DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins

  • Authors:

  • Seungwoo Hwang;Zhenkun Gou;Igor B. Kuznetsov

  • Affiliations:

  • Gen*NY*Sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, One Discovery Drive, University at Albany, Rensselaer, NY 12144, USA;Gen*NY*Sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, One Discovery Drive, University at Albany, Rensselaer, NY 12144, USA;Gen*NY*Sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, One Discovery Drive, University at Albany, Rensselaer, NY 12144, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2007

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Abstract

Summary: This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence. Availability: Freely available at http://lcg.rit.albany.edu/dp-bind Contact: IKuznetsov@albany.edu Supplementry information: http://lcg.rit.albany.edu/dp-bind/dpbind_supplement.html