RNA

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RNA NKS Erik A. Schultes Hedgehog
Research hedgehogresearch.info June 16, 2006
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Ribonucleic Acid Universal biopolymer
Linear, polarized
5
3
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Ribonucleic Acid Universal biopolymer
Linear, polarized 4 distinct nitrogenous
bases (nt) RNA can store genetic info (like
DNA)
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Cytosine
Uracil
Adenine
Guanine
3
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Ribonucleic Acid Universal biopolymer
Linear, polarized 4 distinct nitrogenous
bases RNA can store genetic info (like DNA)
Base-pairing rules A with U C with G
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Cytosine
Uracil
Adenine
Guanine
3
5
3
5
Guanine
Cytosine
Adenine
Uracil
Adenine
Uracil
Guanine
Cytosine
5
3
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Double-stranded RNA Helix
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Ribonucleic Acid Universal biopolymer
Linear, polarized 4 distinct nitrogenous
bases RNA can store genetic info (like DNA)
Base-pairing rules A with U C with G RNA
can act as an enzyme (like proteins)
5
Cytosine
Uracil
Adenine
Guanine
3
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Single-stranded RNA
Primary Structure
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Single-stranded RNA
Secondary Structure
Primary Structure
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Single-stranded RNA
Secondary Structure
Primary Structure
Tertiary Structure
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Single-stranded RNA
VS
Group I
Hammer head
HDV
Hairpin
Group II
RNAse P
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Single-stranded RNA
16S rRNA
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16S rRNA Universal Tree of Life
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A New Kind of Science by Stephen Wolfram
In our everyday experience with computers, the
programs that we encounter are normally set up
to perform very definite tasks.
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A New Kind of Science by Stephen Wolfram
In our everyday experience with computers, the
programs that we encounter are normally set up
to perform very definite tasks. Key idea What
happens if one instead just looks at simple
arbitrarily chosen programs, created without any
specific task in mind.
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A New Kind of Science by Stephen Wolfram
In our everyday experience with computers, the
programs that we encounter are normally set up
to perform very definite tasks. Key idea What
happens if one instead just looks at simple
arbitrarily chosen programs, created without any
specific task in mind. How do such programs
typically behave?
17
A New Kind of Science by Stephen Wolfram
In our everyday experience with computers, the
programs that we encounter are normally set up
to perform very definite tasks. Key idea What
happens if one instead just looks at simple
arbitrarily chosen programs, created without any
specific task in mind. How do such programs
typically behave?
computer (hardware) ? RNA molecule program
(software) ? nucleotide sequence
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A New Kind of Science by Stephen Wolfram
In our everyday experience with RNAs, the
sequences that we encounter are normally set up
to perform very definite tasks. Key idea What
happens if one instead just looks at simple
arbitrarily chosen sequences, created without
any specific task in mind. How do such
sequences typically behave?
/. computer (hardware) ? RNA molecule /.
program (software) ? nucleotide sequence
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RNA NKS
What do we mean by
Arbitrary sequence? RNA behavior?
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RNA NKS
What do we mean by
generated by random process
Arbitrary sequence? RNA behavior?
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RNA NKS
What do we mean by
generated by random process
Arbitrary sequence? RNA behavior?
folding dynamics
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RNA NKS
What do we mean by
generated by random process
Arbitrary sequence? RNA behavior?
folding dynamics
Rapidly converges on a unique, specific fold
Never converges on a unique, specific fold
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RNA NKS
What do we mean by
generated by random process
Arbitrary sequence? RNA behavior?
folding dynamics
Rapidly converges on a unique, specific fold
Never converges on a unique, specific fold
Ordered
Disordered
Poly(U)
Helix
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RNA NKS
What do we mean by
generated by random process
Arbitrary sequence? RNA behavior?
folding dynamics
Rapidly converges on a unique, specific fold
Complex
Never converges on a unique, specific fold
Evolved
Ordered
Disordered
Poly(U)
Helix
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RNA NKS
What do we mean by
generated by random process
Arbitrary sequence? RNA behavior?
folding dynamics
Classes I II
Class IV
Class III
Rapidly converges on a unique, specific fold
Complex
Evolved
Ordered
Helix
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RNA NKS
Analyzing RNA Behavior
1. Lead II chemical probing secondary
structure uniqueness of folding 2. Native gel
electrophoresis uniqueness of folding size of
fold 3. Analytical centrifugation size shape
of fold
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RNA NKS
Choosing Arbitrary Sequences
Control Sequences tRNAPHE (76nt) HDV
(85nt) Ligase (87nt) Reference Sequence Poly(U)

