title: iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
abstract: Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had [Formula: see text] pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.
authors: Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, China. yxu@gordonlifescience.org
authors: Yan
authors: Y
authors: Xu
authors: Jun
authors: J
authors: Ding
authors: Ling-Yun
authors: LY
authors: Wu
authors: Kuo-Chen
authors: KC
authors: Chou
mesh_terms: D000465:Algorithms; D000596:Amino Acids; D003545:Cysteine / Q000378:metabolism*; D030562:Databases, Protein; D020407:Internet; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D015203:Reproducibility of Results; D012984:Software
chemical_list: D000596:Amino Acids; D011506:Proteins; D003545:Cysteine