Spectral networks: a new approach to de novo discovery of protein sequences and posttranslational modifications
Spectral networks: a new approach to de novo discovery of protein sequences and posttranslational modifications
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DOI：
10.2144/000112487
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发表时间：
2007-06-01
期刊：
影响因子：
2.7
通讯作者：
Bandeira, Nuno
中科院分区：
文献类型：
--
作者：
Bandeira, Nuno
重大的技术进步加速了高通量蛋白质组学的发展，使其能够每天自动生成数百万个串联质谱图。在这样的设置中，对更大序列覆盖率的需求与标准实验程序相结合，通常从重叠的多肽产生多个串联质谱图-典型的观察包括不同的一个或两个末端氨基酸的多肽，以及来自相同多肽的修饰和未修饰变体的光谱。与传统的单独分析每个串联质谱图的光谱识别算法不同，光谱网络定义了一种新的计算方法，即从重叠的多肽中找到并同时解释一组光谱。在鸟枪式蛋白质测序中，光谱网络利用比对光谱中的冗余序列信息来提供迄今报道的最长和最准确的离子陷阱数据从头开始序列。此外，通过组合相同多肽的多个修饰和未修饰变体的光谱，光谱网络能够绕过主要的猜测/确认方法来识别翻译后修饰，或者直接从实验数据中发现修饰和高度修饰的多肽。这些算法的开源实现可以从Peptide.ucsd.edu下载。
Significant technological advances have accelerated high-throughput proteomics to the automated generation of millions of tandem mass spectra on a daily basis. In such a setup, the desire for greater sequence coverage combines with standard experimental procedures to commonly yield multiple tandem mass spectra from overlapping peptides-typical observations include peptides differing by one or two terminal amino acids and spectra from modified and unmodified variants of the same peptides. In a departure from the traditional spectrum identification algorithms that analyze each tandem mass spectrum in isolation, spectral networks define a new computational approach that instead finds and simultaneously interprets sets of spectra from overlapping peptides. In shotgun protein sequencing, spectral networks capitalize on the redundant sequence information in the aligned spectra to deliver the longest and most accurate de novo sequences ever reported for ion trap data. Also, by combining spectra from multiple modified and unmodified variants of the same peptides, spectral networks are able to bypass the dominant guess/confirm approach to the identification of posttranslational modifications and alternatively discover modifications and highly modified peptides directly from experimental data. Open-source implementations of these algorithms may be downloaded from peptide.ucsd.edu.