Tumor Suppressor p53 Slides on DNA with Low Friction and High Stability

Tafvizi, Anahita; Huang, Fang; Leith, Jason S.; Fersht, Alan R.; Mirny, Leonid A.; Oijen, Antoine M. van

doi:10.1529/biophysj.108.134122

Cited by 162 publications

(209 citation statements)

References 17 publications

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“…The BHW model and its extensions provide a plausible mechanism for facilitated diffusion that has some support from experimental studies, which demonstrate that proteins do indeed slide along DNA (Gorman and Greene, 2008;Gowers et al, 2005;Li et al, 2009;Tafvizi et al, 2008;Winter et al, 1981). In particular, recent advances in single-molecule spectroscopy means that the motion of flourescently labeled proteins along DNA chains can be quantified with high precision, although it should be noted that most of these studies have been performed in vitro.…”

Section: Diffusion-limited Reaction Ratesmentioning

confidence: 97%

Stochastic models of intracellular transport

2013

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The interior of a living cell is a crowded, heterogenuous, fluctuating environment. Hence, a major challenge in modeling intracellular transport is to analyze stochastic processes within complex environments. Broadly speaking, there are two basic mechanisms for intracellular transport: passive diffusion and motor-driven active transport. Diffusive transport can be formulated in terms of the motion of an over-damped Brownian particle. On the other hand, active transport requires chemical energy, usually in the form of ATP hydrolysis, and can be direction specific, allowing biomolecules to be transported long distances; this is particularly important in neurons due to their complex geometry. In this review we present a wide range of analytical methods and models of intracellular transport. In the case of diffusive transport, we consider narrow escape problems, diffusion to a small target, confined and single-file diffusion, homogenization theory, and fractional diffusion. In the case of active transport, we consider Brownian ratchets, random walk models, exclusion processes, random intermittent search processes, quasisteady-state reduction methods, and mean field approximations. Applications include receptor trafficking, axonal transport, membrane diffusion, nuclear transport, protein-DNA interactions, virus trafficking, and the self-organization of subcellular structures.

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Section: Diffusion-limited Reaction Ratesmentioning

confidence: 97%

Stochastic models of intracellular transport

2013

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“…5,6 It is promiscuous in both function and activity, performing many roles in the cell. The p53 CTD binds DNA nonspecifically to promote linear diffusion along the DNA, 7,8 but it can also bind DNA in a more sequencespecific manner depending on its lysine acetylation state. 9,10 As a way to regulate this DNA-binding function, the p53 CTD also binds nonspecifically to RNA.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Relationship between the p53 C-Terminal Domain and Its Binding Partners Using Molecular Dynamics

et al. 2010

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Fifty percent of all cancer cases result from mutations of the TP53 gene, which encodes the tumor suppressor p53, and it is hypothesized that the p53-mediated checkpoint pathway is compromised in most of the remaining cases. The p53 C-terminal domain (CTD) is an important site of p53 regulation but by nature is difficult to study, as it is intrinsically disordered. In this study, we performed molecular dynamics simulations on the p53 CTD and five known regulatory binding partners. We identified distinct trends in fluctuation within and around the p53 CTD binding site on each partner demonstrating a behavior that facilitates association. Further, we present evidence that the size of the hydrophobic pocket in each p53 CTD binding site governs the secondary structure of the p53 CTD when in the bound state. This information will be useful for predicting new binding partners for the p53 CTD, identifying interacting regions within other known partners, and discovering inhibitors that provide additional points of control over p53 activity.

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“…The development of new techniques for in vivo microscopy has recently permitted the direct visualization of the motion of proteins inside the cell and the precise determination of their diffusion coefficients [10][11][12][13][14][15][16][17][18][19]. Nonetheless, the exact mechanism of non-specific DNA-protein interactions still remains unclear, because proteins come into a…”

Section: Introductionmentioning

confidence: 99%

Dynamical model of DNA-protein interaction: Effect of protein charge distribution and mechanical properties

Florescu

Joyeux

2009

The Journal of Chemical Physics

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:The mechanical model based on beads and springs, which we recently proposed to study non-specific DNA-protein interactions [J. Chem. Phys. 130, 015103 (2009)], was improved by describing proteins as sets of interconnected beads instead of single beads. In this paper, we first compare the results obtained with the updated model with those of the original one and then use it to investigate several aspects of the dynamics of DNA sampling, which could not be accounted for by the original model. These aspects include the effect on the speed of DNA sampling of the regularity and/or randomness of the protein charge distribution, the charge and location of the search site, and the shape and deformability of the protein. We also discuss the efficiency of facilitated diffusion, that is, the extent to which the combination of 1D sliding along the DNA and 3D diffusion in the cell can lead to faster sampling than pure 3D diffusion of the protein. (#)

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Tumor Suppressor p53 Slides on DNA with Low Friction and High Stability

Cited by 162 publications

References 17 publications

Stochastic models of intracellular transport

Stochastic models of intracellular transport

Modeling the Relationship between the p53 C-Terminal Domain and Its Binding Partners Using Molecular Dynamics

Dynamical model of DNA-protein interaction: Effect of protein charge distribution and mechanical properties

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