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iSNO-PseAAC

swMATH ID: 22446
Software Authors:
Xu, Y.; Ding, J.; Wu, L. -Y.; Chou, K. -C.
Description: iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. 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
Homepage: https://www.ncbi.nlm.nih.gov/pubmed/23409062
Related Software: iRSpot-PseDNC; iRSpot-TNCPseAAC; propy; iLoc-Hum; iNuc-PhysChem; Pse-in-One; PseAAC-Builder; iSNO-AAPair; iRNA-PseColl; iAMP-2L; LIBSVM; iPPI-Esml; iNitro-Tyr; iNuc-PseKNC; iPTM-mLys; iMethyl-PseAAC; PseKNC; iRSpot-EL; iPro54-PseKNC; iHSP-PseRAAAC
Cited in: 40 Documents
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Cited by 134 Authors

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1 Chen, Cheng
1 Chen, Guodong
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