Use of an Evolutionary Model to Provide Evidence for a Wide Heterogeneity of Required Affinities Between Transcription Factors and Their Binding Sites in Yeast

Lusk, Richard W.

doi:10.1142/9789812776136_0047

Cited by 4 publications

(3 citation statements)

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“…The appropriate length of flanking sequence is probably highly factor-specific. Lusk and Eisen [31] recently observed that the “cutoff score” used to imply significance for PWM-based binding site predictions is probably variable across factors, and the same will certainly be true for DWM-based predictions. Therefore, while DWMs represent a significant advance over PWMs in predictive power, a “one-size-fit-all” solution to the problem of binding-site prediction is unlikely to exist.…”

Section: Discussionmentioning

confidence: 99%

Dinucleotide Weight Matrices for Predicting Transcription Factor Binding Sites: Generalizing the Position Weight Matrix

Siddharthan

2010

PLoS ONE

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BackgroundIdentifying transcription factor binding sites (TFBS) in silico is key in understanding gene regulation. TFBS are string patterns that exhibit some variability, commonly modelled as “position weight matrices” (PWMs). Though convenient, the PWM has significant limitations, in particular the assumed independence of positions within the binding motif; and predictions based on PWMs are usually not very specific to known functional sites. Analysis here on binding sites in yeast suggests that correlation of dinucleotides is not limited to near-neighbours, but can extend over considerable gaps.Methodology/Principal FindingsI describe a straightforward generalization of the PWM model, that considers frequencies of dinucleotides instead of individual nucleotides. Unlike previous efforts, this method considers all dinucleotides within an extended binding region, and does not make an attempt to determine a priori the significance of particular dinucleotide correlations. I describe how to use a “dinucleotide weight matrix” (DWM) to predict binding sites, dealing in particular with the complication that its entries are not independent probabilities. Benchmarks show, for many factors, a dramatic improvement over PWMs in precision of predicting known targets. In most cases, significant further improvement arises by extending the commonly defined “core motifs” by about 10bp on either side. Though this flanking sequence shows no strong motif at the nucleotide level, the predictive power of the dinucleotide model suggests that the “signature” in DNA sequence of protein-binding affinity extends beyond the core protein-DNA contact region.Conclusion/SignificanceWhile computationally more demanding and slower than PWM-based approaches, this dinucleotide method is straightforward, both conceptually and in implementation, and can serve as a basis for future improvements.

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Section: Discussionmentioning

confidence: 99%

Dinucleotide Weight Matrices for Predicting Transcription Factor Binding Sites: Generalizing the Position Weight Matrix

Siddharthan

2010

PLoS ONE

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“…By specifying binding site fitness in terms of binding Copyright © 2012 energy, these models are able to reconstruct quantitative fitness landscapes that can be used to study the evolution of transcription regulation. This has led to remarkable success in explaining such important features of transcription factor binding as the total binding energy associated with a functional binding site Berg et al 2004) and the average change in binding energy with individual mutations as well as some features of position weight matrices (PWMs) Sengupta et al 2002;Moses et al 2003;Lusk and Eisen 2008;Shultzaberger et al 2012). It has also led to estimates for the strength of selection on functional binding sites (Hahn et al 2003;Mustonen et al 2008;He et al 2011).…”

