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Title | Prediction of DNA-binding residues from sequence. |
Publication Type | Journal Article |
Year of Publication | 2007 |
Authors | Ofran, Y, Mysore, V, Rost, B |
Journal | Bioinformatics |
Volume | 23 |
Issue | 13 |
Pagination | i347-53 |
Date Published | 2007 Jul 1 |
ISSN | 1367-4811 |
Keywords | Algorithms, Amino Acid Sequence, Amino Acids, Computer Simulation, DNA, DNA-Binding Proteins, Models, Chemical, Models, Molecular, Molecular Sequence Data, Protein Conformation, Sequence Analysis, Protein |
Abstract | MOTIVATION: Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available.RESULTS: It has been shown that DNA-binding residues have distinct biophysical characteristics. Here we demonstrate that these characteristics are so distinct that they enable accurate prediction of the residues that bind DNA directly from amino acid sequence, without requiring any additional experimental or structural information. In a cross-validation based on the largest non-redundant dataset of high-resolution protein-DNA complexes available today, we found that 89% of our predictions are confirmed by experimental data. Thus, it is now possible to identify DNA-binding sites on a proteomic scale even in the absence of any experimental data or 3D-structural information.AVAILABILITY: http://cubic.bioc.columbia.edu/services/disis. |
DOI | 10.1093/bioinformatics/btm174 |
Alternate Journal | Bioinformatics |
PubMed ID | 17646316 |
Grant List | 1-R01-GM64633 / GM / NIGMS NIH HHS / United States 2-R01-LM007329 / LM / NLM NIH HHS / United States |