Abstract
Discovering hot regions in protein–protein interaction is important for understanding the interactions between proteins, while because of the complexity and time-consuming of experimental methods, the computational prediction method can be very helpful to improve the efficiency to predict hot regions. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method that uses density-based incremental clustering to predict hot regions and optimizes the predicted hot regions using neighbor residues is proposed. Experimental results show that the proposed method significantly improves the prediction performance of hot regions.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61273225, 61201423). Thanks to Bingqing Tan and Jing Ye in our lab, and Shen Peng and Qi Mo for their meaningful discussion.
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Hu, J., Zhang, X. (2015). Identification of Hot Regions in Protein Interfaces: Combining Density Clustering and Neighbor Residues Improves the Accuracy. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_39
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DOI: https://doi.org/10.1007/978-3-319-22186-1_39
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