The Generating Function of CID, ETD, and CID/ETD Pairs of Tandem Mass Spectra: Applications to Database Search
The Generating Function of CID, ETD, and CID/ETD Pairs of Tandem Mass Spectra: Applications to Database Search
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DOI：
10.1074/mcp.m110.003731
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发表时间：
2010-12-01
影响因子：
7
通讯作者：
Pevzner, Pavel A.
中科院分区：
文献类型：
--
作者：
Kim, Sangtae;Mischerikow, Nikolai;Pevzner, Pavel A.
最近出现的新的质谱技术（如电子转移解离，ETD）和在高通量实验中用于蛋白质消化的额外蛋白酶（如Lys-N）的可用性的提高，提出了设计新的算法来解释所产生的新型串联质谱（MS/MS）的挑战。传统的MS/MS数据库搜索算法（如SEQUEST和Mascot）最初是为色氨酸的碰撞诱导解离（CID）而设计的，并且主要基于色氨酸碎片化的专业知识（而不是机器学习技术）来设计特定于CID的评分函数。因此，这些算法的性能是次优的新的质谱技术或非色氨酸肽。我们最近提出了生成函数法（MS-GF）用于色氨酸肽的CID光谱。在本研究中，我们将MS- gf扩展到从一组任何类型（如CID， ETD等）的注释MS/MS谱中自动获得评分参数，并提出了一种基于MS- gf的新的数据库搜索工具MS- gfdb。我们发现MS-GFDB在ETD光谱或被Lys-N消化的肽方面优于Mascot。例如，在ETD光谱中，MS-GFDB鉴定的色氨酸和赖氨酸- n肽的数量比Mascot增加了2.7和2.6倍。此外，即使在Mascot开发用于分析色氨酸肽的CID光谱十年之后，MS-GFDB（不是专门为CID光谱或色氨酸肽量身定制的）在肽鉴定数量上比Mascot增加了28%。最后，我们提出了一个统计框架，用于分析来自同一前体的多个光谱（例如CID/ETD光谱对），并为肽-光谱-光谱匹配分配p值。中国生物医学工程学报（英文版），2010。
Recent emergence of new mass spectrometry techniques (e.g. electron transfer dissociation, ETD) and improved availability of additional proteases (e.g. Lys-N) for protein digestion in high-throughput experiments raised the challenge of designing new algorithms for interpreting the resulting new types of tandem mass (MS/MS) spectra. Traditional MS/MS database search algorithms such as SEQUEST and Mascot were originally designed for collision induced dissociation (CID) of tryptic peptides and are largely based on expert knowledge about fragmentation of tryptic peptides (rather than machine learning techniques) to design CID-specific scoring functions. As a result, the performance of these algorithms is suboptimal for new mass spectrometry technologies or nontryptic peptides. We recently proposed the generating function approach (MS-GF) for CID spectra of tryptic peptides. In this study, we extend MS-GF to automatically derive scoring parameters from a set of annotated MS/MS spectra of any type (e.g. CID, ETD, etc.), and present a new database search tool MS-GFDB based on MS-GF. We show that MS-GFDB outperforms Mascot for ETD spectra or peptides digested with Lys-N. For example, in the case of ETD spectra, the number of tryptic and Lys-N peptides identified by MS-GFDB increased by a factor of 2.7 and 2.6 as compared with Mascot. Moreover, even following a decade of Mascot developments for analyzing CID spectra of tryptic peptides, MS-GFDB (that is not particularly tailored for CID spectra or tryptic peptides) resulted in 28% increase over Mascot in the number of peptide identifications. Finally, we propose a statistical framework for analyzing multiple spectra from the same precursor (e.g. CID/ETD spectral pairs) and assigning p values to peptide-spectrum-spectrum matches. Molecular & Cellular Proteomics 9:2840-2852, 2010.