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Using evolutionary and structural information to predict DNA-binding sites on DNA-binding proteins

Authors

  • Igor B. Kuznetsov,

    Corresponding author
    1. Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University at Albany, Rensselaer, NewYork
    • Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University of Albany, One Discovery Drive, Room 206, Rensselaer, NY 12144
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  • Zhenkun Gou,

    1. Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University at Albany, Rensselaer, NewYork
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  • Run Li,

    1. Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University at Albany, Rensselaer, NewYork
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  • Seungwoo Hwang

    1. Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University at Albany, Rensselaer, NewYork
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Abstract

Proteins that interact with DNA are involved in a number of fundamental biological activities such as DNA replication, transcription, and repair. A reliable identification of DNA-binding sites in DNA-binding proteins is important for functional annotation, site-directed mutagenesis, and modeling protein–DNA interactions. We apply Support Vector Machine (SVM), a supervised pattern recognition method, to predict DNA-binding sites in DNA-binding proteins using the following features: amino acid sequence, profile of evolutionary conservation of sequence positions, and low-resolution structural information. We use a rigorous statistical approach to study the performance of predictors that utilize different combinations of features and how this performance is affected by structural and sequence properties of proteins. Our results indicate that an SVM predictor based on a properly scaled profile of evolutionary conservation in the form of a position specific scoring matrix (PSSM) significantly outperforms a PSSM-based neural network predictor. The highest accuracy is achieved by SVM predictor that combines the profile of evolutionary conservation with low-resolution structural information. Our results also show that knowledge-based predictors of DNA-binding sites perform significantly better on proteins from mainly-α structural class and that the performance of these predictors is significantly correlated with certain structural and sequence properties of proteins. These observations suggest that it may be possible to assign a reliability index to the overall accuracy of the prediction of DNA-binding sites in any given protein using its sequence and structural properties. A web-server implementation of the predictors is freely available online at http://lcg.rit.albany.edu/dp-bind/. Proteins 2006. © 2006 Wiley-Liss, Inc.

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