS instrument parameters that work well with the broad diversity of peptides they target. For this reason, we investigated the impact of using simple linear equations to predict the collision energy (CE) on peptide signal intensity and compared it with the empirical optimization of the CE for each peptide and transition individually. Using optimized linear equations, the difference between predicted and empirically derived CE values was found to be an average gain of only 7.8% of total peak area. We also found that existing commonly used linear equations fall short of their potential, and should be recalculated for each charge state and when introducing new instrument platforms. We provide a fully automated pipeline for calculating these equations and individually optimizing CE of each transition on SRM instruments from Agilent, Applied Biosystems, Thermo-Scientific and Waters in the open source Skyline software tool ( http://proteome.gs.washington.edu/software/skyline ).
基于选择反应监测 (SRM，也称为多重反应监测或 MRM) 的蛋白质组学实验被用于靶向复杂混合物中的大量蛋白质候选物。目前，仪器参数通常针对每个肽进行优化，这是一个耗时且资源密集的过程。通过能够预测与目标肽广泛多样性兼容的 MS 仪器参数，大型 SRM 实验将得到极大的促进。出于这个原因，我们研究了使用简单线性方程预测碰撞能 (CE) 对肽信号强度的影响，并将其与针对每个肽和单独跃迁的 CE 经验优化进行了比较。使用优化的线性方程，预测和经验衍生的 CE 值之间的差异平均仅为总峰面积的 7.8%。我们还发现，现有的常用线性方程未能充分发挥其潜力，并且应该针对每个电荷状态和引入新的仪器平台重新计算。我们提供了一个完全自动化的管道，用于计算这些方程，并在开源 Skyline 软件工具 (http://proteome.gs.washington.edu/software/skyline) 中针对安捷伦、应用生物系统、赛默飞世尔和沃特世的 SRM 仪器分别优化每个跃迁的 CE。
