Identification of protein modifications using MS/MS de novo sequencing and the OpenSea alignment algorithm
Identification of protein modifications using MS/MS de novo sequencing and the OpenSea alignment algorithm
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DOI：
10.1021/pr049781j
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发表时间：
2005-03-01
影响因子：
4.4
通讯作者：
Nagalla, SR
中科院分区：
文献类型：
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作者：
Searle, BC;Dasari, S;Nagalla, SR
数据挖掘和进一步的生物学解释需要能够从质谱数据中健壮地识别翻译后蛋白质修饰的算法。在这项研究中，我们确定了一种基于质量的比对算法（OpenSea），用于从头测序结果，可以在高通量环境中识别翻译后修饰的肽。利用二维液相色谱分析了人类白内障晶状体中蛋白质的复杂消化物，该组织含有丰富的修饰蛋白，并在高质量精度和低质量精度仪器上收集了数据。数据分析采用自动从头测序，然后采用OpenSea基于质量的序列比对。总共检测到80个修饰，其中36个以前未在晶状体中报道。这证明了使用自动化数据处理算法（如OpenSea）在给定组织中识别大量已知和以前未知的蛋白质修饰的潜力。
Algorithms that can robustly identify post-translational protein modifications from mass spectrometry data are needed for data-mining and furthering biological interpretations. In this study, we determined that a mass-based alignment algorithm (OpenSea) for de novo sequencing results could identify post-translationally modified peptides in a high-throughput environment. A complex digest of proteins from human cataractous lens, a tissue containing a high abundance of modified proteins, was analyzed using two-dimensional liquid chromatography, and data was collected on both high and low mass accuracy instruments. The data were analyzed using automated cle novo sequencing followed by OpenSea mass-based sequence alignment. A total of 80 modifications were detected, 36 of which were previously unreported in the lens. This demonstrates the potential to identify large numbers of known and previously unknown protein modifications in a given tissue using automated data processing algorithms such as OpenSea.