Transcription factors are DNA-binding proteins that control gene transcription by binding specific short DNA sequences. Experiments that identify transcription factor binding sites are often laborious and expensive, and the binding sites of many transcription factors remain unknown. We present a computational scheme to predict the binding sites directly from transcription factor sequence using all-atom molecular simulations. This method is a computational counterpart to recent high-throughput experimental technologies that identify transcription factor binding sites (ChIP-chip and protein-dsDNA binding microarrays). The only requirement of our method is an accurate 3D structural model of a transcription factor-DNA complex. We apply free energy calculations by thermodynamic integration to compute the change in binding energy of the complex due to a single base pair mutation. By calculating the binding free energy differences for all possible single mutations, we construct a position weight matrix for the predicted binding sites that can be directly compared with experimental data. As water-bridged hydrogen bonds between the transcription factor and DNA often contribute to the binding specificity, we include explicit solvent in our simulations. We present successful predictions for the yeast MAT-alpha2 homeodomain and GCN4 bZIP proteins. Water-bridged hydrogen bonds are found to be more prevalent than direct protein-DNA hydrogen bonds at the binding interfaces, indicating why empirical potentials with implicit water may be less successful in predicting binding. Our methodology can be applied to a variety of DNA-binding proteins.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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