TY - JOUR
T1 - Sequence tagging reveals unexpected modifications in toxicoproteomics
AU - Dasari, Surendra
AU - Chambers, Matthew C.
AU - Codreanu, Simona G.
AU - Liebler, Daniel C.
AU - Collins, Ben C.
AU - Pennington, Stephen R.
AU - Gallagher, William M.
AU - Tabb, David L.
PY - 2011/2/18
Y1 - 2011/2/18
N2 - 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.
AB - 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.
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U2 - 10.1021/tx100275t
DO - 10.1021/tx100275t
M3 - Article
C2 - 21214251
AN - SCOPUS:79951837896
SN - 0893-228X
VL - 24
SP - 204
EP - 216
JO - Chemical Research in Toxicology
JF - Chemical Research in Toxicology
IS - 2
ER -