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![Page 1: 1. Design Automation for DNA Self-Assembled Nanostructures Constantin Pistol, Alvin R. Lebeck, Christopher Dwyer Duke University Design Automation Conference.](https://reader035.fdocuments.net/reader035/viewer/2022062320/56649d805503460f94a650dc/html5/thumbnails/1.jpg)
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Design Automation for DNA Self-Assembled Nanostructures
Constantin Pistol, Alvin R. Lebeck, Christopher Dwyer
Duke University
Design Automation Conference - July 27th, 2006
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DNA Nano-Assembly Today
100 nm
How to drive this forward ?
Sung Ha Park, Constantin Pistol, Sang Jung Ahn, John H. Reif, Alvin R. Lebeck, Chris Dwyer, Thomas H. LaBean - "Finite-size, Fully-Addressable DNA Tile LatticesForme
by Hierarchical Assembly Procedures", Angewandte Chemie No.5/2006 Y. He, Y. Tian, Y. Chen, Z. Deng, A. E. Ribbe and C. Mao, Sequence Symmetry as a Tool for Designing DNA Nanostructures, Angewandte Chemie International Edition, 44 (2005), pp. 6694-6696.
J. Sharma, R. Chhabra, Y. Liu, Y. Ke and H. Yan, DNA-Templated Self-Assembly of Two-Dimensional and Periodical Gold Nanoparticle Arrays, Angewandte Chemie International Edition, 45 (2006), pp. 730-735.
Constantin Pistol, Alvin R. Lebeck, Dan Sorin, Chris Dwyer - “Nanoscale Device Integration on DNA Self-Asembled Nanostructures”, Duke University, 2006
M. Mertig, W. Pompe: Biomimetic fabrication of DNA-based metallic nanowires and networks, in: Nanobiotechnology - Concepts, Applications and Perspectives, Ed. by C.M. Niemeyer, C.A. Mirkin, WILEY-VCH Verlag GmbH & Co. KgaA, Weinheim, 2004, p.256-277
P. W. K. Rothemund, Folding DNA to create nanoscale shapes and patterns, Nature, 440 (2006), pp. 297-302.
Nanowire Transistors, Gate Electrodes, and Their Directed Self-Assembly K. Skinner, R. L. Carroll, S. Washburn, and C. Dwyer. The 72nd Southeastern Section of the American Physical Society (SESAPS), November 2005
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Goal
• Automate the design of DNA-based self-assembled structures
- Find optimized sequences for given target
structures
- Define metrics for quantitative design comparison
- Apply self-assembly specific design rules
- Expose useful trade-offs to designers
INPUT Structure Motif Seq. space
CONSTRAINTS Set optimization Design rules Trade-offs
OUTPUT Seq. map files manufacture ready
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Outline
1. DNA Basics
2. DNA Self-Assembly and DNA Motifs
3. Metrics and Design Rules
4. Implementation
5. Evaluation
6. Conclusion
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DNA Basics
• A DNA strand is:
- A linear array of bases (A, T, G, and C)
- Directional (one end is distinct from the other)
- In nature, the source of genetic information
• DNA will form a double helix:
- When the bases on each strand (aligned head-to-
toe) are complementary: A with T, and G with C
- But only under “natural” environmental conditions
such as (low) temperatures (sequence dependent) and in
an ionic solution.
A
G
C
T
T
A
G
A
T
C
A
T
C
G
A
A
T
C
T
A
G
T
How does it form?
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DNA Basics
• DNA hybridization is the process that forms the double helix
• Random diffusion: power of self-assembly
• Sequence and temperature controls the hybridization event
• Reverse process called melting
- Melting temperature (Tm) is sequence dependent
- G-C pairs ~2x as strong as A-T pairs
T
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DNA Basics
• A common form of the double helix has some well-known
geometric properties:
- 3.4 Å per base pitch along the helix
- One complete turn between every 10th and 11th base
• Flexibility: the bonds along the sugar-phosphodiester
backbone of each strand can rotate
- Single stranded DNA has ~5 - 10 nm persistence length
- Double stranded DNA has ~50nm persistence length
Persistence length is “straight length”
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Outline
1. DNA Basics
2. DNA Self-Assembly and DNA Motifs
3. Metrics and Design Rules
4. Implementation
5. Evaluation
6. Conclusion
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Self-Assembly
• Self-assembly is ubiquitous in nature
• Generally defined as spontaneously generated order
• Thermodynamics drive the self-assembly process
- We can guide the process by the choice of
materials and environmental conditions
A
B
TA·B
Advantages ?
