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Predicting Protein-Protein Interaction Sites using Radial Basis Function Neural Networks

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5 Author(s)
Bing Wang ; Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China; Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230026, China; Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong. e-mail: bwang76@mail.ustc.edu.cn ; Hau San Wong ; Peng Chen ; Hong-Qiang Wang
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Identifying protein-protein interaction sites is crucial for understanding of the principles of biological systems and processes, as well as mutant design. This paper describes a novel method that can predict protein interaction sites in heterocomplexes using information of evolutionary conservation and spatial sequence profile. A predictor was generated to distinguish the interface residues from protein surface region by radial basis neural networks, which is trained by expectation maximization algorithm. Based on a non-redundant data set of heterodimers consisting of 75 protein chains, the efficiency and the effectiveness of our proposed approach can be validated by a better performance such as the accuracy of 0.60, the sensitivity of 58.3% and the specificity of 59.9%.

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The 2006 IEEE International Joint Conference on Neural Network Proceedings

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