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10.1073/pnas.1715888115 http://scihub22266oqcxt.onion/10.1073/pnas.1715888115 C5910820!5910820 !29588420     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29588420 &cmd=llinks): Failed to open stream: HTTP request failed! 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Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding |pdf=|usr=}}{{29588420 }} gab.com Text Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=29588420 #FOAvip Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites. Twit Text FOAVip Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=29588420 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29588420 #FOAvip English Wikipedia <ref>{{Cite pmid|29588420 }}</ref> Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding #MMPMID29588420 Le DD ; Shimko TC ; Aditham AK ; Keys AM ; Longwell SA ; Orenstein Y ; Fordyce PM Proc Natl Acad Sci U S A 2018[Apr]; 115 (16 ): E3702-E3711 PMID29588420 show ga Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites. |Base Sequence [MESH] |Basic Helix-Loop-Helix Leucine Zipper Transcription Factors/metabolism [MESH] |Binding Sites [MESH] |Computer Simulation [MESH] |DNA-Binding Proteins/metabolism [MESH] |DNA/*metabolism [MESH] |E-Box Elements [MESH] |Gene Library [MESH] |High-Throughput Nucleotide Sequencing/*methods [MESH] |Microfluidic Analytical Techniques [MESH] |Monte Carlo Method [MESH] |Protein Binding [MESH] |Saccharomyces cerevisiae Proteins/metabolism [MESH] |Sequence Analysis, DNA [MESH] |Thermodynamics [MESH] |Transcription Factors/*metabolism [MESH]Comprehensive, high-resolution binding energy landscapes reveal contex(epmc).pdf DeepDyve Pubget Overpricing