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Nanoscale self-assembly advantages
• Size – feature sizes in the 1 - 20nm range
• Count – simple parallel assembly (1014 – 1016)
• Infrastructure – $460 billion chemical manufacturing industry
• New material properties – leverage quantum effects
• Potential for dense nano-scale active devices
Why use DNA ?
* 2002 US Census
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• DNA can provide substrate for fabricating nano-devices
Why DNA Based Self Assembly
• Nano-scale components placed / interconnected on substrate
- Crossed carbon nanotube FETs
- Ring-gated FETs
- Nanowires
- Quantum dots
- Precise binding rules
- Nanometer pitch
• Challenge: specify DNA sequences for
- Intended geometry
- Thermodynamic stability
How to address this challenge?
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Motifs and Hierarchical Assembly
• Complex designs built from small set of building blocks (motifs)
• Many possible motifs
- Junctions (to form triangles, corners, etc.)
- Sticky-ends: single strand of DNA protruding out of a helix
• Two motifs with complementary sticky-ends bind to form a composite motif
• Composite motifs can bind with other composite motifs to form larger composite motifs
Structure design = hierarchical structuring of motifs
T3.4 ÅT
Sticky ends = “smart” glue
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Cruciform Motif
• Motif* has 9 DNA single strands
- Arms end in 5bp sticky ends
• Not exactly flat*
- Some structural curvature
• Core strand can be functionalized
Shell 1
Shell 2Shell 3
Shell 4
Arm 1
Arm 2
Arm 3
Arm 4 Core
* H. Yan, S. H. Park, L. Feng, G. Finkelstein, J. H. Reif, and T. H. LaBean, "4x4 DNA Tile and Lattices: Characterization, Self-Assembly, and Metallization of a Novel DNA Nanostructure Motif," in Proceedings of the Ninth International Meeting on DNA Based Computers (DNA9), 2003.
- Molecular addressability
- Two sticky ends per arm
- Attach nanoscale components
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Target System – 4x4 Grid
• 16 active access points on flat 60nm x 60nm grid
• “Nano-board” scaffold – “pin” device elements to it
T16
T2
T1
• Hierarchical 2-level assembly
60nm
Sung Ha Park, Constantin Pistol, Sang Jung Ahn, John H. Reif, Alvin R. Lebeck, Chris Dwyer, Thomas H. LaBean - "Finite-size, Fully-Addressable DNA Tile Lattices Formed by Hierarchical Assembly Procedures", Angewandte Chemie No. 5/2006
How to design this system?Core and shells reused - fixed sequences
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Design Automation – 4x4 Grid
• Complex simultaneous interaction – 16 motifs with 48 arms
• Design targets:
Target 1 : design optimized 96 sticky end set
Target 2 : eliminate curvature (flat grid)
Target 3 : control & optimize self-assembly process
DNA Design Automation Software
T1 T2 T3
INPUT Structure Motif Seq. space
Constraints OUTPUT
Sequence map
How to evaluate these targets? What metrics?
