Xue-wen Chen1, Jong Cheol Jeong. 1. Bioinformatics and Computational Life Sciences Laboratory, Information and Telecommunication Technology Center, University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu
Abstract
MOTIVATION: Identification of protein interaction sites has significant impact on understanding protein function, elucidating signal transduction networks and drug design studies. With the exponentially growing protein sequence data, predictive methods using sequence information only for protein interaction site prediction have drawn increasing interest. In this article, we propose a predictive model for identifying protein interaction sites. Without using any structure data, the proposed method extracts a wide range of features from protein sequences. A random forest-based integrative model is developed to effectively utilize these features and to deal with the imbalanced data classification problem commonly encountered in binding site predictions. RESULTS: We evaluate the predictive method using 2829 interface residues and 24,616 non-interface residues extracted from 99 polypeptide chains in the Protein Data Bank. The experimental results show that the proposed method performs significantly better than two other sequence-based predictive methods and can reliably predict residues involved in protein interaction sites. Furthermore, we apply the method to predict interaction sites and to construct three protein complexes: the DnaK molecular chaperone system, 1YUW and 1DKG, which provide new insight into the sequence-function relationship. We show that the predicted interaction sites can be valuable as a first approach for guiding experimental methods investigating protein-protein interactions and localizing the specific interface residues. AVAILABILITY: Datasets and software are available at http://ittc.ku.edu/~xwchen/bindingsite/prediction.
MOTIVATION: Identification of protein interaction sites has significant impact on understanding protein function, elucidating signal transduction networks and drug design studies. With the exponentially growing protein sequence data, predictive methods using sequence information only for protein interaction site prediction have drawn increasing interest. In this article, we propose a predictive model for identifying protein interaction sites. Without using any structure data, the proposed method extracts a wide range of features from protein sequences. A random forest-based integrative model is developed to effectively utilize these features and to deal with the imbalanced data classification problem commonly encountered in binding site predictions. RESULTS: We evaluate the predictive method using 2829 interface residues and 24,616 non-interface residues extracted from 99 polypeptide chains in the Protein Data Bank. The experimental results show that the proposed method performs significantly better than two other sequence-based predictive methods and can reliably predict residues involved in protein interaction sites. Furthermore, we apply the method to predict interaction sites and to construct three protein complexes: the DnaK molecular chaperone system, 1YUW and 1DKG, which provide new insight into the sequence-function relationship. We show that the predicted interaction sites can be valuable as a first approach for guiding experimental methods investigating protein-protein interactions and localizing the specific interface residues. AVAILABILITY: Datasets and software are available at http://ittc.ku.edu/~xwchen/bindingsite/prediction.