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D ‐score: A search engine independent MD ‐score
Author(s) -
Vaudel Marc,
Breiter Daniela,
Beck Florian,
Rahnenführer Jörg,
Martens Lennart,
Zahedi René P.
Publication year - 2013
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201200408
Subject(s) - in silico , mascot , search engine , database search engine , inference , computer science , quality score , fragmentation (computing) , inference engine , peptide , set (abstract data type) , computational biology , artificial intelligence , data mining , biology , information retrieval , engineering , biochemistry , metric (unit) , operations management , programming language , gene , political science , law , operating system
While peptides carrying PTM s are routinely identified in gel‐free MS , the localization of the PTM s onto the peptide sequences remains challenging. Search engine scores of secondary peptide matches have been used in different approaches in order to infer the quality of site inference, by penalizing the localization whenever the search engine similarly scored two candidate peptides with different site assignments. In the present work, we show how the estimation of posterior error probabilities for peptide candidates allows the estimation of a PTM score called the D ‐score, for multiple search engine studies. We demonstrate the applicability of this score to three popular search engines: M ascot, OMSSA , and X ! T andem, and evaluate its performance using an already published high resolution data set of synthetic phosphopeptides. For those peptides with phosphorylation site inference uncertainty, the number of spectrum matches with correctly localized phosphorylation increased by up to 25.7% when compared to using M ascot alone, although the actual increase depended on the fragmentation method used. Since this method relies only on search engine scores, it can be readily applied to the scoring of the localization of virtually any modification at no additional experimental or in silico cost.