Peptide sequence prediction supported by correlation-associated networks in human cerebrospinal fluid.
During the course of biosynthesis, processing and degradation of a peptide, many structurally related intermediate peptide products are generated. Human body fluids and tissues contain several thousand peptides that can be profiled by reversed-phase chromatography and subsequent MALDI-ToF-mass spectrometry. Correlation-Associated Peptide Networks (CAN) efficiently detect structural and biological relations of peptides, based on statistical analysis of peptide concentrations. We combined CAN with recognition of probable cleavage sites for peptidases and proteases in cerebrospinal fluid, resulting in a model able to predict the sequence of unknown peptides with high accuracy. On the basis of this approach, identification of peptide coordinates can be prioritized, and a rapid overview of the peptide content of a novel sample source can be obtained.
Lamerz
Jens
J
Department of Bioinformatics, BioVisioN AG, Feodor-Lynen-Str. 5, D-30625 Hannover, Germany.
Crameri
Reto
R
Scapozza
Leonardo
L
Mohring
Thomas
T
Selle
Hartmut
H
Zucht
Hans-Dieter
HD
D000818:Animals; D014408:Biomarkers, Tumor; D045744:Cell Line, Tumor; D005260:Female; D051379:Mice; D016513:Mice, SCID; D009368:Neoplasm Transplantation / Q000502:physiology*; D019151:Peptide Library; D010455:Peptides / Q000737:chemistry* / Q000235:genetics; D016511:Severe Combined Immunodeficiency / Q000378:metabolism*; D021241:Spectrometry, Mass, Electrospray Ionization; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
D014408:Biomarkers, Tumor; D019151:Peptide Library; D010455:Peptides