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Effect of Collision Energy Optimization on the Measurement of Peptides by Selected Reaction Monitoring (SRM) Mass Spectrometry

机译:碰撞能量优化对选择性反应监测(SRM)质谱测定肽的影响

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Proteomics experiments based on Selected ReactionnMonitoring (SRM, also referred to as Multiple ReactionnMonitoring or MRM) are being used to target largennumbers of protein candidates in complex mixtures. Atnpresent, instrument parameters are often optimized forneach peptide, a time and resource intensive process.nLarge SRM experiments are greatly facilitated by havingnthe ability to predict MS instrument parameters that worknwell with the broad diversity of peptides they target. Fornthis reason, we investigated the impact of using simplenlinear equations to predict the collision energy (CE) onnpeptide signal intensity and compared it with the empiricalnoptimization of the CE for each peptide and transitionnindividually. Using optimized linear equations, the differencenbetween predicted and empirically derived CE valuesnwas found to be an average gain of only 7.8% of total peaknarea. We also found that existing commonly used linearnequations fall short of their potential, and should benrecalculated for each charge state and when introducingnnew instrument platforms. We provide a fully automatednpipeline for calculating these equations and individuallynoptimizing CE of each transition on SRM instruments fromnAgilent, Applied Biosystems, Thermo-Scientific and Watersnin the open source Skyline software tool (http:/proteome.gs.washington.edu/software/skyline).
机译:基于选择性反应监测(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候选蛋白质。目前,通常针对每种肽都需要对仪器参数进行优化,这是一个时间和资源密集的过程。由于无法预测与所靶向肽的多样性有关的MS仪器参数,因此大大简化了大型SRM实验。因此,我们研究了使用简单线性方程式预测碰撞能量(CE)对肽信号强度的影响,并将其与每种肽的CE经验优化和单个过渡进行了比较。使用优化的线性方程,发现预测值和根据经验得出的CE值之间的差异n仅是总峰面积的7.8%。我们还发现,现有的常用线性方程组均未达到其潜力,因此,对于每种充电状态以及引入新的仪器平台时,均不应重新计算。我们提供了一个全自动的管线,用于计算这些方程式,并通过开源Skyline软件工具(http:/proteome.gs.washington.edu/software/skyline)中的aAgilent,Applied Biosystems,Thermo-Scientific和Watersn在SRM仪器上分别优化每个过渡的CE。 )。

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