Document Detail

GPS-YNO2: computational prediction of tyrosine nitration sites in proteins.
MedLine Citation:
PMID:  21258675     Owner:  NLM     Status:  Publisher    
The last decade has witnessed rapid progress in the identification of protein tyrosine nitration (PTN), which is an essential and ubiquitous post-translational modification (PTM) that plays a variety of important roles in both physiological and pathological processes, such as the immune response, cell death, aging and neurodegeneration. Identification of site-specific nitrated substrates is fundamental for understanding the molecular mechanisms and biological functions of PTN. In contrast with labor-intensive and time-consuming experimental approaches, here we report the development of the novel software package GPS-YNO2 to predict PTN sites. The software demonstrated a promising accuracy of 76.51%, a sensitivity of 50.09% and a specificity of 80.18% from the leave-one-out validation. As an example application, we predicted potential PTN sites for hundreds of nitrated substrates which had been experimentally detected in small-scale or large-scale studies, even though the actual nitration sites had still not been determined. Through a statistical functional comparison with the nitric oxide (NO) dependent reversible modification of S-nitrosylation, we observed that PTN prefers to attack certain fundamental biological processes and functions. These prediction and analysis results might be helpful for further experimental investigation. Finally, the online service and local packages of GPS-YNO2 1.0 were implemented in JAVA and freely available at: .
Zexian Liu; Jun Cao; Qian Ma; Xinjiao Gao; Jian Ren; Yu Xue
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-1-21
Journal Detail:
Title:  Molecular bioSystems     Volume:  -     ISSN:  1742-2051     ISO Abbreviation:  -     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2011-1-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101251620     Medline TA:  Mol Biosyst     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Life Sciences School, Sun Yat-sen University (SYSU), Guangzhou, 510275, China.
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