GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. G protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. With the avalanche of newly generated protein sequences in the post genomic age, to expedite the process of drug discovery, it is highly desirable to develop an automated method to rapidly identify GPCRs and their types. A new predictor was developed by hybridizing two different modes of pseudo-amino acid composition (PseAAC): the functional domain PseAAC and the low-frequency Fourier spectrum PseAAC. The new predictor is called GPCR-2L, where ”2L” means that it is a two-layer predictor: the 1st layer prediction engine is to identify a query protein as GPCR or not; if it is, the prediction will be automatically continued to further identify it as belonging to one of the following six types: (1) rhodopsin-like (Class A), (2) secretin-like (Class B), (3) metabotropic glutamate/pheromone (Class C), (4) fungal pheromone (Class D), (5) cAMP receptor (Class E), or (6) frizzled/smoothened family (Class F). The overall success rate of GPCR-2L in identifying proteins as GPCRs or non-GPCRs is over 97.2%, while identifying GPCRs among their six types is over 97.8%. Such high success rates were derived by the rigorous jackknife cross-validation on a stringent benchmark dataset, in which none of the included proteins had ≥40% pairwise sequence identity to any other protein in a same subset. As a user-friendly web-server, GPCR-2L is freely accessible to the public at, by which one can obtain the 2-level results in about 20 s for a query protein sequence of 500 amino acids. The longer the sequence is, the more time it may usually need. The high success rates reported here indicate that it is a quite effective approach to identify GPCRs and their types with the functional domain information and the low-frequency Fourier spectrum analysis. It is anticipated that GPCR-2L may become a useful tool for both basic research and drug development in the areas related to GPCRs.

References in zbMATH (referenced in 22 articles )

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  1. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  2. Saghapour, Ehsan; Sehhati, Mohammadreza: Prediction of metastasis in advanced colorectal carcinomas using CGH data (2017)
  3. Ali, Farman; Hayat, Maqsood: Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (2016)
  4. Muthu Krishnan, S.: Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach (2016)
  5. Georgiou, D. N.; Karakasidis, T. E.; Megaritis, A. C.; Nieto, Juan J.; Torres, A.: An extension of fuzzy topological approach for comparison of genetic sequences (2015)
  6. Niu, Xiao-Hui; Hu, Xue-Hai; Shi, Feng; Xia, Jing-Bo: Predicting DNA binding proteins using support vector machine with hybrid fractal features (2014)
  7. Liu, Zhi-Xin; Liu, Song-lei; Yang, Hong-Qiang; Bao, Li-Hua: Using protein granularity to extract the protein sequence features (2013)
  8. 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)
  9. Yu, Chenglong; Deng, Mo; Cheng, Shiu-Yuen; Yau, Shek-Chung; He, Rong L.; Yau, Stephen S.-T.: Protein space: a natural method for realizing the nature of protein universe (2013)
  10. Hayat, Maqsood; Khan, Asifullah: MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM (2012)
  11. Li, Tao; Li, Qian-Zhong: Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure (2012)
  12. Liu, Guoqing; Liu, Jia; Cui, Xiangjun; Cai, Lu: Sequence-dependent prediction of recombination hotspots in \textitSaccharomycescerevisiae (2012)
  13. Lu, Jin-Long; Hu, Xue-Hai; Hu, Dong-Gang: A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences (2012)
  14. Mei, Suyu: Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization (2012)
  15. Mishra, Pooja; Nath Pandey, Paras: Elman RNN based classification of proteins sequences on account of their mutual information (2012)
  16. Qiu, Zhijun; Wang, Xicheng: Prediction of protein-protein interaction sites using patch-based residue characterization (2012)
  17. Yang, Lianping; Zhang, Xiangde; Zhu, Hegui: Alignment free comparison: similarity distribution between the DNA primary sequences based on the shortest absent word (2012)
  18. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  19. 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)
  20. Khan, Asifullah; Majid, Abdul; Hayat, Maqsood: CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition (2011)

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