GPCR-CA

GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. Given an uncharacterized protein sequence, how can we identify whether it is a G-protein-coupled receptor (GPCR) or not? If it is, which functional family class does it belong to? It is important to address these questions because GPCRs are among the most frequent targets of therapeutic drugs and the information thus obtained is very useful for ”comparative and evolutionary pharmacology,” a technique often used for drug development. Here, we present a web-server predictor called ”GPCR-CA,” where ”CA” stands for ”Cellular Automaton” (Wolfram, S. Nature 1984, 311, 419), meaning that the CA images have been utilized to reveal the pattern features hidden in piles of long and complicated protein sequences. Meanwhile, the gray-level co-occurrence matrix factors extracted from the CA images are used to represent the samples of proteins through their pseudo amino acid composition (Chou, K.C. Proteins 2001, 43, 246). GPCR-CA is a two-layer predictor: the first layer prediction engine is for identifying a query protein as GPCR on non-GPCR; if it is a GPCR protein, the process will be automatically continued with the second-layer prediction engine to further identify its type among the following six functional classes: (a) rhodopsin-like, (b) secretin-like, (c) metabotrophic/glutamate/pheromone; (d) fungal pheromone, (e) cAMP receptor, and (f) frizzled/smoothened family. The overall success rates by the predictor for the first and second layers are over 91% and 83%, respectively, that were obtained through rigorous jackknife cross-validation tests on a new-constructed stringent benchmark dataset in which none of proteins has >or=40% pairwise sequence identity to any other in a same subset. GPCR-CA is freely accessible at http://218.65.61.89:8080/bioinfo/GPCR-CA, by which one can get the desired two-layer results for a query protein sequence within about 20 seconds.


References in zbMATH (referenced in 35 articles )

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  1. Hayat, Maqsood; Khan, Asifullah: MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM (2012)
  2. Liu, Guoqing; Liu, Jia; Cui, Xiangjun; Cai, Lu: Sequence-dependent prediction of recombination hotspots in \textitSaccharomycescerevisiae (2012)
  3. Lu, Jin-Long; Hu, Xue-Hai; Hu, Dong-Gang: A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences (2012)
  4. Qin, Wenli; Li, Yizhou; Li, Juan; Yu, Lezheng; Wu, Di; Jing, Runyu; Pu, Xuemei; Guo, Yanzhi; Li, Menglong: Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes (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. Gong, Binsheng; Liu, Tao; Zhang, Xiaoyu; Chen, Xi; Li, Jiang; Lv, Hongchao; Zou, Yi; Li, Xia; Rao, Shaoqi: Disease embryo development network reveals the relationship between disease genes and embryo development genes (2011)
  9. 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)
  10. Lin, Hao; Ding, Hui: Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition (2011)
  11. Mahdavi, Abbas; Jahandideh, Samad: Application of density similarities to predict membrane protein types based on pseudo-amino acid composition (2011)
  12. Mohabatkar, Hassan; Mohammad Beigi, Majid; Esmaeili, Abolghasem: Prediction of GABA(_\mathrmA) receptor proteins using the concept of Chou’s pseudo-amino acid composition and support vector machine (2011)
  13. Qi, Zhao-Hui; Li, Ling; Zhang, Zhi-Meng; Qi, Xiao-Qin: Self-similarity analysis of eubacteria genome based on weighted graph (2011)
  14. Qi, Zhao-Hui; Wei, Ruo-Yan: A combination dimensionality reduction approach to codon position patterns of eubacteria based on their complete genomes (2011)
  15. Xie, Guosen; Mo, Zhongxi: Three 3D graphical representations of DNA primary sequences based on the classifications of DNA bases and their applications (2011)
  16. Zhang, Jiapu; Sun, Jie; Wu, Changzhi: Optimal atomic-resolution structures of prion AGAAAAGA amyloid fibrils (2011)
  17. Zhou, Guo-Ping: The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism (2011)
  18. Esmaeili, Maryam; Mohabatkar, Hassan; Mohsenzadeh, Sasan: Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses (2010)
  19. 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)
  20. 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)

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