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Prediction of RNA-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature

Authors

  • Xin Ma,

    1. State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
    2. Department of Elementary Courses, Golden Audit College, Nanjing Audit University, Nanjing 210029, People's Republic of China
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  • Jing Guo,

    1. State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
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  • Jiansheng Wu,

    1. Department of Bioinformatics, School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210046, People's Republic of China
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  • Hongde Liu,

    1. State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
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  • Jiafeng Yu,

    1. State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
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  • Jianming Xie,

    1. State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People's Republic of China
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  • Xiao Sun

    Corresponding author
    1. 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
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Abstract

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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