Definition and characterization of a "trypsinosome" from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures
Definition and characterization of a "trypsinosome" from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures
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DOI：
10.1021/pr049909x
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发表时间：
2004-11-01
影响因子：
4.4
通讯作者：
Figeys, D
中科院分区：
文献类型：
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作者：
Le Bihan, T;Robinson, MD;Figeys, D
尽管 HPLC-ES1-MS/MS 正在迅速成为复杂混合物中肽分析不可或缺的工具，但它提供的序列覆盖率通常相当差。低蛋白表达导致肽信号强度低于 MS 系统的检测限，再加上肽电离效率的差异，在这方面发挥了重要作用。第二个重要因素源于每种肽的物理化学性质的差异以及这些性质与色谱保留和最终检测的关系。为了识别和理解这些特性，我们将实验鉴定的肽的数据与通过对实验组中所有相应蛋白质进行计算机消化预测的肽的数据进行比较。提取的三种不同的复杂蛋白质混合物用于定义训练集，以基于线性回归分析评估氨基酸保留系数。保留系数也与之前的其他疏水性和保留系数进行了比较。由此，我们构建了一个经验模型，可以根据其物理化学性质轻松用于预测可能在我们的 HPLC-ES1-MS/MS 系统上观察到的肽。最后，我们证明了肽及其保留系数的计算机预测可用于生成包含列表，用于复杂蛋白质样品中低丰度蛋白质的目标质谱鉴定。该方法基于实验得出的数据来校准该方法，因此理论上可以应用于生成数据的任何 HPLC-MS/MS 系统。
Although HPLC-ESl-MS/MS is rapidly becoming an indispensable tool for the analysis of peptides in complex mixtures, the sequence coverage it affords is often quite poor. Low protein expression resulting in peptide signal intensities that fall below the limit of detection of the MS system in combination with differences in peptide ionization efficiency plays a significant role in this. A second important factor stems from differences in physicochemical properties of each peptide and how these properties relate to chromatographic retention and ultimate detection. To identify and understand those properties, we compared data from experimentally identified peptides with data from peptides predicted by in silico digest of all corresponding proteins in the experimental set. Three different complex protein mixtures extracted were used to define a training set to evaluate the amino acid retention coefficients based on linear regression analysis. The retention coefficients were also compared with other previous hydrophobic and retention scale. From this, we have constructed an empirical model that can be readily used to predict peptides that are likely to be observed on our HPLC-ESl-MS/MS system based on their physicochemical properties. Finally, we demonstrated that in silico prediction of peptides and their retention coefficients can be used to generate an inclusion list for a targeted mass spectrometric identification of low abundance proteins in complex protein samples. This approach is based on experimentally derived data to calibrate the method and therefore may theoretically be applied to any HPLC-MS/MS system on which data are being generated.