A data-mining scheme for identifying peptide structural motifs responsible for different MS/MS fragmentation intensity patterns
A data-mining scheme for identifying peptide structural motifs responsible for different MS/MS fragmentation intensity patterns
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DOI：
10.1021/pr070106u
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发表时间：
2008-01-01
影响因子：
4.4
通讯作者：
Wysocki, Vicki H.
中科院分区：
文献类型：
--
作者：
Huang, Yingying;Tseng, George C.;Wysocki, Vicki H.
虽然串联质谱（MS/MS）已成为蛋白质组学的一个组成部分，在MS/MS光谱中的强度模式很少在大多数广泛使用的算法中加权，因为它们还没有被完全理解。在这里，知识挖掘方法被证明是发现碎片强度模式，并阐明这种模式背后的化学因素。使用惩罚K-means算法对来自不同电荷状态和序列的28330离子阱肽MS/MS谱的碎片强度信息进行无监督聚类。没有任何事先的化学假设，获得了四个具有独特的碎片模式的集群。生成决策树以研究导致这些片段化模式的肽序列基序和电荷状态。这种数据挖掘方案一般适用于任何大型数据集。它绕过了常见的先验知识约束，并报告了整体肽片段化行为。它提高了对气相肽解离的理解，并为新的或改进的蛋白质鉴定算法提供了基础。
Although tandem mass spectrometry (MS/MS) has become an integral part of proteomics, intensity patterns in MS/MS spectra are rarely weighted heavily in most widely used algorithms because they are not yet fully understood. Here a knowledge mining approach is demonstrated to discover fragmentation intensity patterns and elucidate the chemical factors behind such patterns. Fragmentation intensity information from 28330 ion trap peptide MS/MS spectra of different charge states and sequences went through unsupervised clustering using a penalized K-means algorithm. Without any prior chemistry assumptions, four clusters with distinctive fragmentation patterns were obtained. A decision tree was generated to investigate peptide sequence motif and charge state status that caused these fragmentation patterns. This data-mining scheme is generally applicable for any large data sets. It bypasses the common prior knowledge constraints and reports on the overall peptide fragmentation behavior. It improves the understanding of gas-phase peptide dissociation and provides a foundation for new or improved protein identification algorithms.