GPS-YNO2: computational prediction of tyrosine nitration sites in proteins.
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: .
Life Sciences School, Sun Yat-sen University, Guangzhou 510275, China.
Zexian
Z
Liu
Jun
J
Cao
Qian
Q
Ma
Xinjiao
X
Gao
Jian
J
Ren
Yu
Y
Xue
D000465:Algorithms; D019295:Computational Biology; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve; D012984:Software; D014443:Tyrosine / Q000737:chemistry*
D011506:Proteins; D014443:Tyrosine