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This article is part of the supplement: The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08)

Open Access Research

Prediction of DNA-binding residues from protein sequence information using random forests

Liangjiang Wang12*, Mary Qu Yang3 and Jack Y Yang4

Author Affiliations

1 Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA

2 J.C. Self Research Institute of Human Genetics, Greenwood Genetic Center, Greenwood, SC 29646, USA

3 National Human Genome Research Institute, National Institutes of Health (NIH), U.S. Department of Health and Human Services, Bethesda, MD 20852, USA

4 Harvard Medical School, Harvard University, P.O. Box 400888, Cambridge, MA 02115, USA

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BMC Genomics 2009, 10(Suppl 1):S1  doi:10.1186/1471-2164-10-S1-S1

Published: 7 July 2009

Abstract

Background

Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data.

Results

A new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures.

Conclusion

The results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF http://bioinfo.ggc.org/bindn-rf/ webcite has thus been developed to make the RF classifier accessible to the biological research community.