Abstract
Enzymes have been proven to play considerable roles in disease diagnosis and biological functions. The feature extraction that truly reflects the intrinsic properties of protein is the most critical step for the automatic identification of enzymes. Although lots of feature extraction methods have been proposed, some challenges remain. In this study, we developed a predictor called IHEC_RAAC, which has the capability to identify whether a protein is a human enzyme and distinguish the function of the human enzyme. To improve the feature representation ability, protein sequences were encoded by a new feature-vector called ‘reduced amino acid cluster’. We calculated 673 amino acid reduction alphabets to determine the optimal feature representative scheme. The tenfold cross-validation test showed that the accuracy of IHEC_RAAC to identify human enzymes was 74.66% and further discriminate the human enzyme classes with an accuracy of 54.78%, which was 2.06% and 8.68% higher than the state-of-the-art predictors, respectively. Additionally, the results from the independent dataset indicated that IHEC_RAAC can effectively predict human enzymes and human enzyme classes to further provide guidance for protein research. A user-friendly web server, IHEC_RAAC, is freely accessible at http://bioinfor.imu.edu.cn/ihecraac.
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Acknowledgements
This work was supported by the National Nature Scientific Foundation of China (No: 62061034, 61702290, 61861036), Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (NJYT-18-B01) and the Fund for Excellent Young Scholars of Inner Mongolia (2017JQ04).
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YZ designed this work. HW and QX performed the data analyses and wrote the manuscript. PL and LZ contributed significantly to analysis and manuscript preparation. YH helped perform the analysis with constructive discussions.
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Wang, H., Xi, Q., Liang, P. et al. IHEC_RAAC: a online platform for identifying human enzyme classes via reduced amino acid cluster strategy. Amino Acids 53, 239–251 (2021). https://doi.org/10.1007/s00726-021-02941-9
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DOI: https://doi.org/10.1007/s00726-021-02941-9