GPCR-GIA

GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis. G-protein-coupled receptors (GPCRs) play fundamental roles in regulating various physiological processes as well as the activity of virtually all cells. Different GPCR families are responsible for different functions. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop an automated method to address the two problems: given the sequence of a query protein, can we identify whether it is a GPCR? If it is, what family class does it belong to? Here, a two-layer ensemble classifier called GPCR-GIA was proposed by introducing a novel scale called ’grey incident degree’. The overall success rate by GPCR-GIA in identifying GPCR and non-GPCR was about 95%, and that in identifying the GPCRs among their nine family classes was about 80%. These rates were obtained by the jackknife cross-validation tests on the stringent benchmark data sets where none of the proteins has > or = 50% pairwise sequence identity to any other in a same class. Moreover, a user-friendly web-server was established at http://218.65.61.89:8080/bioinfo/GPCR-GIA. For user’s convenience, a step-by-step guide on how to use the GPCR-GIA web server is provided. Generally speaking, one can get the desired two-level results in around 10 s for a query protein sequence of 300-400 amino acids; the longer the sequence is, the more time that is needed.


References in zbMATH (referenced in 19 articles )

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  1. Podder, Avijit; Jatana, Nidhi; Latha, N.: Human dopamine receptors interaction network (DRIN): a systems biology perspective on topology, stability and functionality of the network (2014)
  2. Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen: iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints (2013)
  3. Liu, Guoqing; Liu, Jia; Cui, Xiangjun; Cai, Lu: Sequence-dependent prediction of recombination hotspots in \textitSaccharomycescerevisiae (2012)
  4. Lu, Jin-Long; Hu, Xue-Hai; Hu, Dong-Gang: A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences (2012)
  5. Qiu, Zhijun; Wang, Xicheng: Prediction of protein-protein interaction sites using patch-based residue characterization (2012)
  6. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  7. de Avila e Silva, Scheila; Echeverrigaray, Sergio; Gerhardt, Günther J. L.: BacPP: bacterial promoter prediction -- a tool for accurate sigma-factor specific assignment in enterobacteria (2011)
  8. González-Díaz, Humberto; Prado-Prado, Francisco; Sobarzo-Sánchez, Eduardo; Haddad, Mohamed; Maurel Chevalley, Séverine; Valentin, Alexis; Quetin-Leclercq, Joëlle; Dea-Ayuela, María A.; Gomez-Muños, María Teresa; Munteanu, Cristian R.; Torres-Labandeira, Juan José; García-Mera, Xerardo; Tapia, Ricardo A.; Ubeira, Florencio M.: NL MIND-BEST: a web server for ligands and proteins discovery -- theoretic-experimental study of proteins of \textitGiardialamblia and new compounds active against \textitPlasmodiumfalciparum (2011)
  9. Kavousi, Kaveh; Moshiri, Behzad; Sadeghi, Mehdi; Araabi, Babak N.; Moosavi-Movahedi, Ali Akbar: A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM (2011)
  10. Lin, Hao; Ding, Hui: Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition (2011)
  11. Qi, Zhao-Hui; Li, Ling; Zhang, Zhi-Meng; Qi, Xiao-Qin: Self-similarity analysis of eubacteria genome based on weighted graph (2011)
  12. Georgiou, D. N.; Karakasidis, T. E.; Nieto, Juan J.; Torres, A.: A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets (2010)
  13. Huang, Chen; Zhang, Ruijie; Chen, Zhiqiang; Jiang, Yongshuai; Shang, Zhenwei; Sun, Peng; Zhang, Xuehong; Li, Xia: Predict potential drug targets from the ion channel proteins based on SVM (2010)
  14. Huang, Wei; Zhang, Jianmin; Wang, Yurong; Huang, Dan: A simple method to analyze the similarity of biological sequences based on the fuzzy theory (2010)
  15. Ji, Guoli; Wu, Xiaohui; Shen, Yingjia; Huang, Jiangyin; Quinn Li, Qingshun: A classification-based prediction model of messenger RNA polyadenylation sites (2010)
  16. Masso, Majid; Vaisman, Iosif I.: Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms (2010)
  17. Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra: High performance set of PseAAC and sequence based descriptors for protein classification (2010)
  18. Wang, Shiyuan; Tian, Fengchun; Qiu, Yu; Liu, Xiao: Bilateral similarity function: a novel and universal method for similarity analysis of biological sequences (2010)
  19. Yu, Lezheng; Guo, Yanzhi; Li, Yizhou; Li, Gongbing; Li, Menglong; Luo, Jiesi; Xiong, Wenjia; Qin, Wenli: SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition (2010)