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DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues

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  • Xin Ma
  • Jing Guo
  • Xiao Sun

Abstract

DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

Suggested Citation

  • Xin Ma & Jing Guo & Xiao Sun, 2016. "DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-20, December.
  • Handle: RePEc:plo:pone00:0167345
    DOI: 10.1371/journal.pone.0167345
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    References listed on IDEAS

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    1. Bi-Qing Li & Le-Le Hu & Lei Chen & Kai-Yan Feng & Yu-Dong Cai & Kuo-Chen Chou, 2012. "Prediction of Protein Domain with mRMR Feature Selection and Analysis," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
    2. Wei-Zhong Lin & Jian-An Fang & Xuan Xiao & Kuo-Chen Chou, 2011. "iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-7, September.
    3. Bin Liu & Longyun Fang & Fule Liu & Xiaolong Wang & Junjie Chen & Kuo-Chen Chou, 2015. "Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-20, March.
    4. Bi-Qing Li & Yu-Dong Cai & Kai-Yan Feng & Gui-Jun Zhao, 2012. "Prediction of Protein Cleavage Site with Feature Selection by Random Forest," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
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    Cited by:

    1. Yu-Hui Qu & Hua Yu & Xiu-Jun Gong & Jia-Hui Xu & Hong-Shun Lee, 2017. "On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-18, December.

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