The spectral networks paradigm in high throughput mass spectrometry.
The spectral networks paradigm in high throughput mass spectrometry.
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DOI：
10.1039/c2mb25085c
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发表时间：
2012-10
影响因子：
--
通讯作者：
Bandeira N
中科院分区：
文献类型：
--
作者：
Guthals A;Watrous JD;Dorrestein PC;Bandeira N
高通量蛋白质组学是通过将能够每天生成数百万个串联质量（MS 2）谱的现代质谱仪器与日益复杂的自动识别相关软件相结合而实现的。尽管鉴定谱的收集越来越多，并且从相关肽中定期生成MS 2数据，但用于肽鉴定的主流方法仍然是近二十年的方法，即每次将一个MS 2谱与蛋白质序列数据库进行匹配。此外，数据库搜索工具压倒性地继续要求用户预先猜测可能存在于其数据中的4-6个翻译后修饰的小集合，以避免引起显著的假阳性和假阴性率。用于分析MS 2光谱的光谱网络范例在三个基本方面不同于主流数据库搜索范例。首先，光谱网络是基于匹配光谱与其他光谱，而不是对蛋白质序列。其次，光谱网络甚至在考虑它们可能的识别之前就从相关肽中找到光谱。第三，光谱网络从相关肽的光谱组中确定共识识别，而不是一次单独尝试识别一个光谱。尽管光谱网络算法仍处于起步阶段，但它们已经提供了迄今为止最长和最准确的从头序列，揭示了发现意想不到的翻译后修饰和高度修饰肽的新途径，能够自动排序的循环非具有未知氨基酸的核糖体肽，现在正在定义一种新的方法来绘制生物系统的整个分子输出，适用于串联质谱分析。在这里，我们回顾了光谱网络算法的现状，并讨论了未来可能的方向，从任何类别的分子光谱的自动化解释。
High-throughput proteomics is made possible by a combination of modern mass spectrometry instruments capable of generating many millions of tandem mass (MS2) spectra on a daily basis and the increasingly sophisticated associated software for their automated identification. Despite the growing accumulation of collections of identified spectra and the regular generation of MS2 data from related peptides, the mainstream approach for peptide identification is still the nearly two decades old approach of matching one MS2 spectrum at a time against a database of protein sequences. Moreover, database search tools overwhelmingly continue to require that users guess in advance a small set of 4–6 post-translational modifications that may be present in their data in order to avoid incurring substantial false positive and negative rates. The spectral networks paradigm for analysis of MS2 spectra differs from the mainstream database search paradigm in three fundamental ways. First, spectral networks are based on matching spectra against other spectra instead of against protein sequences. Second, spectral networks find spectra from related peptides even before considering their possible identifications. Third, spectral networks determine consensus identifications from sets of spectra from related peptides instead of separately attempting to identify one spectrum at a time. Even though spectral networks algorithms are still in their infancy, they have already delivered the longest and most accurate de novo sequences to date, revealed a new route for the discovery of unexpected post-translational modifications and highly-modified peptides, enabled automated sequencing of cyclic non-ribosomal peptides with unknown amino acids and are now defining a novel approach for mapping the entire molecular output of biological systems that is suitable for analysis with tandem mass spectrometry. Here we review the current state of spectral networks algorithms and discuss possible future directions for automated interpretation of spectra from any class of molecules.
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影响因子：
3.6
作者：
Butcher, BG;Helmann, JD
通讯作者：
Helmann, JD
影响因子：
5.8
作者：
Craig, R;Beavis, RC
通讯作者：
Beavis, RC
影响因子：
4.4
作者：
Bora, Adriana;Annangudi, Suresh P.;Millet, Larry J.;Rubakhin, Stanislav S.;Forbes, Andrew J.;Kelleher, Neil L.;Gillette, Martha U.;Sweedler, Jonathan V.
通讯作者：
Sweedler, Jonathan V.
DOI：
10.1073/pnas.1232301100
发表时间：
2003-06-10
影响因子：
11.1
作者：
Englander, JJ;Del Mar, C;Woods, VL
通讯作者：
Woods, VL
影响因子：
48
作者：
Frank, Ari M.;Monroe, Matthew E.;Shah, Anuj R.;Carver, Jeremy J.;Bandeira, Nuno;Moore, Ronald J.;Anderson, Gordon A.;Smith, Richard D.;Pevzner, Pavel A.
通讯作者：
Pevzner, Pavel A.