S-nitrosylation site detection software tools | Post-translational modification data analysis
S-nitrosylation (SNO), a selective and reversible protein post-translational modification that involves the covalent attachment of nitric oxide (NO) to the sulfur atom of cysteine, critically regulates protein activity, localization and stability. Due to its importance in regulating protein functions and cell signaling, a mass spectrometry-based proteomics method rapidly evolved to increase the dataset of experimentally determined SNO sites.
Allows users to detect S-nitrosylation sites. GPS-SNO aims to assist users in investigating mechanisms and regulatory roles of S-nitrosylation. The program permits users to query multiple protein sequences through a batch prediction mode as well as to set different levels of threshold. The application can be queried as a web application for basic research or as a standalone program for an advanced analysis.
Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. The predictor was implemented using the conditional random field (CRF) algorithm. Users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method.
Identifies S-nitrosylation (SNO) sites from protein sequences. Using both informative features and an elaborate feature selection scheme, PSNO achieves good prediction performance with a mean Mathews correlation coefficient (MCC) value of about 0.5119 on the training dataset using 10-fold cross-validation. These results indicate that PSNO can be used as a competitive predictor among the state-of-the-art SNOs prediction tools.