Protein-protein recognition prediction using support vector machine based on feature vectors

Analysis of protein-protein recognition is a popular issue recently, which plays a crucial role in regulation of biochemical pathways and signaling transmittal. A protein is recognized with the other protein by combining a transient complex, otherwise which will become a permanent complex. Therefore, understanding physico-chemical properties of the protein interface can offer important clues for biological processes and functions. In this paper, we propose prediction method for protein-protein recognition based on features extracted from the residues. Residues on binding sites of two contacting proteins in a complex are projected from 3D to 2D plane. In order to have the same direction, each 2D plane is rotated by an angle decided by principal component analysis (PCA) method. Then, the 2D plane is partitioned into a 5x5 grid. The feature vector is composed of the residues distribution of polarity, electricity, and hydrophobicity on the 2D plane. Support vector machine (SVM) is adopted for prediction. Experimental results show that the prediction achieves an accuracy rate of 80%.