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. 2010 Jun 24;5(6):e11290.
doi: 10.1371/journal.pone.0011290.

GPS-SNO: Computational Prediction of Protein S-nitrosylation Sites With a Modified GPS Algorithm

Free PMC article

GPS-SNO: Computational Prediction of Protein S-nitrosylation Sites With a Modified GPS Algorithm

Yu Xue et al. PLoS One. .
Free PMC article


As one of the most important and ubiquitous post-translational modifications (PTMs) of proteins, S-nitrosylation plays important roles in a variety of biological processes, including the regulation of cellular dynamics and plasticity. Identification of S-nitrosylated substrates with their exact sites is crucial for understanding the molecular mechanisms of S-nitrosylation. In contrast with labor-intensive and time-consuming experimental approaches, prediction of S-nitrosylation sites using computational methods could provide convenience and increased speed. In this work, we developed a novel software of GPS-SNO 1.0 for the prediction of S-nitrosylation sites. We greatly improved our previously developed algorithm and released the GPS 3.0 algorithm for GPS-SNO. By comparison, the prediction performance of GPS 3.0 algorithm was better than other methods, with an accuracy of 75.80%, a sensitivity of 53.57% and a specificity of 80.14%. As an application of GPS-SNO 1.0, we predicted putative S-nitrosylation sites for hundreds of potentially S-nitrosylated substrates for which the exact S-nitrosylation sites had not been experimentally determined. In this regard, GPS-SNO 1.0 should prove to be a useful tool for experimentalists. The online service and local packages of GPS-SNO were implemented in JAVA and are freely available at:

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Figure 1
Figure 1. The biochemical processes of the endogenous NO source and protein S-nitrosylation.
Figure 2
Figure 2. The screen snapshot of GPS-SNO 1.0 software.
The medium threshold was chosen as the default threshold. As an example, the prediction results of human tissue transglutaminase (tTG, P21980) are presented.
Figure 3
Figure 3. The prediction performance of GPS-SNO 1.0.
The leave-one-out validation and 4-, 6-, 8-, 10-fold cross-validations were calculated. The Receiver Operating Characteristic (ROC) curves and AROCs (area under ROCs) were also carried out.
Figure 4
Figure 4. Comparison of GPS 3.0, GPS 2.0 and PSSM.
For comparison, the leave-one-out results of GPS 3.0, GPS 2.0 and PSSM were calculated.
Figure 5
Figure 5. Applications of GPS-SNO 1.0.
Here we predicted potential S-nitrosylation sites in experimentally identified S-nitrosylated substrates with the default threshold. (A) Human p53 (P04637); (B) Human P4HB (P07237); (C) Mouse Masp1 (P98064); (D) Arabidopsis SAHH1 (O23255).

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