Abstract
BackgroundCharacterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment) method designed to produce many such sequences.ResultsWe analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP), and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones) in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay) and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation) trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites.ConclusionWe think that training binding site detection algorithms on datasets from binding assays lead to better prediction. The improvements in accuracy came from the unbiased nature of the SELEX dataset rather than from the number of sites available. We believe that with progress in short-read sequencing technology, one could use SELEX methods to characterize binding affinities of many low specificity transcription factors.
Highlights
Characterizing transcription factor binding motifs is a common bioinformatics task
Different methods have been employed for abstracting patterns or "motifs" from the sequences that bind particular transcription factors (TFs) leading to predictions of likely binding sites in the genome of the organism under study
(page number not for citation purposes) http://www.biomedcentral.com/1472-6750/8/94 sequences through a quality filter to find sequences that were likely to have been bound by a Catabolic Activator Protein (CAP) dimer in the random region of the SELEX pool
Summary
Characterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One of the best understood control mechanisms is the binding of transcription factors (TFs) to the regulatory sites on DNA in a sequence-specific manner, which affects transcription initiation [1]. Different methods have been employed for abstracting patterns or "motifs" from the sequences that bind particular TFs leading to predictions of likely binding sites in the genome of the organism under study. Factors regulating multiple genes often have binding motifs low in information content [4], making the task of prediction harder. Examples of such highly pleiotropic proteins range from global regulators in prokaryotes CAP, LRP, FIS, IHF, H-NS, HU, σ factors [5] in E. coli) to Hox proteins [6], important in metazoan development
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