iHyd-PseCp

iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Protein hydroxylation is a posttranslational modification (PTM), in which a CH group in Pro (P) or Lys (K) residue has been converted into a COH group, or a hydroxyl group (-OH) is converted into an organic compound. Closely associated with cellular signaling activities, this type of PTM is also involved in some major diseases, such as stomach cancer and lung cancer. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of P or K, which ones can be hydroxylated, and which ones cannot? With the explosive growth of protein sequences in the post-genomic age, the problem has become even more urgent. To address such a problem, we have developed a predictor called iHyd-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition (PseAAC) and introducing the ”Random Forest” algorithm to operate the calculation. Rigorous jackknife tests indicated that the new predictor remarkably outperformed the existing state-of-the-art prediction method for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for iHyd-PseCp has been established at http://www.jci-bioinfo.cn/iHyd-PseCp, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.


References in zbMATH (referenced in 15 articles )

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  1. Adilina, Sheikh; Farid, Dewan Md; Shatabda, Swakkhar: Effective DNA binding protein prediction by using key features via Chou’s general PseAAC (2019)
  2. Ahmad, Jamal; Hayat, Maqsood: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components (2019)
  3. 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)
  4. 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)
  5. 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)
  6. Wang, Lidong; Zhang, Ruijun; Mu, Yashuang: Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC (2019)
  7. 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)
  8. Arif, Muhammad; Hayat, Maqsood; Jan, Zahoor: IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition (2018)
  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. 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)
  11. Goede, Simon L.; de Galan, Bastiaan E.; Leow, Melvin Khee Shing: Personalized glucose-insulin model based on signal analysis (2017)
  12. Pai, Priyadarshini P.; Dash, Tirtharaj; Mondal, Sukanta: Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach (2017)
  13. Saghapour, Ehsan; Sehhati, Mohammadreza: Prediction of metastasis in advanced colorectal carcinomas using CGH data (2017)
  14. 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)
  15. Yang, Lei; Wang, Shiyuan; Zhou, Meng; Chen, Xiaowen; Zuo, Yongchun; Lv, Yingli: Characterization of BioPlex network by topological properties (2016)