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Proteomics Clin Appl. 2015 Jun;9(5-6):531-42. doi: 10.1002/prca.201400163.

Comparison of higher energy collisional dissociation and collision-induced dissociation MS/MS sequencing methods for identification of naturally occurring peptides in human urine.

Author information

1
Charite-Universitätsmedizin Berlin, Berlin, Germany.
2
Mosaiques Diagnostics GmbH, Hanover, Germany.
3
Biotechnology Division, Biomedical Research Foundation, Academy of Athens, Athens, Greece.
4
Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
5
Universitätsklinikum RWTH Aachen, Institute of Molecular Cardiovascular Research, Aachen, Germany.

Abstract

PURPOSE:

The aim of this study is to determine the best fragmentation method for sequence identification of naturally occurring urinary peptides in the field of clinical proteomics.

EXPERIMENTAL DESIGN:

We used LC-MS/MS analysis of urine samples to determine the analytical performance of higher energy collisional dissociation (HCD), CID with high and low resolution MS/MS for the identification of naturally occurring peptides in the low molecular weight urinary proteome.

RESULTS:

HCD and CID high-resolution generated a 22% error rate in peptide sequence identifications. CID low-resolution showed significantly higher error rates (37%). Excluding the error rate (i.e rejection of cysteine-containing peptides), we observed a higher degree of overlap between HCD and CID high resolution for identification of peptide sequences of rank 1 and cross-correlation ≥ 1.9 (262 peptide sequences) compared to CID low (208 peptide sequences with HCD and 192 peptide sequences with CID high). Reproducibility of detected peptides in three out of the five replicates was also higher in HCD and CID high in relation to CID low resolution.

CONCLUSION AND CLINICAL RELEVANCE:

Our data demonstrated that HCD and CID high-resolution performed with better accuracy and reproducibility than CID low resolution in respect to the identification of naturally occurring urinary peptide sequences.

KEYWORDS:

Biomarkers; Clinical proteomics; MS/MS sequencing; Peptide fragments

PMID:
25821083
DOI:
10.1002/prca.201400163
[Indexed for MEDLINE]

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