GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm - PubMed (original) (raw)

GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm

Yu Xue et al. PLoS One. 2010.

Abstract

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: http://sno.biocuckoo.org/.

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Conflict of interest statement

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

Figures

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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