Abstract
Site-specific transcription factors (TFs) bind to their target sites on the DNA, where they regulate the rate at which genes are transcribed. Bacterial TFs undergo facilitated diffusion (a combination of 3D diffusion around and 1D random walk on the DNA) when searching for their target sites. Using computer simulations of this search process, we show that the organization of the binding sites, in conjunction with TF copy number and binding site affinity, plays an important role in determining not only the steady state of promoter occupancy, but also the order at which TFs bind. These effects can be captured by facilitated diffusion-based models, but not by standard thermodynamics. We show that the spacing of binding sites encodes complex logic, which can be derived from combinations of three basic building blocks: switches, barriers and clusters, whose response alone and in higher orders of organization we characterize in detail. Effective promoter organizations are commonly found in the E. coli genome and are highly conserved between strains. This will allow studies of gene regulation at a previously unprecedented level of detail, where our framework can create testable hypothesis of promoter logic.
Highlights
Bacterial promoters are often complex, containing many densely spaced and potentially overlapping transcription factor (TF) binding sites [1]
The prediction of TF binding to promoters has received significant attention in the literature, as it is the first step towards developing mechanistic models of gene expression
The underlying principle behind this assumption is that the cell operates as a well-stirred reactor, which can lead to misleading results because of rapid TF-DNA rebindings [40]
Summary
Bacterial promoters are often complex, containing many densely spaced and potentially overlapping transcription factor (TF) binding sites [1]. PWMs are used alongside statistical thermodynamic-based methods to incorporate additional properties influencing TF binding, such as TF concentration and spatial hindrance between TFs [3,4,5,6,7,8,9,10,11,12,13,14]. These thermodynamic ensemble models assume that the probability of a configuration occurring is directly correlated with the thermodynamic stability of that configuration, which is primarily influenced by the binding site affinities and protein abundances. To model promoter configuration without requiring strong assumptions about the presence of thermodynamic equilibrium, the kinetics underlying TF binding must be taken into account
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