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

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

Volume: 8 Issue: 8

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

Affiliation:

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

Abstract: 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.

Cite this article as:

Lamerz Jens, Crameri Reto, Scapozza Leonardo, Mohring Thomas, Selle Hartmut and Zucht Hans-Dieter, Peptide Sequence Prediction Supported by Correlation-Associated Networks in Human Cerebrospinal Fluid, Combinatorial Chemistry & High Throughput Screening 2005; 8 (8) . https://dx.doi.org/10.2174/138620705774962463