GPS-SNO: Computational Prediction of Protein S-Nitrosylation Sites with a Modified GPS Algorithm

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/.

How widespread is stable protein S-nitrosylation as an end-effector of protein regulation?

“…The computational prediction software GPS-SNO (http://sno.biocuckoo.org/) was used to analyze 467 experimentally verified S-nitrosothiol sites in 302 proteins, to develop algorithms that computationally 'predict' Snitrosation from a primary sequence alone. Experimentally these algorism were found to be 75% accurate at predicting sites of S-nitrosation [56]. However, these motifs have been criticised due to the high concentrations of NO donor used in the founding experiments to map S-nitrosation sites [57,58], and so provide a platform for the prediction algorithm.…”

Computational Prediction of Post-Translational Modification Sites in Proteins

“…Later, we improved the algorithm to version 2.1 for the prediction of phosphorylation sites with a additional motif length selection method (MLS) . Recently, with two additional approaches of k-means clustering and weight training (WT), we further designed GPS 3.0 algorithm for the prediction of Snitrosylation (Xue et al, 2010b) and nitration sites . The details of GPS series algorithms were described below.…”

“…These methods were largely introduced from the fields of informatics or statistics and originally designed for general propose. Previously, we developed a series of GPS algorithms (Initially defined as Group-based Phosphorylation Scoring and later renamed as Group-based Prediction System), which have been exclusively and successfully used for the prediction of kinds of PTM sites, such as phosphorylation (Xue et al, 2005;Xue et al, 2008;Xue et al, 2011;Zhou et al, 2004), sumoylation (Ren et al, 2009;Xue et al, 2006b), palmitoylation Zhou et al, 2006a), S-Nitrosylation (Xue et al, 2010b) and nitration . The prediction performance of GPS 1.x (1.0 and 1.1) could be comparative to other analogous approaches, while GPS 2.x versions (2.0 and 2.1) are much better than other strategies (Xue et al, 2010a).…”

“…The prediction performance of GPS 1.x (1.0 and 1.1) could be comparative to other analogous approaches, while GPS 2.x versions (2.0 and 2.1) are much better than other strategies (Xue et al, 2010a). Recently, we greatly improved our previous method and released the GPS 3.0 algorithm, while has been successfully adopted for predicting S-nitrosylation (Xue et al, 2010b) and nitration sites , with further enhanced performances. During the past several years, a considerable number of bioinformatists or experimentalists have communicated with us on GPS algorithm details, which were never thoroughly described in our previously research articles due to page limitation.…”

A computational analysis of S-(2-succino)cysteine sites in proteins

“…Computational prediction of these sites could be a convenient and high-throughput strategy to generate helpful information for subsequent experimental verification. Methods for computationally predicting redox-active Cys residues with specific modifications have already been proposed [8,[33][34][35][36][37]. However, to date no method has been developed to predict 2SC sites in proteins, and the succinate proteome has never been studied in order to evaluate the specificity of the protein succination.…”