EzyPred

EzyPred: a top-down approach for predicting enzyme functional classes and subclasses. Given a protein sequence, how can we identify whether it is an enzyme or non-enzyme? If it is, which main functional class it belongs to? What about its sub-functional class? It is important to address these problems because they are closely correlated with the biological function of an uncharacterized protein and its acting object and process. Particularly, with the avalanche of protein sequences generated in the Post Genomic Age and relatively much slower progress in determining their functions by experiments, it is highly desired to develop an automated method by which one can get a fast and accurate answer to these questions. Here, a top-down predictor, called EzyPred, is developed by fusing the results derived from the functional domain and evolution information. EzyPred is a 3-layer predictor: the 1st layer prediction engine is for identifying a query protein as enzyme or non-enzyme; the 2nd layer for the main functional class; and the 3rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 90% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has > or = 40% sequence identity to any other in a same class or subclass. EzyPred is freely accessible at http://chou.med.harvard.edu/bioinf/EzyPred/, by which one can get the desired 3-level results for a query protein sequence within less than 90 s.


References in zbMATH (referenced in 16 articles )

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  1. 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)
  2. Khan, Zaheer Ullah; Hayat, Maqsood; Khan, Muazzam Ali: Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model (2015)
  3. Lyons, James; Biswas, Neela; Sharma, Alok; Dehzangi, Abdollah; Paliwal, Kuldip K.: Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (2014)
  4. Rahimi, Amir; Madadkar-Sobhani, Armin; Touserkani, Rouzbeh; Goliaei, Bahram: Efficacy of function specific 3D-motifs in enzyme classification according to their EC-numbers (2013)
  5. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  6. 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)
  7. Huang, Wei; Zhang, Jianmin; Wang, Yurong; Huang, Dan: A simple method to analyze the similarity of biological sequences based on the fuzzy theory (2010)
  8. Yang, Jie; Li, Jia-Huang; Wang, Jin; Zhang, Chen-Yu: Molecular modeling of BAD complex resided in a mitochondrion integrating glycolysis and apoptosis (2010)
  9. Anand, Ashish; Suganthan, P. N.: Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates (2009)
  10. Frenkel, Zakharia M.; Frenkel, Zeev M.; Trifonov, Edward N.; Snir, Sagi: Structural relatedness via flow networks in protein sequence space (2009)
  11. Georgiou, D. N.; Karakasidis, T. E.; Nieto, J. J.; Torres, A.: Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou’s pseudo amino acid composition (2009)
  12. Jahandideh, Samad; Hoseini, Somayyeh; Jahandideh, Mina; Hoseini, Afsaneh; Miri Disfani, Fatemeh: (\gamma)-turn types prediction in proteins using the two-stage hybrid neural discriminant model (2009)
  13. Shao, Xiaojian; Tian, Yingjie; Wu, Lingyun; Wang, Yong; Jing, Ling; Deng, Naiyang: Predicting DNA- and RNA-binding proteins from sequences with kernel methods (2009)
  14. Yang, Jian-Yi; Peng, Zhen-Ling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng: Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation (2009)
  15. Zeng, Yu-hong; Guo, Yan-zhi; Xiao, Rong-quan; Yang, Li; Yu, Le-zheng; Li, Meng-long: Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach (2009)
  16. Munteanu, Cristian Robert; González-Díaz, Humberto; Magalhães, Alexandre L.: Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices (2008)