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Modeling promoter search by E.coli RNA polymerase : One-dimensional diffusion in a sequence-dependent energy landscape Journal of Theoretical Biology 2009.

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Presentation on theme: "Modeling promoter search by E.coli RNA polymerase : One-dimensional diffusion in a sequence-dependent energy landscape Journal of Theoretical Biology 2009."— Presentation transcript:

1 Modeling promoter search by E.coli RNA polymerase : One-dimensional diffusion in a sequence-dependent energy landscape Journal of Theoretical Biology 2009

2 Outline  Introduction  Previous Work  Preliminary  Materials and Method  Experiment Results  Discussion

3 Introduction  Gene Transcription: DNA → RNA

4 Introduction  Stages of transcription: 5’, 3’ RNA polymerase binding release mRNA

5 Outline  Introduction  Previous Work  Preliminary  Materials and Method  Experiment Results  Discussion

6 Previous Work  1970:  association rate much higher than the rate achievable by 3D diffusion.  1981:  theory for 3D diffusion bind to DNA and then 1D diffusion on DNA(1D random walk)  1989:  Two mode(search/recognition) of RNAP binding to DNA

7 Previous Work  1999:  RNAP searches its target site (the promoter) by randomly binding the DNA and subsequently sliding along (1D Brownian motion )DNA  2005:  same probability(forward/backward) for 1D random walk is inefficient process  But, a detailed understanding of one- dimensional diffusion and the possible sliding length could not yet be obtained.

8 Previous Work  Two kinds of sliding:  1D Brownian motion random walk  sequence-dependent random walk Three Commonly discussed microscopic pathways for transferring a protein from one site to another. sliding hopping Intersegment transfer

9 Outline  Introduction  Previous Work  Preliminary  Materials and Method  Experiment Results  Discussion

10 Preliminary  assumption :  the sliding process has a sequence-dependent component  it does not perform a random walk with equal probabilities of stepping forward or backward  the sliding is influenced by the binding energy at each position

11 Preliminary  transition rates : β=(k B T) -1 v affective attempt frequency k B the Boltzmann constant T the ambient temperature in Kelvin

12 Preliminary  Binding energy E(i p ) between the sigma factor and a promoter p at position i p :

13 Preliminary

14

15 Outline  Introduction  Previous Work  Preliminary  Materials and Method  Experiment Results  Discussion

16 Materials and Method  Weight Matrix W  the contribution of the 12 nucleotides in the promoter regions to the binding energy

17 Materials and Method  Weight Matrix W Color scheme: Black=A, dark gray=C, light gray=G, white=T.

18 Materials and Method  The binding energy E(i) at position i of an analyzed DNA sequence is obtained by minimizing the energy score calculated according to Eq. high negative overall energies should indicate candidate target sites

19 Outline  Introduction  Previous Work  Preliminary  Materials and Method  Experiment Results  Discussion

20 Experiment Results  Data:  σ 70 and 651 promoters from RegulonDB  Average energy landscape E(i)

21 Experiment Results  Speed of sliding : (τ i denotes the time the protein spends bound to site i )  Region 1: |g|=0 → r i = 2v.  Region 2: |g|>0 → r i < 2v.  Region 3: |g|>0 → r i < 2v.  Region 4: |g|=0 → r i = 2v.

22 Experiment Results  Direction of sliding : (p i means forward ; q i means backward) 

23 Experiment Results  Efficiency of promoter search :  Mean first-passage time(MFPT)  The mean number of steps the protein will make to slide from site i = 0 to site i = L (α i =q i /p i )  Assuming α 0 = α 1 = ….α k ( the approximation is a polynomial of order L depending on α )

24 Experiment Results  Efficiency of promoter search :  L=50 (α i =q i /p i ) 

25 Experiment Results  Efficiency of promoter search :  L=-50 (α i =q i /p i ) 

26 Experiment Results

27 Outline  Introduction  Previous Work  Preliminary  Materials and Method  Experiment Results  Discussion

28 Discussion  RNAP can move either direction  L=50(5’→ 3’),in Region 2:  MFPT decrease =more efficient  L=-50(3’→ 5’),in Region 3:  MFPT decrease =more efficient

29 Discussion  The decrease of binding energies the RNAP faces when approaching the promoters strongly influences the efficiency of promoter search.

30 Discussion  Summary  the movement of the RNAP along the DNA slows down when approaching the promoter regions.  sequence-dependent interaction between σ and promoter surrounding directs the RNAP towards the promoter.  mean first-passage time decreases when approaching the promoter regions (promoter search becomes more efficient in this region)

31 Discussion  surrounding of the promoters contains important information to guide the RNAP and its sigma subunit.  increasing the probability of transcription initiation by :  slowing down the sliding process  controlling the direction  more efficiency of the movement.

32 Problem  Why like poisson? Do other species have the same distribution?  Do all promoters have familiar energy landscape surrounding of the promoters?


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