Journal Article

iDBPs: a web server for the identification of DNA binding proteins

Guy Nimrod, Maya Schushan, András Szilágyi, Christina Leslie and Nir Ben-Tal

in Bioinformatics

Volume 26, issue 5, pages 692-693
Published in print March 2010 | ISSN: 1367-4803
Published online January 2010 | e-ISSN: 1460-2059 | DOI:

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Summary: The iDBPs server uses the three-dimensional (3D) structure of a query protein to predict whether it binds DNA. First, the algorithm predicts the functional region of the protein based on its evolutionary profile; the assumption is that large clusters of conserved residues are good markers of functional regions. Next, various characteristics of the predicted functional region as well as global features of the protein are calculated, such as the average surface electrostatic potential, the dipole moment and cluster-based amino acid conservation patterns. Finally, a random forests classifier is used to predict whether the query protein is likely to bind DNA and to estimate the prediction confidence. We have trained and tested the classifier on various datasets and shown that it outperformed related methods. On a dataset that reflects the fraction of DNA binding proteins (DBPs) in a proteome, the area under the ROC curve was 0.90. The application of the server to an updated version of the N-Func database, which contains proteins of unknown function with solved 3D-structure, suggested new putative DBPs for experimental studies.



Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1178 words. 

Subjects: Bioinformatics and Computational Biology

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