Prediction of RNA-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature
Xin Ma
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Department of Elementary Courses, Golden Audit College, Nanjing Audit University, Nanjing 210029, People's Republic of China
Search for more papers by this authorJing Guo
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Search for more papers by this authorJiansheng Wu
Department of Bioinformatics, School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210046, People's Republic of China
Search for more papers by this authorHongde Liu
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Search for more papers by this authorJiafeng Yu
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Search for more papers by this authorJianming Xie
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Search for more papers by this authorCorresponding Author
Xiao Sun
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China===Search for more papers by this authorXin Ma
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Department of Elementary Courses, Golden Audit College, Nanjing Audit University, Nanjing 210029, People's Republic of China
Search for more papers by this authorJing Guo
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Search for more papers by this authorJiansheng Wu
Department of Bioinformatics, School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210046, People's Republic of China
Search for more papers by this authorHongde Liu
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Search for more papers by this authorJiafeng Yu
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Search for more papers by this authorJianming Xie
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
Search for more papers by this authorCorresponding Author
Xiao Sun
State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China===Search for more papers by this authorAbstract
The identification of RNA-binding residues in proteins is important in several areas such as protein function, posttranscriptional regulation and drug design. We have developed PRBR (Prediction of RNA Binding Residues), a novel method for identifying RNA-binding residues from amino acid sequences. Our method combines a hybrid feature with the enriched random forest (ERF) algorithm. The hybrid feature is composed of predicted secondary structure information and three novel features: evolutionary information combined with conservation information of the physicochemical properties of amino acids and the information about dependency of amino acids with regards to polarity-charge and hydrophobicity in the protein sequences. Our results demonstrate that the PRBR model achieves 0.5637 Matthew's correlation coefficient (MCC) and 88.63% overall accuracy (ACC) with 53.70% sensitivity (SE) and 96.97% specificity (SP). By comparing the performance of each feature we found that all three novel features contribute to the improved predictions. Area under the curve (AUC) statistics from receiver operating characteristic curve analysis was compared between PRBR model and other models. The results show that PRBR achieves the highest AUC value (0.8675) which represents that PRBR attains excellent performance on predicting the RNA-binding residues in proteins. The PRBR web-server implementation is freely available at http://www.cbi.seu.edu.cn/PRBR/. Proteins 2011; © 2011 Wiley-Liss, Inc.
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