from asymmetric exclusion processes to protein synthesis
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From Asymmetric Exclusion Processes to Protein Synthesis. Beate Schmittmann Physics Department, Virginia Tech. with Jiajia Dong (Hamline U.) and Royce Zia (Virginia Tech), and many thanks to Leah Shaw (William & Mary). Workshop on - PowerPoint PPT PresentationTRANSCRIPT
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From Asymmetric Exclusion Processes to Protein Synthesis
Beate SchmittmannPhysics Department, Virginia Tech
Workshop on Nonequilibrium dynamics of
spatially extended interacting particle systems
January 11-13, 2010
Funded by the Division of Materials Research, NSF
with Jiajia Dong (Hamline U.) and Royce Zia (Virginia Tech),
and many thanks to Leah Shaw (William & Mary).
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Outline:
• Basic facts about protein synthesis
• A simple model: TASEP with locally varying rates– Currents and density profiles for one and two slow codons
– “point” particles– “extended” objects
– Real genes
• Conclusions and open questions
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Protein synthesis
Image courtesy of National Health Museum
Two steps:
• Transcription: DNA RNA
• Translation: RNA Protein
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Shine-Dalgarno, Kozak
A ribosome… • starts at one end (initiation)
• goes to the other, “knitting” the amino acid chain (elongation)
• releases aa-chain at the end and falls off mRNA (termination)
Before one falls off,another one starts!
initiation elongation termination
http://cellbio.utmb.edu/cellbio/rer4.jpg
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Knitting the aa into the polypeptide chain
Left: http://www.emc.maricopa.edu/faculty/farabee/BIOBK/BioBookglossE.htmlRight: cellbio.utmb.edu/cellbio/ribosome.htm; also Alberts et al, 1994
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Some interesting features:
• In E. coli, 61 codons code for 20 amino acids, mediated by 46 tRNAs
• tRNA concentrations can vary by orders of magnitude
• Translation rate believed to be determined by tRNA concentrations
“Fast” and “slow” codons
Synonymous codons code for same amino acid;Degeneracy ranges from 1 to 6
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Example: Leucine in E. Coli
0
10
20
30
Leu2 Leu2 Leu3 Leu1,3 Leu5 Leu4,5
CUU CUC CUA CUG UUA UUG
tRN
A c
ellu
lar
con
cen
trati
on
[u
M]
H. Dong, L. Nilsson, and C.G. Kurland, J. Mol. Biol. 1996
tRNA
codon
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Some interesting features:
• In E. coli, 61 codons code for 20 amino acids, mediated by 46 tRNAs
• tRNA concentrations can vary by orders of magnitude
• Translation rate believed to be determined by tRNA concentrations
• Codon bias: In highly expressed genes, “fast” codons appear more frequently than their “slower” synonymous counterparts
“Fast” and “slow” codons
Synonymous codons code for same amino acid;Degeneracy ranges from 1 to 6
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Towards a theoretical description:
• Translation is a one-dimensional, unidirectional process with excluded volume interactions
• Suggests modeling via a totally asymmetric exclusion process
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The model: TASEP of point particles• Open chain:
– sites are occupied or empty
– particles hop with rate 1 to empty nearest-neighbor sites on the right
– particles hop on (off) the chain with rate ()
– random sequential dynamics (easily simulated!)
Totally asymmetric simple exclusion process
… …
• Ring: much simplerThe proto model: F. Spitzer, Adv. Math. 5, 246 (1970)
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Why study TASEP ?
• Mathematicians: “Consider… this stochastic process”• Biologists:
simple minded model for protein synthesis• Physicists:
– Non-equilibrium statistical mechanics– Interacting systems with dynamics that violate
detailed balance, time reversal– Novel states and stationary distributions– Many other potential applications
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(T)ASEP: Far from equilibrium ! • Non-zero transport current – mass (energy, charge, …)
• Open boundaries
• Coupled to two reservoirs
• Simplest question: Properties of non-equilibrium steady state?
• Answer: Solve master equation!
… …
??)(),(lim *
CPtCPt
'
),()'(),'()'(),(C
t tCPCCWtCPCCWtCP
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TASEP of point particles:• P*(C) can be found exactly:
– density profiles, currents, dependence on system size
– non-trivial phase transitions!
… …
1/2 1
1
1/2High
Low
Max J
• Phase diagram:
MacDonald et al, 1968; Derrida et al, 1992, 1993; Schütz and Domany 1993; many others
High:
Low:
Max:
)1( J
)1( J
)(4/1 1 LOJ
Note on pbc
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Towards a theoretical description:
• Translation is a one-dimensional, unidirectional process with excluded volume interactions
• Suggests modeling via a totally asymmetric exclusion process
• Modifications:
– Translation rates are spatially non-uniform; start with one or two slow codons, then consider a whole gene
– Ribosomes are extended objects (cover about 10 – 12 codons); start with point- like objects, then consider different sizes
• Goal: Explore the effect of “bottle necks” (rates, location) and xxxribosome size
(L.B. Shaw et al, 2003, 2004)
(A.Kolomeisky, 1998; Chou & Lakatos, 2004)
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TASEP with bottle necks:• To model the effects of one or two slow codons:
– change hopping rates locally to q 1
– for simplicity, choose = = 1q q
x
… …11
y
• Measure current ( protein production rate) and density profile:
– as a function of x, y and q
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One slow site:• Without slow site: System is in max current phase:
• With slow site: Left/right segment in high/low density phase
N = 1000 q = 0.2; centered
Particles – holes :
…except for q 0.7
)(4/1 1 NOJ
Density profile:
0
0.2
0.4
0.6
0.8
1
0 500 1000
Simulations…
Edge effect!
