Journal Article

Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature

Jiansheng Wu, Hongde Liu, Xueye Duan, Yan Ding, Hongtao Wu, Yunfei Bai and Xiao Sun

in Bioinformatics

Volume 25, issue 1, pages 30-35
Published in print January 2009 | ISSN: 1367-4803
Published online November 2008 | e-ISSN: 1460-2059 | DOI:

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Motivation: In this work, we aim to develop a computational approach for predicting DNA-binding sites in proteins from amino acid sequences. To avoid overfitting with this method, all available DNA-binding proteins from the Protein Data Bank (PDB) are used to construct the models. The random forest (RF) algorithm is used because it is fast and has robust performance for different parameter values. A novel hybrid feature is presented which incorporates evolutionary information of the amino acid sequence, secondary structure (SS) information and orthogonal binary vector (OBV) information which reflects the characteristics of 20 kinds of amino acids for two physical–chemical properties (dipoles and volumes of the side chains). The numbers of binding and non-binding residues in proteins are highly unbalanced, so a novel scheme is proposed to deal with the problem of imbalanced datasets by downsizing the majority class.

Results: The results show that the RF model achieves 91.41% overall accuracy with Matthew's correlation coefficient of 0.70 and an area under the receiver operating characteristic curve (AUC) of 0.913. To our knowledge, the RF method using the hybrid feature is currently the computationally optimal approach for predicting DNA-binding sites in proteins from amino acid sequences without using three-dimensional (3D) structural information. We have demonstrated that the prediction results are useful for understanding protein–DNA interactions.

Availability: DBindR web server implementation is freely available at


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4485 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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