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confidence: 99%

Why Transcription Factor Binding Sites Are Ten Nucleotides Long

2012

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Gene expression is controlled primarily by transcription factors, whose DNA binding sites are typically 10 nt long. We develop a population-genetic model to understand how the length and information content of such binding sites evolve. Our analysis is based on an inherent trade-off between specificity, which is greater in long binding sites, and robustness to mutation, which is greater in short binding sites. The evolutionary stable distribution of binding site lengths predicted by the model agrees with the empirical distribution (5-31 nt, with mean 9.9 nt for eukaryotes), and it is remarkably robust to variation in the underlying parameters of population size, mutation rate, number of transcription factor targets, and strength of selection for proper binding and selection against improper binding. In a systematic data set of eukaryotic and prokaryotic transcription factors we also uncover strong relationships between the length of a binding site and its information content per nucleotide, as well as between the number of targets a transcription factor regulates and the information content in its binding sites. Our analysis explains these features as well as the remarkable conservation of binding site characteristics across diverse taxa. MUCH of the phenotypic divergence between species is driven by changes in transcriptional regulation, and especially by point mutations at transcription factor binding sites (Stern 2000;Carroll 2005;Ihmels et al. 2005;Prud'homme et al. 2006Prud'homme et al. , 2007Tsong et al. 2006;Wray 2007;Lemos et al. 2008; Tuch et al. 2008a,b). Such mutations can increase or decrease the affinity of a transcription factor protein to its binding sites, which in turn modifies the expression of regulated genes. Binding sites are typically $10 nt in length, in both eukaryotes and prokaryotes, although this number varies from as few as 5 to .30 nt (Figure 1). Binding sites are also characterized by their information content (D'haeseleer 2006), which is determined by the number of different bases that can occur at each nucleotide and still produce functional binding. The average information content varies from a maximum 2 bits per nucleotide (each nucleotide must assume a specific base to produce functional binding) to ,0.25 bits (each nucleotide can assume one of several bases and still produce functional binding).What determines the length and information content of a transcription factor binding site? Biophysical factors provide some constraints, and numerous studies have explored their effects on the function of individual transcription factor binding sites Berg et al. 2004;Bintu et al. 2005;Shultzaberger et al. 2007;Mustonen et al. 2008;Gerland and Hwa 2009). Natural selection also plays an important role because, to produce the correct patterns of gene expression, transcription factors must correctly bind to some sites in the genome and avoid binding elsewhere (Sengupta et al. 2002;Shultzaberger et al. 2012;. If binding sites are too short, transcription factors bind too readily to...

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“…The typical gene has approximately 12 transcription factor binding sites [the distribution of this across genomes is not well characterized, and this number is given with an approximation of six to eight base pairs (Harbison et al, 2004;]. The specificity of binding typically enables transcription factors to discriminate among many sites with single-base-pair mutations (Lusk and Eisen, 2008). Because of the small size of transcription factor-binding sites, site loss and de novo site evolution are reasonably common, and this is explored further below.…”

Section: Mutational Dynamics and Substitutionsmentioning

confidence: 99%

Understanding Gene Duplication Through Biochemistry and Population Genetics

Liberles

Kolesov

Dittmar

2010

Evolution After Gene Duplication

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Gene duplication has emerged as an important process supporting the functional diversification of genes. Since publication of the seminal book Evolution by Gene Duplication by Ohno (1970), the hypothesis regarding the importance of gene duplication in the generation of evolutionary novelty has steadily gained support as we have entered the genome-sequencing era. It is through the link to functional biology that an ultimate understanding of the preservation and diversification of duplicate genes will be accomplished.Genes can diverge in function through accumulation (fixation) of coding sequence changes, which may influence binding interactions and/or catalysis, through the evolution of splice variants, and through spatial, temporal, and concentration-level changes in the expression of the protein product. Governing these processes is an interplay among mutational opportunity, population dynamics, protein biochemistry, and systems and organismal biology. This interplay is described systematically in this chapter. SYSTEMS BIOLOGY AND HIGHER-LEVEL ORGANIZATIONAt the level of biological systems, two early but still relevant views suggested a role for gene duplication in constructing pathways. These views are both dependent on a new function emerging in one of the duplicates, but differ in the manner in which it occurs. One view, patchwork evolution, involved a conservation of catalytic activity coupled with the evolution of a new substrate after duplication (Jensen, 1976). An alternative view, retrograde evolution, suggested that pathways are built up backward, with product becoming substrate based on recognition of the transition state in the Evolution After Gene Duplication, Edited by Katharina Dittmar and David Liberles

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Use of an Evolutionary Model to Provide Evidence for a Wide Heterogeneity of Required Affinities Between Transcription Factors and Their Binding Sites in Yeast

Cited by 4 publications

References 14 publications

Dinucleotide Weight Matrices for Predicting Transcription Factor Binding Sites: Generalizing the Position Weight Matrix

Dinucleotide Weight Matrices for Predicting Transcription Factor Binding Sites: Generalizing the Position Weight Matrix

Why Transcription Factor Binding Sites Are Ten Nucleotides Long

Understanding Gene Duplication Through Biochemistry and Population Genetics

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