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RNA NKS
Choosing Arbitrary Sequences
Control Sequences tRNAPHE (76nt) HDV
(85nt) Ligase (87nt) Reference
Sequence Poly(U)85 Arbitrary Sequences 10
Permuted HDV (85nt) 10 Isoheteropolymer (85nt)
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Automated DNA Synthesis
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Structure Probing with Pb
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Structure Probing with Pb
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Structure Probing with Pb
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Structure Probing with Pb
OH ladder
Time
T1 ladder
HDV
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Structure Probing with Pb
OH ladder
Time
T1 ladder
HDV
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Arbitrary sequences acquire sequence-specific
folds
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Native Gel Electrophoresis
8 (291) 100mM THE, pH7.5 30mM KCl 0, 1, 10mM
MgCl2 3W, 2000Vhr XC 100mm T 23-24 ºC
-

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Arbitrary sequences acquire compact folds
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Sedimentation Velocity Experiments

Inferring molecular size and shape from the
concentration distribution of pure sample under
a centrifugal filed.
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Sedimentation Velocity Experiments
Reference
Sample
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Sedimentation Velocity Experiments
Meniscus
RNA
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Sedimentation Velocity Experiments
40,000 RPM 100,000 Xg
A260
r (cm)
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Sedimentation Velocity Experiments
S4.072 S D8.45 Ficks M25.2 kDa Rs26.2 Å
tRNA
A260
r (cm)
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Arbitrary sequences acquire compact folds
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The behavior of arbitrary RNA
Arbitrary sequences frequently have compact,
sequence specific folding - properties that have
always been assumed to be evolutionarily
derived.
So far 20 seq, 2 compositions (1/2 postdoc)
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The behavior of arbitrary RNA
Arbitrary sequences frequently have compact,
sequence specific folding - properties that have
always been assumed to be evolutionarily
derived.
So far 20 seq, 2 compositions (1/2
postdoc) Next step 35,000 seq, 1700
compositions (100M)
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The behavior of arbitrary RNA
Arbitrary sequences frequently have compact,
sequence specific folding - properties that have
always been assumed to be evolutionarily
derived.
Principle of Computational Equivalence weak RNA
PCE complex, biologically relevant folds are
abundant in seq space
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The behavior of arbitrary RNA
Arbitrary sequences frequently have compact,
sequence specific folding - properties that have
always been assumed to be evolutionarily
derived.
Principle of Computational Equivalence weak RNA
PCE complex, biologically relevant folds are
abundant in seq space strong RNA PCE specific
folds may be abundant in seq space
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Distribution of Folds in RNA Sequence Space
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Prototype Ribozymes
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Intersection Sequence
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Testing for Ligation Cleavage
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Connecting the Prototypes by Neutral Paths
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Connecting the Prototypes by Neutral Paths
Prototype Ligase
42 mutations
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Connecting the Prototypes by Neutral Paths
Prototype Ligase
42 mutations
44 mutations
Prototype HDV
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Intersection of Fitness Landscapes
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Conclusion

• RNA NN exist -
• seq space is highly redundant in folds

2. Different NN are proximal
Biological Implications
1. New folds from existing folds
2. RNAs with different sequences and folds
could still share ancestry
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NKS and Neutral Networks
1. Are INT sequences typical or rare?
2. Are sequences on NN typical or evolved?
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NKS and Neutral Networks
1. Are INT sequences typical or rare?
2. Are sequences on NN typical or evolved?
3. Does CA rule space have NN? 4. If so, are
there INT rules?
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Acknowledgments
David P. Bartel Whitehead Institute NSF/Alfred
P. Sloan Fellowship TMF/Charles A. King Trust
Fellowship
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