Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, USA.
J Proteome Res. 2010 Mar 5;9(3):1323-9. doi: 10.1021/pr900863u.
Large-scale protein sequencing methods rely on enzymatic digestion of complex protein mixtures to generate a collection of peptides for mass spectrometric analysis. Here we examine the use of multiple proteases (trypsin, LysC, ArgC, AspN, and GluC) to improve both protein identification and characterization in the model organism Saccharomyces cerevisiae. Using a data-dependent, decision tree-based algorithm to tailor MS(2) fragmentation method to peptide precursor, we identified 92 095 unique peptides (609 665 total) mapping to 3908 proteins at a 1% false discovery rate (FDR). These results were a significant improvement upon data from a single protease digest (trypsin) - 27 822 unique peptides corresponding to 3313 proteins. The additional 595 protein identifications were mainly from those at low abundances (i.e., < 1000 copies/cell); sequence coverage for these proteins was likewise improved nearly 3-fold. We demonstrate that large portions of the proteome are simply inaccessible following digestion with a single protease and that multiple proteases, rather than technical replicates, provide a direct route to increase both protein identifications and proteome sequence coverage.
大规模蛋白质测序方法依赖于酶解复杂的蛋白质混合物，以生成用于质谱分析的肽集合。在这里，我们研究了使用多种蛋白酶（胰蛋白酶、LysC、ArgC、AspN 和 GluC）来提高模型生物酿酒酵母中的蛋白质鉴定和表征。使用基于数据依赖的决策树算法来定制 MS(2) 碎片化方法以适应肽前体，我们在 1%假发现率 (FDR) 下鉴定了 92095 个独特肽（总计 609665 个），这些肽对应于 3908 个蛋白质。这些结果与单一蛋白酶消化（胰蛋白酶）的数据相比有显著改善 - 对应于 3313 个蛋白质的 27822 个独特肽。额外的 595 个蛋白质鉴定主要来自低丰度的蛋白质（即 < 1000 个拷贝/细胞）；这些蛋白质的序列覆盖率也提高了近 3 倍。我们证明，在使用单一蛋白酶消化后，大量蛋白质组是无法获得的，而多种蛋白酶而不是技术重复提供了一种直接途径，可以增加蛋白质鉴定和蛋白质组序列覆盖率。
