Peptide Sequence Prediction Supported by Correlation-Associated Networks in Human Cerebrospinal Fluid

Author(s): Jens Lamerz, Reto Crameri, Leonardo Scapozza, Thomas Mohring, Hartmut Selle, Hans-Dieter Zucht

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 8 , Issue 8 , 2005

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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.

Keywords: Peptidomics, computational biology, bioinformatics, protease, peptidase, CAN

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Article Details

Year: 2005
Published on: 01 March, 2012
Page: [789 - 799]
Pages: 11
DOI: 10.2174/138620705774962463
Price: $65

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