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利用串联质谱中形成的增强互补离子对混合物光谱进行解卷积和提高肽鉴定的通量。

Deconvolution of mixture spectra and increased throughput of peptide identification by utilization of intensified complementary ions formed in tandem mass spectrometry.

机构信息

Protein Research Group, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark.

出版信息

J Proteome Res. 2013 Jul 5;12(7):3362-71. doi: 10.1021/pr400210m. Epub 2013 Jun 19.

Abstract

A cornerstone of mass spectrometry based proteomics is to relate with high statistical significance experimentally obtained tandem mass spectrometry (MS/MS) data to peptide sequences from a protein database. Most sequence specific fragment ions in MS/MS spectra are represented by a subset of complementary ion pairs. Here, we investigated the reliabilities of complementary ion pairs formed in CAD and CAD/ETD MS/MS and developed a reliability-based approach of intensification of ion signals of complementary pairs prior to database searching. In a large-scale proteomics experiment using high-resolution orbitrap mass spectrometry, an increase in the number of peptide identifications was obtained relative to the original CAD MS/MS spectra when intensified golden complementary (+18.6%) and CAD complementary pairs (+17.2%) were submitted to the Mascot search engine. This also exceeded the results obtained by deisotoping/deconvolution of CAD MS/MS spectra. A novel approach for extracting sequence-specific fragment ions of co-isolated peptides was developed based on the complementarity rules. This technique demonstrated an impressive gain of 42.4% more peptide identifications as compared with the use of the initial data set.

摘要

基于质谱的蛋白质组学的一个基石是将具有高统计意义的实验获得的串联质谱(MS/MS)数据与蛋白质数据库中的肽序列相关联。MS/MS 谱中的大多数序列特异性片段离子由互补离子对的子集表示。在这里,我们研究了 CAD 和 CAD/ETD MS/MS 中形成的互补离子对的可靠性,并开发了一种基于可靠性的方法,在进行数据库搜索之前增强互补离子对的离子信号强度。在使用高分辨率轨道阱质谱进行的大规模蛋白质组学实验中,与原始 CAD MS/MS 谱相比,当将强化的金色互补(+18.6%)和 CAD 互补对(+17.2%)提交给 Mascot 搜索引擎时,获得了更多的肽鉴定数量。这也超过了对 CAD MS/MS 谱进行去同位素/去卷积的结果。基于互补规则,开发了一种提取共分离肽的序列特异性片段离子的新方法。与使用初始数据集相比,该技术可实现高达 42.4%的肽鉴定数量的显著增加。

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