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The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments

Mario Cannataro1, Giovanni Cuda2, Marco Gaspari2, Sergio Greco3, Giuseppe Tradigo1 and Pierangelo Veltri1*

Author Affiliations

1 Bioinformatics Laboratory, Experimental and Clinical Medicine Department, Magna Græcia University, viale Europa 88100, Catanzaro, Italy

2 Proteomics Laboratory, Experimental and Clinical Medicine Department, Magna Græcia University, viale Europa 88100, Catanzaro, Italy

3 Department of Electronics, Computer and System Sciences (DEIS), University of Calabria, via P. Bucci 41 C 87036, Rende, Italy

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BMC Bioinformatics 2007, 8:255  doi:10.1186/1471-2105-8-255

Published: 15 July 2007

Abstract

Background

Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high.

Results

We designed a method for improving the data processing and peptide identification in sample sets subjected to ICAT labeling and LC-MS/MS analysis, based on cross validating MS/MS results. Such a method has been implemented in a tool, called EIPeptiDi, which boosts the ICAT data analysis software improving peptide identification throughout the input data set. Heavy/Light (H/L) pairs quantified but not identified by the MS/MS routine, are assigned to peptide sequences identified in other samples, by using similarity criteria based on chromatographic retention time and Heavy/Light mass attributes. EIPeptiDi significantly improves the number of identified peptides per sample, proving that the proposed method has a considerable impact on the protein identification process and, consequently, on the amount of potentially critical information in clinical studies. The EIPeptiDi tool is available at http://bioingegneria.unicz.it/~veltri/projects/eipeptidi/ webcite with a demo data set.

Conclusion

EIPeptiDi significantly increases the number of peptides identified and quantified in analyzed samples, thus reducing the number of unassigned H/L pairs and allowing a better comparative analysis of sample data sets.