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Edge effect:
0.4
0.6
0.8
0 50 100 150 200
x=1
x=32
x=64
x=100
0.244
0.246
0.248
0.25
0.252
0 200 400 600 800 1000
position of the blockage
%2
Mean-field theory:
Density profiles:
234.0)1/( 2 qqJ
Current:
A.Kolomeisky, 1998
Simulations…
N = 1000, q = 0.6
Maximized at q=0.49: 2.5%k=1: good results from FSMFT
site
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Two slow sites:
L = 1000; q1 = q2 = 0.2; separated by 500 sites
Particles – holes:
Typical density profiles:
0
0.2
0.4
0.6
0.8
1
0 200 400 600 800 1000
0.2
0.4
0.6
0.8
0 200 400 600 800 1000
q1 = q2 = 0.2 q1 = q2 = 0.6
Simulations…
… and extension of MFT
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Current is sensitive to separation:
0.22
0.23
0.24
0.25
0 100 200 300
separation
%5
Current vs separation:
q1 = q2 = 0.6
Current reduction vs q:
0.5
0.6
0.7
0.8
0.9
1
0 0.25 0.5 0.75 1
q
)(/)1( JJ
Significant effect!
Chou and Lakatos, 2004
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Note:
• Two slow sites with q1 q2 : Slowest site determines current
• Fast site(s) : Significant effects on profiles; none on currents
First set of conclusions:
• To maximize current, i.e., protein synthesis rate:
– Slow codons should be spaced as far apart as possible!
• Check effect of particle size!
Chou and Lakatos, PLR 2004;Dong, Schmittmann, Zia JSP 2007
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Effect of particle size, l
… …
• Entry:
– only if first l sites are free; then, whole particle enters with rate
• Hopping:
– left-most site is “reader”, determines local rate
• Exit:
– hops out gradually, “reader” leaves with rate β
Lakatos and Chou, JPA 36, 2027 (2003): Complete entry and incremental exit
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Phase diagram:
1
1
High
Low
Max J
• High:
• Low:
• Max:
)]1(1/[)1( J
)]1(1/[)1( J
2)1/(1 J
McDonald and Gibbs, 1969; Lakatos and Chou, 2003; Shaw et al., 2003
)1/(1
)1/(1
Results based on mean-field analysis or extremal principle; no longer exact but in
good agreement with simulations.
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One slow site:• Without slow site: System is in max current phase.
• With slow site: Left/right segment in high/low density phase
Coverage density profile
(all occupied sites)
Reader density profile
(only sites occupied by readers)
Simulations…
N = 1000, q = 0.2, x = 82
l = 01
l = 06
l = 12
Edge effect!
Long tails!
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Edge effect: Simulations…
Current reduction vs q: )(
)1()(1 centerJ
Jq
)(1 q
q
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Two slow sites:
Coverage density profile: Reader density profile:
Simulations…
N = 1000, q = 0.2
l = 01
l = 02
l = 06
l = 12
Shock still develops!
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Current is sensitive to separation:
Current reduction vs q: )(/)1()(2 JJq
Simulations…
)(2 q
q
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Second set of conclusions:
• The basic conclusion of the point particle study remains valid:
– Currents are maximized if slow codons are spaced as far apart as possible.
– Edge effect becomes more dramatic, as l increases
• Real genes?
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From TASEP to protein production:
Lattice
Site
Particle
Hopping rate γi
Current J
mRNA template
Codon
Ribosome
tRNA cellular concentration
Protein production rate
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A real gene: dnaA in E. coli• Protein required to initiate chromosome replication
• 467 codons, 138 (30%) are sub-optimal
Raw tRNA abundances:
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Optimize:
original (wild) optimal abysmal
J 0.011455 0.017514 0.007115
Δ J + 53 % 38 %
highest wild
wild lowest
~ 1.5 ~
(138 replacements) (225 replacements)
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Optimize:
original (wild) optimal abysmal
J 0.011455 0.017514 0.007115
Δ J + 53 % 38 %
2.8%2 slowest:
10 slowest: 17%
Clustering!
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Clustering is important:
• Introduce “coarse-grained” rate:
11
,
1
i
ik kiK
• K 1 is time needed to traverse l consecutive sites
Shaw, Zia, and Lee PRE 2003
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K12 measure:
original optimal abysmal
J 0.011455 0.017514 0.007115
Δ J + 53 % 38 %
Δmin { K12 } + 58 % 42 %
K12 min = 0.441
K12 min = 0.699
K12 min = 0.255
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Several sequences – same protein:
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Fully Optimized
Wild (“original”)Totally
Suppressed
700 other sequences
Simulated current JMC vs. K12 min
Best linear fitthrough OWS
Both fits provide tolerable and simple estimates for the J ’s
Best linear fitthrough OWS and the origin
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Similar results for 10 other genes in E.coli
Example of lacI : (with just 5 other randomly generated sequences)
Slopes are ~10% of each other.
J ~ const. K12 min
Simulated current JMC vs. K12 min
???DNA-binding transcriptional repressor
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Conclusions: • Protein production can be increased significantly by a few xxtargeted removals of bottlenecks and clustered bottlenecks.
• K measure provides simple estimate of changes in production rates
• Extensions: Initiation-rate limited mRNA; finite ribosome xxsupply; polycistronic mRNA; parallel translation of multiple xxmRNAs; and many other issues.
J.J. Dong, B. Schmittmann, and R.K.P. Zia, J. Stat. Phys. 128, 21 (2007); Phys. Rev. E 76, 051113 (2007);
J. Phys. A42, 015002 (2009) J.J. Dong, PhD thesis. Virginia Tech (May 2008)
• Experiments!