Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry.
Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry.
复制标题
通过选择反应监测质谱法估计未标记样品的绝对蛋白质含量。
DOI：
10.1074/mcp.m111.013987
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发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
Aebersold R
中科院分区：
文献类型：
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作者：
Ludwig C;Claassen M;Schmidt A;Aebersold R
对于现代分子和系统生物学中的许多研究问题，关于蛋白质绝对数量的信息是必不可少的。该信息包括例如过程的动力学建模、蛋白质周转测定、蛋白质复合物的化学计量研究或一个样品内或样品间不同蛋白质的定量比较。迄今为止，绝大多数蛋白质组学研究仅限于提供有限数量样品之间蛋白质水平的相对定量比较。在这里，我们描述和演示的实用程序的靶向MS技术的绝对蛋白质丰度的估计在未标记和nonfractionated细胞裂解液。该方法是基于选择反应监测（SRM）质谱法和“最佳飞行”的假设，它假设最强烈的胰蛋白酶肽每蛋白质的特定MS信号强度是近似恒定的整个蛋白质组。从来自定向MS数据的肽前体离子信号强度中为每种蛋白质选择SRM靶向的最佳飞行肽。从粗合成类似物的全MS/MS扫描中选择每个肽的最强转换。我们使用Monte Carlo交叉验证系统地研究了该技术的准确性作为测量的最佳飞行肽的数量和每个肽的SRM转换的数量的函数。我们发现，基于每个蛋白质的三个最佳飞行肽（TopPep 3/TopTra 2）的两个最强烈的转变的线性模型产生了最佳结果，其交叉相关平均倍数误差为1.8，皮尔逊平方系数R2为0.88。应用优化模型的微生物钩端螺旋体的裂解物，我们检测到显着的蛋白质丰度变化的39个目标蛋白抗生素治疗后，与文献值相关性良好。所描述的方法是普遍适用的，并利用SRM的固有性能优势，如高灵敏度，选择性，再现性和动态范围，并估计绝对蛋白质浓度的选择蛋白质在最小化的成本。
For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the “best flyer” hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R2 of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.