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Department of Physics and Astronomy

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Title: Department of Physics and Astronomy


1
Santa Fe 2005
Modeling Single Gene Transcription Biological
Control without Maxwell Demons?
Andrei Ruckenstein
  • Department of Physics and Astronomy
  • BioMaPS Institute for Quantitative Biology
  • Rutgers University, Piscataway NJ 07030

2
Santa Fe 2005
CENTRAL DOGMA
DNA
?
Transcription
RNA
?
Protein
3
Transcription
Santa Fe 2005
  • Multi-subunit molecular motor
  • Highly conserved across species

Darst et al. Science 289 (2000)
Kornberg et al. Science 292 (2001)
  • Promoter binding
  • Transcription initiation
  • Transcription elongation
  • Transcription termination

4
Model of the elongation complex
12-14 bp bubble
8-9 bp hybrid
3-end
exit channel
secondary channel
active center
RNA transcript
5-end of RNA
5
NTP incorporation
NTP (NMP)n (NMP)n1 PPi
i1
6
NTP (NMP)n (NMP)n1 PPi
i
i1
7
NTP (NMP)n (NMP)n1 PPi
i1
8
NTP (NMP)n (NMP)n1 PPi
i1
0
(UNTRANSLOCATED RNA1)
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Proofreading editing
18
Proofreading editing
(REVERSE TRANSLOCATED-EDITED)
19
Proofreading
20
Outline
  • Pausing sequences where RNAP stalls reversibly
    or irreversibly
  • Termination with or without memory?
  • New model of translocation two (or more)
    coupled ratchets

21
H. Matsuzaki, J. Mol. Biol. (1994), 235, 1173-1192
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24
TEC Stability Profile
  • Average over bubble configurations
  • Pauses local minima of G
  • Minima are backtracked from position 0!!

(Result of weak DNA-RNA hybrid)
25
Arrest!
26
  • Effect of multiple bubbles

Shifts the HIV-LTR pause transcript length 62
to the correct backtracked position ( -1)
  • Effect of RNA folding

It can eliminate pauses
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Pause criterium
  • Identify pauses within pause clusters
  • Define pause cutoff
  • Do not backtrack past equilibrium RNA folds!

Kinetic barriers from co-transcriptional RNA
folding! (D. Luse 1996) Guess separation of
scales between translocation (?sec) RNA unfolding
(msec)

(10 bp GC hairpin unbinding time scale of 100
years! (Herschlag, 1995))
31
  • Results
  • In 100 experimental pauses
  • lt15 false negatives
  • ?35 false positives
  • Random assignment of pauses on
  • the corresponding templates
  • ?70 false negatives and
  • ?68 false positives
  • Best results for multiple bubble
  • with folding

32
Intrinsic Termination
Yarnell Roberts Science 1999 Santangelo
Roberts Mol Cell 2004
126 Terminators 96 predictive rate
33
Another mechanism of transcription termination
Gusarov Nudler (1999)
Phage l tR2 terminator
34
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35
Santa Fe 2005
Gil Bar-Nahum,Vitaly Epshtein, Ruslan Rafiko,
Evgeny Nudler (NYU), and Arkady Mustaev (Public
Health, NJ)
Two mutant E-coli RNAPs
slow
fast
  • Slower elongation
  • Faster elongation
  • Higher fidelity
  • Lower fidelity
  • More responsive to signals
  • Less responsive
  • Preferentially back-tracked
  • More forward-tracked
  • Less bent than wt
  • F-bridge preferentially bent
  • Incorrect NTPs enhance back translocation w.r.t.
    the
  • case of both correct and no NTP
    Proofreading

36
The Double Ratchet Model
37
Santa Fe 2005
Fidelity
F
Misincorporation rate
F
WT
S
38
Santa Fe 2005
Higher Fidelity ?
F
Misincorporation rate
  • Arbitrary fidelity can be achieved in thermal
    equilibrium!!
  • Editing requires energy!

39
Santa Fe 2005
Time dependence of NTP incorporation
Allosteric binding site for a second NTP in the
active center
Erie et al. Cell 106. 242 (2001)
40
incorporation
30 mM
1mM
100 mM
5m?
500 mM
15m?
41
  • Fit to 22 sets of curves
  • with five parameters

42
Santa Fe 2005
  • The model explains all available biochemical and
    kinetic
  • data on elongation by E-coli RNAP (Nahum et
    al., Cell 120,
  • 183-193, January 28 2005)
  • The interplay between internal and
    translocational ratchets
  • play a role in the fidelity, proofreading and
    processivity of
  • transcription already before the chemical
    incorporation step!
  • No energy required for increasing fidelity
    (multiple check point
  • idea) but energy is needed for editing
  • Challange
  • Model selection optimize fit, minimize
    complexity of the model,
  • and minimize variation with additional data
  • (maximize predictibility).

43
Santa Fe 2005
MY GRAND CHALLENGES
  • CO-TRANSCRIPTIONAL RNA FOLDING
  • STRUCTURAL INF/MODELING OF TERMINATION
  • PREDICTIONS MODEL SELCTION

44
Santa Fe 2005
IT IS SOMETIMES EASIER TO MAKE A THEORY OF
EVERYTHING THAN A THEORY OF SOMETHING! Aharon
Katchalsky
45
BC 2005
Acknowledgments
Gil Bar-Nahum Vitaly Epshtein Ruslan
Rafikov Evgeny Nudler
(NYU School of Medicine)
(BioMaPS Insitute/Rutgers)
Daibhid OMaoileidigh Vasisht Tadigotla Richard
Ebright Konstantin Severinov Peter von Hippel
(University of Oregon)
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