Abstract
SummaryDespite their diverse biochemical characteristics and functions, all DNA-binding proteins share the ability to accurately locate their target sites among the vast excess of non-target DNA. Toward identifying universal mechanisms of the target search, we used single-molecule tracking of 11 diverse DNA-binding proteins in living Escherichia coli. The mobility of these proteins during the target search was dictated by DNA interactions rather than by their molecular weights. By generating cells devoid of all chromosomal DNA, we discovered that the nucleoid is not a physical barrier for protein diffusion but significantly slows the motion of DNA-binding proteins through frequent short-lived DNA interactions. The representative DNA-binding proteins (irrespective of their size, concentration, or function) spend the majority (58%–99%) of their search time bound to DNA and occupy as much as ∼30% of the chromosomal DNA at any time. Chromosome crowding likely has important implications for the function of all DNA-binding proteins.
Highlights
DNA is organized into chromosomes that must be maintained in a highly compacted state while keeping the genetic information accessible for processing by many DNA-binding proteins
We found that the representative DNA-binding proteins spend the majority of their search time bound to DNA, occupying as much as $30% of the chromosomal DNA at any time
Live-cell, single-molecule tracking of a variety of DNAbinding proteins To uncover universal mechanisms that govern the target search process of DNA-binding proteins, we measured the diffusion characteristics of 11 proteins involved in various DNA transactions and spanning a large range of molecular weights and intracellular concentrations
Summary
DNA is organized into chromosomes that must be maintained in a highly compacted state while keeping the genetic information accessible for processing by many DNA-binding proteins. In the crowded and heterogeneous intracellular environment, a myriad of specific and non-specific interactions and steric effects influence the mobility of macromolecules Because of this complexity, efforts to understand molecular mobility have relied on phenomenological models (Kalwarczyk et al, 2012; Mika and Poolman, 2011) or coarsegrained simulations of the cytoplasm (Chow and Skolnick, 2017; Feig et al, 2015; Hasnain et al, 2014). Efforts to understand molecular mobility have relied on phenomenological models (Kalwarczyk et al, 2012; Mika and Poolman, 2011) or coarsegrained simulations of the cytoplasm (Chow and Skolnick, 2017; Feig et al, 2015; Hasnain et al, 2014) In this context, analysis of in vivo experimental data is crucial to determine parameter values and the structure of such models by informing which cellular components and interactions should be included in a model
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