Computational Biology and Chemistry
Identification of DNA-Binding and Protein-Binding Proteins Using Enhanced Graph Wavelet Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Protein–DNA interaction has significant importance in many biological processes. However, the underlying principle of the molecular recognition process is still largely unknown. As more high-resolution 3D structures of protein–DNA complex are becoming available, the surface characteristics of the complex become an important research topic. Result: In our work, we apply an alpha shape model to represent the surface structure of the protein–DNA complex and developed an interface-atom curvature-dependent conditional probability discriminatory function for the prediction of protein–DNA interaction. The interface-atom curvature-dependent formalism captures atomic interaction details better than the atomic distance-based method. The proposed method provides good performance in discriminating the native structures from the docking decoy sets, and outperforms the distance-dependent formalism in terms of the z-score. Computer experiment results show that the curvature-dependent formalism with the optimal parameters can achieve a native z-score of -8.17 in discriminating the native structure from the highest surface-complementarity scored decoy set and a native z-score of -7.38 in discriminating the native structure from the lowest RMSD decoy set. The interface-atom curvature-dependent formalism can also be used to predict apo version of DNA-binding proteins. These results suggest that the interface-atom curvature-dependent formalism has a good prediction capability for protein–DNA interactions. Availability: The code and data sets are available for download on http://www.hy8.com/bioinformatics.htm Contact: kenandzhou@hotmail.com