title: Ab initio prediction of transcription factor binding sites.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, High-Throughput Biology Center, Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: L Angela
authors.initials: LA
authors.lastname: Liu
authors.forename: Joel S
authors.initials: JS
authors.lastname: Bader
mesh_terms: D001483:Base Sequence; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology; D004247:DNA / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D004271:DNA, Fungal / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D006860:Hydrogen Bonding; D046911:Macromolecular Substances; D008958:Models, Molecular; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism; D013816:Thermodynamics; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*
chemical_list: D004271:DNA, Fungal; D046911:Macromolecular Substances; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors; D004247:DNA