Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments
Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments
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DOI：
10.1074/mcp.m115.051300
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发表时间：
2015-09-01
影响因子：
7
通讯作者：
MacCoss, Michael J.
中科院分区：
文献类型：
--
作者：
Searle, Brian C.;Egertson, Jarrett D.;MacCoss, Michael J.
靶向质谱是检测整个蛋白质组中低丰度蛋白质定量变化的重要工具。尽管选择反应监测（SRM）是用于定量复杂样品中的肽的优选方法，但是设计SRM测定的过程是费力的。肽具有由序列特异性理化性质决定的广泛变化的信号响应;一个主要挑战是选择代表性肽作为蛋白质丰度的代表。在这里，我们提出了PREGO，一个软件工具，预测SRM实验的高响应肽。PREGO预测肽反应与人工神经网络训练使用11个最小冗余，最大相关属性。其成功的关键是，PREGO是使用从数据独立采集实验中提取的等摩尔合成肽的碎片离子强度进行训练的。由于仪器和数据收集性质的相似性，来自数据独立采集实验的相对肽响应是SRM实验的合适替代品，因为它们都可以从积分碎片离子色谱图进行定量测量。使用包含来自724种合成蛋白质的12，973种肽的SRM实验，PREGO在选择高响应肽方面比先前公布的方法表现出40-85%的改进。这些结果也代表了对文献中常用的基于规则的肽选择方法的显著改进。
Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physio-chemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40-85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature.