iSNO-AAPair

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 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.


References in zbMATH (referenced in 24 articles )

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  1. Chen, Guodong; Cao, Man; Yu, Jialin; Guo, Xinyun; Shi, Shaoping: Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou’s general PseAAC (2019)
  2. Hussain, Waqar; Khan, Yaser Daanial; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: SPrenylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins (2019)
  3. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  4. Khan, Yaser Daanial; Jamil, Mehreen; Hussain, Waqar; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments (2019)
  5. Ning, Qiao; Ma, Zhiqiang; Zhao, Xiaowei: Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components (2019)
  6. Tahir, Muhammad; Tayara, Hilal; Chong, Kil To: iRNA-PseKNC(2methyl): identify RNA 2’-O-methylation sites by convolution neural network and Chou’s pseudo components (2019)
  7. Tian, Baoguang; Wu, Xue; Chen, Cheng; Qiu, Wenying; Ma, Qin; Yu, Bin: Predicting protein-protein interactions by fusing various Chou’s pseudo components and using wavelet denoising approach (2019)
  8. Zhao, Xiaowei; Zhang, Ye; Ning, Qiao; Zhang, Hongrui; Ji, Jinchao; Yin, Minghao: Identifying N(^6)-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer (2019)
  9. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  10. Ju, Zhe; Wang, Shi-Yun: Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm (2018)
  11. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  12. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  13. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  14. Tarafder, Sumit; Toukir Ahmed, Md.; Iqbal, Sumaiya; Tamjidul Hoque, Md; Sohel Rahman, M.: RBSURFpred: modeling protein accessible surface area in real and binary space using regularized and optimized regression (2018)
  15. Jiao, Xiong; Ranganathan, Shoba: Prediction of interface residue based on the features of residue interaction network (2017)
  16. Zhai, Jing-Xuan; Cao, Tian-Jie; An, Ji-Yong; Bian, Yong-Tao: Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC (2017)
  17. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  18. Ju, Zhe; Cao, Jun-Zhe; Gu, Hong: iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou’s general PseAAC (2015)
  19. Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil: Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology (2014)
  20. Chen, Xiao; Peng, Qinke; Han, Libin; Zhong, Tao; Xu, Tao: An effective haplotype assembly algorithm based on hypergraph partitioning (2014)

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