A data-mining scheme for identifying peptide structural motifs responsible for different MS/MS fragmentation intensity patterns.
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 28 330 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.
Department of Chemistry, University of Arizona, Tucson, Arizona 85721, USA.
Yingying
Y
Huang
George C
GC
Tseng
Shinsheng
S
Yuan
Ljiljana
L
Pasa-Tolic
Mary S
MS
Lipton
Richard D
RD
Smith
Vicki H
VH
Wysocki
D000465:Algorithms; D020816:Amino Acid Motifs; D016000:Cluster Analysis; D019295:Computational Biology; D016247:Information Storage and Retrieval; D010446:Peptide Fragments / Q000032:analysis*; D040901:Proteomics / Q000379:methods; D053719:Tandem Mass Spectrometry / Q000379:methods*
D010446:Peptide Fragments