Faculty of Life Sciences, The University of Manchester, Manchester, UK.
OMICS. 2012 Sep;16(9):449-56. doi: 10.1089/omi.2011.0156. Epub 2012 Jul 17.
Quantitative proteomics experiments are usually performed using proteolytic peptides as surrogates for their parent proteins, inferring protein amounts from peptide-level quantitation. This process is frequently dependent on complete digestion of the parent protein to its limit peptides so that their signal is truly representative. Unfortunately, proteolysis is often incomplete, and missed cleavage peptides are frequently produced that are unlikely to be optimal surrogates for quantitation, particularly for label-mediated approaches seeking to derive absolute values. We have generated a predictive computational tool that is able to predict which candidate proteolytic peptide bonds are likely to be missed by the standard enzyme trypsin. Our cross-validated prediction tool uses support vector machines and achieves high accuracy in excess of 0.94 precision (PPV), with attendant high sensitivity of 0.79, across multiple proteomes. We believe this is a useful tool for selecting candidate quantotypic peptides, seeking to minimize likely loss owing to missed cleavage, which will be a boon for quantitative proteomic pipelines as well as other areas of proteomics. Our results are discussed in the context of recent results examining the kinetics of missed cleavages in proteomic digestion protocols, and show agreement with observed experimental trends. The software has been made available at http://king.smith.man.ac.uk/mcpred .
定量蛋白质组学实验通常使用酶解肽段作为其母蛋白的替代物，通过肽段水平的定量来推断蛋白质的含量。这个过程通常依赖于母蛋白完全酶解至其极限肽段，以确保其信号具有真正的代表性。不幸的是，酶解往往是不完全的，并且经常会产生可能不是最佳定量替代物的漏切肽段，尤其是对于标签介导的方法，这些方法试图获得绝对值。我们开发了一种预测性计算工具，能够预测标准酶胰蛋白酶可能会错过的候选酶切肽键。我们的交叉验证预测工具使用支持向量机，在多个蛋白质组中实现了超过 0.94 的高精度（PPV），同时具有 0.79 的高灵敏度。我们相信这是一种选择候选定量肽段的有用工具，旨在尽量减少因漏切而导致的可能损失，这将对定量蛋白质组学管道以及蛋白质组学的其他领域都有帮助。我们的结果在最近研究蛋白质组酶解方案中漏切动力学的结果背景下进行了讨论，并与观察到的实验趋势一致。该软件可在 http://king.smith.man.ac.uk/mcpred 获得。
