Daniel Liebler
Faculty Member
Last active: 2/15/2016

Sequence tagging reveals unexpected modifications in toxicoproteomics.

Dasari S, Chambers MC, Codreanu SG, Liebler DC, Collins BC, Pennington SR, Gallagher WM, Tabb DL
Chem Res Toxicol. 2011 24 (2): 204-16

PMID: 21214251 · PMCID: PMC3042045 · DOI:10.1021/tx100275t

Toxicoproteomic samples are rich in posttranslational modifications (PTMs) of proteins. Identifying these modifications via standard database searching can incur significant performance penalties. Here, we describe the latest developments in TagRecon, an algorithm that leverages inferred sequence tags to identify modified peptides in toxicoproteomic data sets. TagRecon identifies known modifications more effectively than the MyriMatch database search engine. TagRecon outperformed state of the art software in recognizing unanticipated modifications from LTQ, Orbitrap, and QTOF data sets. We developed user-friendly software for detecting persistent mass shifts from samples. We follow a three-step strategy for detecting unanticipated PTMs in samples. First, we identify the proteins present in the sample with a standard database search. Next, identified proteins are interrogated for unexpected PTMs with a sequence tag-based search. Finally, additional evidence is gathered for the detected mass shifts with a refinement search. Application of this technology on toxicoproteomic data sets revealed unintended cross-reactions between proteins and sample processing reagents. Twenty-five proteins in rat liver showed signs of oxidative stress when exposed to potentially toxic drugs. These results demonstrate the value of mining toxicoproteomic data sets for modifications.

MeSH Terms (14)

Algorithms Animals Cell Line, Tumor Computational Biology Crystallins Databases, Protein Histones Humans Liver Protein Processing, Post-Translational Proteins Rats Software Toxicogenetics

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