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Signatures of protein-DNA recognition in free DNA binding sites.

ABSTRACT: One obstacle to achieving complete understanding of the principles underlying sequence-dependent recognition of DNA is the paucity of structural data for DNA recognition sequences in their free (unbound) state. Here, we carried out crystallization screening of 50 DNA duplexes containing cognate protein binding sites and obtained new crystal structures of free DNA binding sites for three distinct modes of DNA recognition: anti-parallel beta strands (MetR), helix-turn-helix motif + hinge helices (PurR), and zinc fingers (Zif268). Structural changes between free and protein-bound DNA are manifested differently in each case. The new DNA structures reveal that distinctive sequence-dependent DNA geometry dominates recognition by MetR, protein-induced bending of DNA dictates recognition by PurR, and deformability of DNA along the A-B continuum is important in recognition by Zif268. Together, our findings show that crystal structures of free DNA binding sites provide new information about the nature of protein-DNA interactions and thus lend insights towards a structural code for DNA recognition.

SUBMITTER: Locasale JW

PROVIDER: S-EPMC2753591 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Signatures of protein-DNA recognition in free DNA binding sites.

Locasale Jason W JW Napoli Andrew A AA Chen Shengfeng S Berman Helen M HM Lawson Catherine L CL

Journal of molecular biology 20090301 4

One obstacle to achieving complete understanding of the principles underlying sequence-dependent recognition of DNA is the paucity of structural data for DNA recognition sequences in their free (unbound) state. Here, we carried out crystallization screening of 50 DNA duplexes containing cognate protein binding sites and obtained new crystal structures of free DNA binding sites for three distinct modes of DNA recognition: anti-parallel beta strands (MetR), helix-turn-helix motif + hinge helices ( ...[more]

PMID: 19244617

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Project description:Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.

Dataset Information

Signatures of protein-DNA recognition in free DNA binding sites.

Publications

Signatures of protein-DNA recognition in free DNA binding sites.

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