Abstract
The most important mechanism in the regulation of transcription is the binding of a transcription factor (TF) to a DNA sequence called the TF binding site (TFBS). Most binding sites are short and degenerate, which makes predictions based on their primary sequence alone somewhat unreliable. We present a new web tool that implements a flexible and extensible algorithm for predicting TFBS. The algorithm makes use of both direct (the sequence) and several indirect readout features of protein–DNA complexes (biophysical properties such as bendability or the solvent-excluded surface of the DNA). This algorithm significantly outperforms state-of-the-art approaches for in silico identification of TFBS. Users can submit FASTA sequences for analysis in the PhysBinder integrative algorithm and choose from >60 different TF-binding models. The results of this analysis can be used to plan and steer wet-lab experiments. The PhysBinder web tool is freely available at http://bioit.dmbr.ugent.be/physbinder/index.php.
Highlights
Proteins called transcription factors (TFs) are crucial for proper regulation of gene expression. They function by binding to regions of DNA called transcription factor binding sites (TFBS)
Two different mechanisms contribute to the TF–DNA binding specificity needed for correct regulation of gene expression: a direct readout component caused by direct contact between the amino acids of the protein and the bases of the DNA and an indirect readout component caused by the global shape of the DNA and by conformational changes in both interaction partners [1,2]
We examined the effect of incorporating nucleotide position dependencies, which are related to the 3D structure of the DNA [7], on the prediction of TFBS [8]
Summary
Proteins called transcription factors (TFs) are crucial for proper regulation of gene expression. We examined the effect of incorporating nucleotide position dependencies, which are related to the 3D structure of the DNA [7], on the prediction of TFBS [8]. Binding sites for these models can be visualized together with the ENCODE TFBS data track of UCSC genome [9] to get a useful insight in the genomic context of the inspected region.
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