Identification of DNA-binding Proteins Using Structural, Electrostatic and Evolutionary Features

Guy Nimrod, András Szilágyi, Christina Leslie, Nir Ben-Tal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


DNA-binding proteins (DBPs) participate in various crucial processes in the life-cycle of the cells, and the identification and characterization of these proteins is of great importance. We present here a random forests classifier for identifying DBPs among proteins with known 3D structures. First, clusters of evolutionarily conserved regions (patches) on the surface of proteins were detected using the PatchFinder algorithm; earlier studies showed that these regions are typically the functionally important regions of proteins. Next, we trained a classifier using features like the electrostatic potential, cluster-based amino acid conservation patterns and the secondary structure content of the patches, as well as features of the whole protein, including its dipole moment. Using 10-fold cross-validation on a dataset of 138 DBPs and 110 proteins that do not bind DNA, the classifier achieved a sensitivity and a specificity of 0.90, which is overall better than the performance of published methods. Furthermore, when we tested five different methods on 11 new DBPs that did not appear in the original dataset, only our method annotated all correctly. The resulting classifier was applied to a collection of 757 proteins of known structure and unknown function. Of these proteins, 218 were predicted to bind DNA, and we anticipate that some of them interact with DNA using new structural motifs. The use of complementary computational tools supports the notion that at least some of them do bind DNA.

Original languageEnglish
Pages (from-to)1040-1053
Number of pages14
JournalJournal of Molecular Biology
Issue number4
StatePublished - 10 Apr 2009


FundersFunder number
National Institute of General Medical SciencesR01GM074257
National Institute of General Medical Sciences
Hungarian Scientific Research Fund


    • DNA-binding proteins
    • DNA-binding sites
    • PatchFinder
    • random forests
    • structural genomics


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