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Outline
1. DNA Basics
2. DNA Self-Assembly and DNA Motifs
3. Metrics and Design Rules
4. Implementation
5. Evaluation
6. Conclusion
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Target 1: Sequence design
• Generate optimized DNA sequence set for a given target structure
• Two metrics
- SEM: average single-interaction energy measure (stability)
- TLM: average target-interaction likelihood measure (accuracy)
• Find sequences that
- Minimize strength of unintentional interactions
- Maximize strength of intentional interactions
• Designer can add tradeoff between accuracy and stability
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SEM and TLM Metrics
Specific Tm
No
n-S
pec
ific
Tm
•
1:1
diagonal
SEM = Spec[strand]
TLMSEM and TLM for single sequence point
Specific Tm : Melting temp. with the complement strand
Non-Specific Tm : Highest melting temp. with a non-complementary strand
* N. Le Novere, "MELTING, computing the melting temperature of nucleic acid duplex," Bioinformatics, vol. 17, pp. 1226-1227, 2001
Melting temp. of strand pairs calculated using modified version of MELTING4* software
NonSpec[strand]
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SEM and TLM Metrics – Stability and Accuracy
- Set E + C1 has high SEM and low TLM
G-C-CC-G-G
A-C-CT-G-G
A-T-AT-A-T
EXISTING (E) CANDIDATE 1 (C1) CANDIDATE 2 (C2)
- High stability (melting temperature) but low accuracy (more defects)
• Assume sequences E are present in system (sticky end)
• Need to add an additional sticky end
• Which is better: C1 or C2 ?
- Set E + C2 has lower SEM and high TLM
- Lower stability but higher accuracy (less defects)
G-C pair stronger than A-T
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Target 2: Flat Grid
- Corrugation design rule – alternate motif normals
Face UP
Face DOWN
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Target 3: Optimize Assembly
- Thermal ordering design rules - hierarchy of sub-products
1st
2nd
3rd
4th
Thermal groups
Temperature
High
Low
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Outline
1. DNA Basics
2. DNA Self-Assembly and DNA Motifs
3. Metrics and Design Rules
4. Implementation
5. Evaluation
6. Conclusion
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• Exhaustive thermodynamic search
Thermodynamic Optimization Software
• Optimize target design against TLM and SEM metrics given:
• Evaluate each sticky-end sequence
- Target topology
- Basic motif design
- Against all other candidate and motif sequences
- Map their mutual interaction
• High computational cost
- 4x4 grid: 6 CPU-years to design and verify
- Parallel implementation
- Creates detailed sequence interaction
map
Str
an
d A
Strand B
Maximum Tm(A,B)
Strand Function1 – 4 : Shell5 : Core6 – 9 : Arms
°C
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• Modified nearest-neighbor algorithm using MELTING4* tool
Thermodynamic Interaction
• The number of interactions to evaluate depends on:
- Length of sticky-ends
- Extent of fixed motif sequences
- Energy contribution of each base also depends on neighbors
- Handles internal and terminal mismatches
Sticky-end length Sticky-end Interactions
5bp > 500,000
10bp > 500,000,000,000
• Calculate melting temperature for each interacting pair – used for SEM, TLM metrics
* N. Le Novere, "MELTING, computing the melting temperature of nucleic acid duplex," Bioinformatics, vol. 17, pp. 1226-1227, 2001
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Design Automation Suite (DAS)
• Input: structure + motifs + sequence space
• Software runs thermodynamic analysis
- Generates metric-annotated DNA sequence sets
• Designer can customize set
- Default: optimize for maximal TLM
- SF (Stability Factor): trade TLM for SEM
• Design rules are applied on candidate set
- Corrugation and thermal ordering
• Output: sequence map files
- For order and manufacturing
Co
ns
traints
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Outline
1. DNA Basics
2. DNA Self-Assembly and DNA Motifs
3. Metrics and Design Rules
4. Implementation
5. Evaluation
6. Conclusion
• Target systems and methods• Theoretical metric-based evaluation• Experimental evaluation
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Evaluation: target systems
• Large design - 96 sticky-ends set
- 4x4 grid with 16 motifs
• Small design – 20 sticky-ends set
- AB Polymer* with 4 motifs
* H. Yan, S. H. Park, L. Feng, G. Finkelstein, J. H. Reif, and T. H. LaBean, "4x4 DNA Tile and Lattices: Characterization, Self-Assembly, and Metallization of a Novel DNA Nanostructure Motif," in Proceedings of the Ninth International Meeting on DNA Based Computers (DNA9), 2003.
