ArticleBMC bioinformatics2005
Quantitative analysis of EGR proteins binding to DNA: assessing additivity in both the binding site and the protein.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Spec-seq: determining protein-DNA-binding specificity by sequencing.Briefings in functional genomics · 2015Review
- High-resolution specificity from DNA sequencing highlights alternative modes of Lac repressor binding.Genetics · 2014Article
- Screening of potential pseudo att sites of Streptomyces phage ΦC31 integrase in the human genome.Acta pharmacologica Sinica · 2013Article
- A flexible integrative approach based on random forest improves prediction of transcription factor binding sites.Nucleic acids research · 2012Article
- Purification of proteins containing zinc finger domains using immobilized metal ion affinity chromatography.Protein expression and purification · 2011Article
- Using a structural and logics systems approach to infer bHLH-DNA binding specificity determinants.Nucleic acids research · 2011Article
- Optimization of minimum set of protein-DNA interactions: a quasi exact solution with minimum over-fitting.Bioinformatics (Oxford, England) · 2010Article
- Experimentally based contact energies decode interactions responsible for protein-DNA affinity and the role of molecular waters at the binding interface.Nucleic acids research · 2009Article
- An affinity-based scoring scheme for predicting DNA-binding activities of modularly assembled zinc-finger proteins.Nucleic acids research · 2009Article
- Protein-DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins.Nucleic acids research · 2008Article
- Context-dependent DNA recognition code for C2H2 zinc-finger transcription factors.Bioinformatics (Oxford, England) · 2008Article
- Limited functional conservation of a global regulator among related bacterial genera: Lrp in Escherichia, Proteus and Vibrio.BMC microbiology · 2008Article
- Algorithm for prediction of tumour suppressor p53 affinity for binding sites in DNA.Nucleic acids research · 2008Article
- Predicted transcription factor binding sites as predictors of operons in Escherichia coli and Streptomyces coelicolor.BMC genomics · 2008Article
- Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities.Nature biotechnology · 2006Article
- Free-energy distribution of binary protein-protein binding suggests cross-species interactome differences.Proceedings of the National Academy of Sciences of the United States of America · 2006Article
- Combining SELEX with quantitative assays to rapidly obtain accurate models of protein-DNA interactions.Nucleic acids research · 2005Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundRecognition codes for protein-DNA interactions typically assume that the interacting positions contribute additively to the binding energy. While this is known to not be precisely true, an additive model over the DNA positions can be a good approximation, at least for some proteins. Much less information is available about whether the protein positions contribute additively to the interaction.
resultsUsing EGR zinc finger proteins, we measure the binding affinity of six different variants of the protein to each of six different variants of the consensus binding site. Both the protein and binding site variants include single and double mutations that allow us to assess how well additive models can account for the data. For each protein and DNA alone we find that additive models are good approximations, but over the combined set of data there are context effects that limit their accuracy. However, a small modification to the purely additive model, with only three additional parameters, improves the fit significantly.
conclusionThe additive model holds very well for every DNA site and every protein included in this study, but clear context dependence in the interactions was detected. A simple modification to the independent model provides a better fit to the complete data.
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