Zhan-Chao Li1, Meng-Hua Huang1, Wen-Qian Zhong1, Zhi-Qing Liu1, Yun Xie1, Zong Dai2, Xiao-Yong Zou3. 1. School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China. 2. School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China. 3. SYSU-CMU Shunde International Joint Research Institute, Shunde 528300, People's Republic of China and School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
Abstract
MOTIVATION: Identifying drug-target protein interaction is a crucial step in the process of drug research and development. Wet-lab experiment are laborious, time-consuming and expensive. Hence, there is a strong demand for the development of a novel theoretical method to identify potential interaction between drug and target protein. RESULTS: We use all known proteins and drugs to construct a nodes- and edges-weighted biological relevant interactome network. On the basis of the 'guilt-by-association' principle, novel network topology features are proposed to characterize interaction pairs and random forest algorithm is employed to identify potential drug-protein interaction. Accuracy of 92.53% derived from the 10-fold cross-validation is about 10% higher than that of the existing method. We identify 2272 potential drug-target interactions, some of which are associated with diseases, such as Torg-Winchester syndrome and rhabdomyosarcoma. The proposed method can not only accurately predict the interaction between drug molecule and target protein, but also help disease treatment and drug discovery. CONTACTS: zhanchao8052@gmail.com or ceszxy@mail.sysu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Identifying drug-target protein interaction is a crucial step in the process of drug research and development. Wet-lab experiment are laborious, time-consuming and expensive. Hence, there is a strong demand for the development of a novel theoretical method to identify potential interaction between drug and target protein. RESULTS: We use all known proteins and drugs to construct a nodes- and edges-weighted biological relevant interactome network. On the basis of the 'guilt-by-association' principle, novel network topology features are proposed to characterize interaction pairs and random forest algorithm is employed to identify potential drug-protein interaction. Accuracy of 92.53% derived from the 10-fold cross-validation is about 10% higher than that of the existing method. We identify 2272 potential drug-target interactions, some of which are associated with diseases, such as Torg-Winchester syndrome and rhabdomyosarcoma. The proposed method can not only accurately predict the interaction between drug molecule and target protein, but also help disease treatment and drug discovery. CONTACTS: zhanchao8052@gmail.com or ceszxy@mail.sysu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Y Liu; M Brossard; C Sarnowski; A Vaysse; M Moffatt; P Margaritte-Jeannin; F Llinares-López; M H Dizier; M Lathrop; W Cookson; E Bouzigon; F Demenais Journal: Sci Rep Date: 2017-04-20 Impact factor: 4.379