iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins

As one of the most important and universal posttranslational modifications (PTMs) of proteins, S-nitrosylation (SNO) plays crucial roles in a variety of biological processes, including the regulation of cellular dynamics and many signaling events. Knowledge of SNO sites in proteins is very useful fo...

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Published in:PeerJ (San Francisco, CA) CA), 2013-10, Vol.1, p.e171-e171
Main Authors: Xu, Yan, Shao, Xiao-Jian, Wu, Ling-Yun, Deng, Nai-Yang, Chou, Kuo-Chen
Format: Article
Language:English
Subjects:
Acquired immune deficiency syndrome
AIDS
Amino acid sequence
Amino acids
Applied mathematics
Bioinformatics
Biology
Computational Biology
Cysteine
Cystine
Datasets
Drug development
Genetics
Genomics
Kinases
Mathematical models
Molecular Biology
Nearest neighbor pair
Next nearest neighbor pair
Peptides
Position-specific amino acid propensity
Post-translational modification
Proteins
Pseudo amino acid composition
S-nitrosylation
Science
Set theory
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Summary:As one of the most important and universal posttranslational modifications (PTMs) of proteins, S-nitrosylation (SNO) plays crucial roles in a variety of biological processes, including the regulation of cellular dynamics and many signaling events. Knowledge of SNO sites in proteins is very useful for drug development and basic research as well. Unfortunately, it is both time-consuming and costly to determine the SNO sites purely based on biological experiments. Facing the explosive protein sequence data generated in the post-genomic era, we are challenged to develop automated vehicles for timely and effectively determining the SNO sites for uncharacterized proteins. To address the challenge, a new predictor called iSNO-AAPair was developed by taking into account the coupling effects for all the pairs formed by the nearest residues and the pairs by the next nearest residues along protein chains. The cross-validation results on a state-of-the-art benchmark have shown that the new predictor outperformed the existing predictors. The same was true when tested by the independent proteins whose experimental SNO sites were known. A user-friendly web-server for iSNO-AAPair was established at http://app.aporc.org/iSNO-AAPair/, by which users can easily obtain their desired results without the need to follow the mathematical equations involved during its development.
ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.171