A simple method for incorporating sequence information into directed evolution experiments
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Transcript of A simple method for incorporating sequence information into directed evolution experiments
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A simple method for incorporating sequence information into directed evolution experiments
Kyle L. Jensen*, Hal Alper*,Curt Fischer, Gregory Stephanopoulos
Department of Chemical EngineeringMassachusetts Institute of Technology
sequence phenotype
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When screening throughput is limit, linking sequence to phenotype can help direct
downstream searches
Screen based(selectable trait)
Assay based(no selectable trait)
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Here, a PLtet promoter was mutated to create a
library of promoter variants
Alper H., C. Fischer, E. Nevoigt, and G. Stephanopoulos, 2005. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. U S A 102:12678-83.
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69 promoter variants were created using error prone PCR
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The 69 promoter variants spanned an 800-fold range of activity
- How different are the underlying, mutagenized sequences?
- What, on a sequence level, causes the variation?
800 fold range
Log relative fluorescence
Muta
nt
num
ber
Top 50%
Bottom 50%
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Each of the 69 mutants had a unique sequence and incorporated multiple transition SNPs
mutations
promoter region
Log relative fluorescence
Muta
nt
num
ber
Position [nt]
Muta
nt n
um
ber
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The effects of individual mutations were “masked” by the presence of other mutations
Just because a mutation occurs more frequently in one class, is it correlated?
Is the ratio of top/bottom important?
What is the statistical significance of a mutation that is distributed between the two classes?
Some mutations
have obvious effects
...most do not
Position [nt]M
uta
nt n
um
ber
Cla
ss distrib
utio
n
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Each individual position can be evaluated using a simple binomial distribution
Same as: what's the probability of getting heads 14
of 20 coin tosses?
P-value: 14 or more heads out of 20
Assuming the positions are independent
Position [nt]
Cla
ss distrib
utio
n
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Similar analysis over the promoter region revealed 7 positions significantly correlated with activity
Cla
ss distrib
utio
n
Position [nt]
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Position [nt]M
uta
nt n
um
ber
Cla
ss distrib
utio
nLog relative fluorescence
Muta
nt
num
ber
Position [nt]
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A similar analysis can be applied to an arbitrary number of mutants and phenotypic classes
1
2
M
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mutants
M phenotypes
Mutants with mutations as “position 35”
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or
1
2
3
4
5
6
Y
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The generalized probability of the phenotype distribution can be used to find
mutation-phenotype correlations
Probability of a particular vector color distribution
Significance of a correlation between mutations at “position 35” and the green phenotypic class
Prior probability ofgreen phenotype
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In our case, we tested 8 locations, spanning a range of functions & confidences
Cla
ss distrib
utio
n
Position [nt]
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7/8 of the single position mutants were in agreement with the predicted function
Site Predictedactivity
P-value Observ-ations
Confidence RelativeFluorescnece
Log RelativeFluorescence
Agreement?
-8 Low <0.0001 22 High 0.036 -3.32 Yes
-10 Low 0.1094 6 Med 0.011 -4.52 Yes
-14 High 0.0625 4 High 1.428 0.35 Yes
-21 High 0.0625 4 High 1.585 0.46 Yes
-28 Low 0.3770 10 Low 0.756 -2.58 Yes
-82 No effect 0.5000 2 Low 0.926 -0.08 Yes
-96 No effect 0.5000 5 Med 0.046 -3.08 No
-123 Low 0.1938 12 Med 0.087 -2.45 Yes
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Rationally designed promoters with combinations of mutations showed predicted activity but also
signs of site interaction
Sites Predictedactivity
Log RelativeFluorescence
Agreement?
-14, -21 High 0.65 Yes-14, -82 High -0.04 No-21, -82 High 0.36 Yes-96, -123 Low -1.43 Yes
-82,-14,-21 High -1.97 No-8,-10,-28 Low -4.03 Yes
*
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In summary, this simple method, based on multinomial statistics, can be used to link sequence variations
to particular phenotypes