Genome-wide organization of eukaryotic pre-initiation complex is influenced by nonconsensus protein-DNA binding

Genome-wide binding preferences of the key components of eukaryotic pre-initiation complex (PIC) have been recently measured with high resolution in Saccharomyces cerevisiae by Rhee and Pugh (Nature (2012) 483:295-301). Yet the rules determining the PIC binding specificity remain poorly understood. In this study we show that nonconsensus protein-DNA binding significantly influences PIC binding preferences. We estimate that such nonconsensus binding contribute statistically at least 2-3 kcal/mol (on average) of additional attractive free energy per protein, per core promoter region. The predicted attractive effect is particularly strong at repeated poly(dA:dT) and poly(dC:dG) tracts. Overall, the computed free energy landscape of nonconsensus protein-DNA binding shows strong correlation with the measured genome-wide PIC occupancy. Remarkably, statistical PIC binding preferences to both TFIID-dominated and SAGA-dominated genes correlate with the nonconsensus free energy landscape, yet these two groups of genes are distinguishable based on the average free energy profiles. We suggest that the predicted nonconsensus binding mechanism provides a genome-wide background for specific promoter elements, such as transcription factor binding sites, TATA-like elements, and specific binding of the PIC components to nucleosomes. We also show that nonconsensus binding influences transcriptional frequency genome-wide

Sequence correlations shape protein promiscuity

We predict analytically that diagonal correlations of amino acid positions within protein sequences statistically enhance protein propensity for nonspecific binding. We use the term 'promiscuity' to describe such nonspecific binding. Diagonal correlations represent statistically significant repeats of sequence patterns where amino acids of the same type are clustered together. The predicted effect is qualitatively robust with respect to the form of the microscopic interaction potentials and the average amino acid composition. Our analytical results provide an explanation for the enhanced diagonal correlations observed in hubs of eukaryotic organismal proteomes [J. Mol. Biol. 409, 439 (2011)]. We suggest experiments that will allow direct testing of the predicted effect

Nonspecific Transcription-Factor-DNA Binding Influences Nucleosome Occupancy in Yeast

AbstractQuantitative understanding of the principles regulating nucleosome occupancy on a genome-wide level is a central issue in eukaryotic genomics. Here, we address this question using budding yeast, Saccharomyces cerevisiae, as a model organism. We perform a genome-wide computational analysis of the nonspecific transcription factor (TF)-DNA binding free-energy landscape and compare this landscape with experimentally determined nucleosome-binding preferences. We show that DNA regions with enhanced nonspecific TF-DNA binding are statistically significantly depleted of nucleosomes. We suggest therefore that the competition between TFs with histones for nonspecific binding to genomic sequences might be an important mechanism influencing nucleosome-binding preferences in vivo. We also predict that poly(dA:dT) and poly(dC:dG) tracts represent genomic elements with the strongest propensity for nonspecific TF-DNA binding, thus allowing TFs to outcompete nucleosomes at these elements. Our results suggest that nonspecific TF-DNA binding might provide a barrier for statistical positioning of nucleosomes throughout the yeast genome. We predict that the strength of this barrier increases with the concentration of DNA binding proteins in a cell. We discuss the connection of the proposed mechanism with the recently discovered pathway of active nucleosome reconstitution

Multi-scale sequence correlations increase proteome structural disorder and promiscuity

Numerous experiments demonstrate a high level of promiscuity and structural disorder in organismal proteomes. Here we ask the question what makes a protein promiscuous, i.e., prone to non-specific interactions, and structurally disordered. We predict that multi-scale correlations of amino acid positions within protein sequences statistically enhance the propensity for promiscuous intra- and inter-protein binding. We show that sequence correlations between amino acids of the same type are statistically enhanced in structurally disordered proteins and in hubs of organismal proteomes. We also show that structurally disordered proteins possess a significantly higher degree of sequence order than structurally ordered proteins. We develop an analytical theory for this effect and predict the robustness of our conclusions with respect to the amino acid composition and the form of the microscopic potential between the interacting sequences. Our findings have implications for understanding molecular mechanisms of protein aggregation diseases induced by the extension of sequence repeats

