期刊
JOURNAL OF PROTEOME RESEARCH
卷 7, 期 1, 页码 70-79出版社
AMER CHEMICAL SOC
DOI: 10.1021/pr070106u
关键词
data mining; cluster analysis; K-means algorithm; penalized K-means algorithm; CART; statistical analysis; quantile map; peptide; MS/MS; intensity; ion trap; CID; fragmentation pattern; dissociation pattern; pairwise cleavage; Xxx-Zzz; cleavage pair; Fisher information; Pro; Gly; Asp; Glu; Arg; Lys
资金
- NCRR NIH HHS [KL2 RR024154, KL2RR024154-01] Funding Source: Medline
- NIGMS NIH HHS [R01 GM051387, R01 GM051387-12, GM R0151387] Funding Source: Medline
- NATIONAL CENTER FOR RESEARCH RESOURCES [KL2RR024154] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM051387] Funding Source: NIH RePORTER
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.
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