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PChopper: high throughput peptide prediction for MRM/SRM transition design

Vackar Afzal12, Jeffrey T-J Huang13, Abdel Atrih12 and Daniel J Crowther13*

Author Affiliations

1 Translational Medicine Research Collaboration. Dundee, DD1 9SY, UK

2 College of Life Sciences, University of Dundee, DD1 5EH, UK

3 Sanofi-Aventis, Industriepark Höchst, 65926 Frankfurt am Main, Germany

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BMC Bioinformatics 2011, 12:338  doi:10.1186/1471-2105-12-338

Published: 15 August 2011



The use of selective reaction monitoring (SRM) based LC-MS/MS analysis for the quantification of phosphorylation stoichiometry has been rapidly increasing. At the same time, the number of sites that can be monitored in a single LC-MS/MS experiment is also increasing. The manual processes associated with running these experiments have highlighted the need for computational assistance to quickly design MRM/SRM candidates.


PChopper has been developed to predict peptides that can be produced via enzymatic protein digest; this includes single enzyme digests, and combinations of enzymes. It also allows digests to be simulated in 'batch' mode and can combine information from these simulated digests to suggest the most appropriate enzyme(s) to use. PChopper also allows users to define the characteristic of their target peptides, and can automatically identify phosphorylation sites that may be of interest. Two application end points are available for interacting with the system; the first is a web based graphical tool, and the second is an API endpoint based on HTTP REST.


Service oriented architecture was used to rapidly develop a system that can consume and expose several services. A graphical tool was built to provide an easy to follow workflow that allows scientists to quickly and easily identify the enzymes required to produce multiple peptides in parallel via enzymatic digests in a high throughput manner.