A,B: Two sets of fixed sequences
What is the impact of fixed sequences ?
A-Core B-Core
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Impact of fixed sequences
- Accuracy in arm space with different fixed sequence sets
A-only, B-only:
- same target structure as AB Cores
- use a single core/shells set (fewer fixed sequences)
Accuracy of the 5 bp arm space
0
2
4
6
8
10
12
1 28 55 82 109 136 163 190 217 244 271 298 325 352 379 406 433 460 487 514 541 568 595
Arm Identifier
Ac
cu
rac
y (
TL
M)
AB Cores
A-only
B-only
No Core
More fixed sequences = fewer “good” arms to choose from
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• Random sequence sets
Evaluation: Sequence Design Methods
• DNA Design Automation Suite (DAS)
+ Thermodynamic analysis of all possible interactions
+ High flexibility, detailed sequence interaction map
- Computationally expensive
• Original 20-Arm design – based on existing sequence design tools*
- Text-distance primitive
- GC content as energy approximation
+ Fast but no result / local minimum for large seq. spaces
- No guarantees of optimality
*N. C. Seeman, "De Novo Design of Sequences for Nucleic Acid Structural Engineering," Biomolecular Structure & Dynamics, vol. 8, pp. 573-581, 1990.
+ Fast
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SEM and TLM (20 Arm)
0
2
4
6
8
10
12
14
16
18
20
Upper Bound DAS - B CoreSF=4
DAS - AB SplitSF=7
Original AB Random
SEM
TLM
Sequence design evaluation – 20-Arm
• 20-Arm target system: upper bounds and original design
DAS designs significantly improve both accuracy and stability
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Sequence design evaluation – 96-Arm
• 96-Arm target system: B-only, AB Core and Random
-20
-15
-10
-5
0
5
10
15
20
25
0 5 10 15 20 25
Specific Tm
No
n-S
pec
ific
Tm
RandomAB CoresB-Only
1:1 diagonal
B-only = highest accuracy (TLM)
SEM and TLM (96 Arm)
0.00
2.00
4.00
6.00
8.00
10.00
12.00
B-Only AB Cores Random
SEM
TLM
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Experimental results – 96-Arm target system
• Atomic Force Microscopy – 4x4 grids
60nm
• Molecular height map on flat plane
Sung Ha Park, Constantin Pistol, Sang Jung Ahn, John H. Reif, Alvin R. Lebeck, Chris Dwyer, Thomas H. LaBean - "Finite-size, Fully-Addressable DNA Tile Lattices Formed by Hierarchical Assembly Procedures", Angewandte Chemie No. 5/2006
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Grid – no detectable curvature
• AFM height section shows flat grid
• Corrugation design rules successful
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DNA scaffold – experimental validation
60nm
Manufacturing scale: ~1014 letters/mL!
Trivia: The collection of books and manuscripts in the Library of Congress contains ~1014 letters.
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Conclusion
• Design automation for DNA-based self-assembled structures
• Step forward towards nano-scale device engineering
• Input: structure, motifs, sequence space
• Constraints:
- Optimized sequences for given target structures
- Self-assembly specific design rules
- Design trade-offs and metrics for quantitative design
comparison
• Output: sequence map for manufacturing
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Thank you!
Design Automation Conference - July 27th, 2006
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0
2
4
6
8
10
12
14
16
0 0.5 1 2 3 4 5 6 7SF
TL
M
AB TLM
B_Only TLM
0
2
4
6
8
10
12
14
16
0 0.5 1 2 3 4 5 6 7SF
SE
M
AB SEM
B_Only SEM
Balanced designs
• Trade-off: accuracy for stability (TLM for SEM)
SF: Stability Factor – increases SEM, decreases TLM
SF Impact on SEM and TLM (20 Arms)
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SEM and TLM: Set Metrics
• Average of single sequence metrics
Tm[seqi,seqj] = melting temperature of seqi and seqj
comp[seqi] = sequence complement of seqi