DNA sequence correlations shape nonspecific transcription factor-DNA binding affinity

Transcription factors (TFs) are regulatory proteins that bind DNA in promoter regions of the genome and either promote or repress gene expression. Here we predict analytically that enhanced homo-oligonucleotide sequence correlations, such as poly(dA:dT) and poly(dC:dG) tracts, statistically enhance non-specific TF-DNA binding affinity. This prediction is generic and qualitatively independent of microscopic parameters of the model. We show that non-specific TF binding affinity is universally controlled by the strength and symmetry of DNA sequence correlations. We perform correlation analysis of the yeast genome and show that DNA regions highly occupied by TFs exhibit stronger homo-oligonucleotide sequence correlations, and thus higher propensity for non-specific binding, as compared with poorly occupied regions. We suggest that this effect plays the role of an effective localization potential enhancing the quasi-one-dimensional diffusion of TFs in the vicinity of DNA, speeding up the stochastic search process for specific TF binding sites. The predicted effect also imposes an upper bound on the size of TF-DNA binding motifs

Nonspecific Protein-DNA Binding Is Widespread in the Yeast Genome

Recent genome-wide measurements of binding preferences of ~200 transcription regulators in the vicinity of transcription start sites in yeast, have provided a unique insight into the cis- regulatory code of a eukaryotic genome (Venters et al., Mol. Cell 41, 480 (2011)). Here, we show that nonspecific transcription factor (TF)-DNA binding significantly influences binding preferences of the majority of transcription regulators in promoter regions of the yeast genome. We show that promoters of SAGA-dominated and TFIID-dominated genes can be statistically distinguished based on the landscape of nonspecific protein-DNA binding free energy. In particular, we predict that promoters of SAGA-dominated genes possess wider regions of reduced free energy compared to promoters of TFIID-dominated genes. We also show that specific and nonspecific TF-DNA binding are functionally linked and cooperatively influence gene expression in yeast. Our results suggest that nonspecific TF-DNA binding is intrinsically encoded into the yeast genome, and it may play a more important role in transcriptional regulation than previously thought

Hydration interactions: aqueous solvent effects in electric double layers

A model for ionic solutions with an attractive short-range pair interaction between the ions is presented. The short-range interaction is accounted for by adding a quadratic non-local term to the Poisson-Boltzmann free energy. The model is used to study solvent effects in a planar electric double layer. The counter-ion density is found to increase near the charged surface, as compared with the Poisson-Boltzmann theory, and to decrease at larger distances. The ion density profile is studied analytically in the case where the ion distribution near the plate is dominated only by counter-ions. Further away from the plate the density distribution can be described using a Poisson-Boltzmann theory with an effective surface charge that is smaller than the actual one.Comment: 11 Figures in 13 files + LaTex file. 20 pages. Accepted to Phys. Rev. E. Corrected typos and reference

Energy Fluctuations Shape Free Energy of Nonspecific Biomolecular Interactions

Understanding design principles of biomolecular recognition is a key question of molecular biology. Yet the enormous complexity and diversity of biological molecules hamper the efforts to gain a predictive ability for the free energy of protein-protein, protein-DNA, and protein-RNA binding. Here, using a variant of the Derrida model, we predict that for a large class of biomolecular interactions, it is possible to accurately estimate the relative free energy of binding based on the fluctuation properties of their energy spectra, even if a finite number of the energy levels is known. We show that the free energy of the system possessing a wider binding energy spectrum is almost surely lower compared with the system possessing a narrower energy spectrum. Our predictions imply that low-affinity binding scores, usually wasted in protein-protein and protein-DNA docking algorithms, can be efficiently utilized to compute the free energy. Using the results of Rosetta docking simulations of protein-protein interactions from Andre et al. (Proc. Natl. Acad. Sci. USA 105: 16148, 2008), we demonstrate the power of our predictions