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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Keskin, O., Ma, B., Nussinov, R.: Hot regions in protein-protein interactions: the organization and contribution of structurally conserved hot spot residues. J. Mol. Biol. 345, 1281–1294 (2005)
Ma, B., Nussinov, R.: Druggable orthosteric and allosteric hot spots to target protein-protein interactions. Curr. Pharm. Des. 20, 1293–1301 (2014)
Cukuroglu, E., Gursoy, A., Keskin, O.: HotRegion: A database of predicted hot spot clusters. Nucleic Acids Res. 40, 829–833 (2012)
Tuncbag, N., Gursoy, A., Keskin, O.: Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics 25, 1513–1520 (2009)
Hsu, C.M., Chen, C.Y., Liu, B.J., Huang, C.C., Laio, M.H., Lin, C.C., et al.: Identification of hot regions in protein-protein interactions by sequential pattern mining. BMC Bioinformatics 8(5), S8 (2007)
Pons, C., Glaser, F., Fernandez-Recio, J.: Prediction of protein-binding areas by small-world residue networks and application to docking. BMC Bioinform. 12, 378 (2011)
Han, J.W., Kamber, M., Pei, J.: Data Mining Concepts and Techniques. China Machine Press, Beijing (2012)
Moal, I.H., Fernandez-Recio, J.: SKEMPI: A structural kinetic and energetic database of mutant protein interactions and its use in empirical models. Bioinformatics 28, 2600–2607 (2012)
Xia, J.F., Zhao, X.M., Song, J., Huang, D.S.: APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility. BMC Bioinform. 11, 174 (2010)
Mihel, J., Sikic, M., Tomic, S., Jeren, B., Vlahovicek, K.: PSAIA - protein structure and interaction analyzer. BMC Struct. Biol. 8, 21 (2008)
A Library for Support Vector Machines. http://www.csie.ntu.edu.tw
Hu, J., Zhang, X.L., Liu, X.M., Tang, J.S.: Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification. Comput. Biol. Med. 61, 127–137 (2015)
Liu, X.M., Tang, J.S.: Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selecting method. IEEE Syst. J. 99, 1932–8184 (2013)
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., et al.: The protein data bank. Nucleic Acids Res. 28, 235–242 (2000)
Python Molecule, http://www.pymol.org
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-22186-1_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22185-4
Online ISBN: 978-3-319-22186-1
eBook Packages: Computer ScienceComputer Science (R0)