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iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model

Author

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  • Wei-Zhong Lin
  • Jian-An Fang
  • Xuan Xiao
  • Kuo-Chen Chou

Abstract

DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power. By incorporating the features into the general form of pseudo amino acid composition that were extracted from protein sequences via the “grey model” and by adopting the random forest operation engine, we proposed a new predictor, called iDNA-Prot, for identifying uncharacterized proteins as DNA-binding proteins or non-DNA binding proteins based on their amino acid sequences information alone. The overall success rate by iDNA-Prot was 83.96% that was obtained via jackknife tests on a newly constructed stringent benchmark dataset in which none of the proteins included has pairwise sequence identity to any other in a same subset. In addition to achieving high success rate, the computational time for iDNA-Prot is remarkably shorter in comparison with the relevant existing predictors. Hence it is anticipated that iDNA-Prot may become a useful high throughput tool for large-scale analysis of DNA-binding proteins. As a user-friendly web-server, iDNA-Prot is freely accessible to the public at the web-site on http://icpr.jci.edu.cn/bioinfo/iDNA-Prot or http://www.jci-bioinfo.cn/iDNA-Prot. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0024756
    DOI: 10.1371/journal.pone.0024756
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    Citations

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    Cited by:

    1. Yan Xu & Jun Ding & Ling-Yun Wu & Kuo-Chen Chou, 2013. "iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    2. 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.
    3. 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.
    4. Wenzheng Bao & Bin Yang & Rong Bao & Yuehui Chen, 2019. "LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree," Complexity, Hindawi, vol. 2019, pages 1-9, July.
    5. Xiao Wang & Guo-Zheng Li, 2012. "A Multi-Label Predictor for Identifying the Subcellular Locations of Singleplex and Multiplex Eukaryotic Proteins," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    6. Sabit Ahmed & Afrida Rahman & Md Al Mehedi Hasan & Md Khaled Ben Islam & Julia Rahman & Shamim Ahmad, 2021. "predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
    7. Wu Zhu & Jian-an Fang & Yang Tang & Wenbing Zhang & Wei Du, 2012. "Digital IIR Filters Design Using Differential Evolution Algorithm with a Controllable Probabilistic Population Size," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    8. 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.
    9. Jianjun He & Hong Gu & Wenqi Liu, 2012. "Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-10, June.

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