Post on 25-Dec-2015
transcript
1
Programmable Self-Assembled DNA-Based Autonomous Molecular Devices
John ReifDuke University
DNA Nanostructure GroupJohn H Reif & Thomas H. LaBean
QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.Graduate Students: Harish Chandran and Nikhil Gopalkrishnan
Recent Graduated Phds: Urmi Majumder, Sudheer Sahu, & Peng Yin
2
Self-Assembly in NatureSpontaneous organization of components into stable superstructures due to local
interactions
3
Key to DNA Self-Assembly
T T G T T T A A C C T
A A C A A A T T G G A5’ 3’
5’3’
Hybridization
T T G T T T A A C C T
A A C A A A T T G G A5’ 3’
5’3’
4
Hybridization for superstructures
(Mao et al: Nature00)NYU&Duke Univ(Park, et al 05) Duke Univ
(Yan et al Nature03) Duke Univ(He et al 05)
2D Periodic Grid Lattices
3D Cube
(Chen and Seeman, 91)
(Rothemund et al 04)
1D Algorithmic Assembly
(Rothemund 06)Origami - 2D Addressable Lattices
cool
Base Pairing
cool
sticky end
QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.
(Yan et al: PNAS 03) Duke Univ
Barcode
Barcode patterning
2D Algorithmic Assembly2D Hierarchal Assembled Lattices(Park et al: Angewandte Chemie06) Duke Univ
(Lui et al PNAS 04) Duke(Yin et al Science 08)
Duke&Caltech
Tube Lattices
5
Compact, Complex, Robust, Flexible,
Scalable, Easily CharacterizedComputing Device
Activatable Tiles (Compact, Robust)
Error MinimizationRedundant Tile Design
Binary Counter(Compact, Robust)
Part I
Stochastic ModelYield & Convergence
Rates(Easily Characterized)
DNA WalkersWalking on 1D & 2D
Lattices(Programmable)
Double-decker tiles Tiling in 3D
(Scalable)
ApplicationsReaction CatalyzationDNAzyme DNADoctor
Isothermal DNA or RNA Detection
6
Double-decker tiles: Route to Assembly in 3D
• No tile rigid enough to create 3D periodic lattices
• Difficult to characterize
Challenges
Design a motif that can tile in 2D as well as 3D
Goal
Protein Crystallization: original goal of DNA nanotechnology
Molecular sieve, 3D computing, host guest molecules
Motivation
Double-decker tiles: Route to Assembly in 3D
Urmi Majumder, Abhijit Rangnekar, Thomas H.
LaBean and John H. Reifin preparation
7
Cross tiles: Grid Assembly in 2DCross Tile
Symmetric Tile
Figures adopted from He et al, 2005
Branched Junction
Corrugation creates enormous lattices
8
Double-decker tiles: Route to Assembly in 3D
4 identical arms
sticky ends
2 cross tiles held together by branched junctions
Branched Junction
9
Double-decker tiles: Route to Assembly in 3D
Corrugation cancels curvature of lattice=> creates enormous lattices
2D Corrugation
2D Pad Programming of Double-Decker Tiles
10
Double-decker tiles: Route to Assembly in 3D2D Lattices
Yeilds:Extremely
Large, Regular2D Grids
with Predominant Unidirectional
Banding10 um
2D ProgrammedDouble-Decker
Tiles
11
Double-decker tiles: Route to Assembly in 3D
3D Programming ofDouble-Decker
Tiles
3D Generalized Corrugation cancels curvature of lattice in all 3 dimensions !
12
Summary of Results
Double-decker tiles: Route to Assembly in 3DDNA Design of new motif (Double-decker tile)
Flexible sticky end programming
Sticky Ends can be programmed to form 2D lattices
Sticky Ends can be programmed to form 3D lattices
Agarose gel verification of tile formation
Programming of sticky ends for 2D Lattices with corrugation
AFM verification of formation of big, rigid lattices (10s of um)
Fluorescence verification of formation of enormous lattices (100s of um)
Analyze unidirectional banding in 2D lattices
Reprogramming of sticky ends for 2D lattices without corrugation
Fluorescence verification of formation of enormous lattices (100s of um)
Double-decker tiles: Route to Assembly in 3D
Urmi Majumder, Abhijit Rangnekar, Thomas H.
LaBean and John H. Reifin preparation
13
Compact, Complex, Robust, Flexible,
Scalable, Easily CharacterizedComputing Device
Activatable Tiles (Compact, Robust)
Error MinimizationRedundant Tile Design
Binary Counter(Compact, Robust)
Part II
Stochastic ModelYield & Convergence
Rates(Easily Characterized)
DNA WalkersWalking on 1D & 2D
Lattices(Programmable)
Double-decker tiles Tiling in 3D
(Scalable)
ApplicationsReaction CatalyzationDNAzyme DNA Doctor
Isothermal DNA or RNA Detection
14
Stochastic Analysis of Reversible Assembly
Every chemical reaction is reversible
Reversible Assembly close to reality
Information about time complexity, assembly yields
Motivation
Existing abstract model: irreversible and assumes error-free growth
Kinetic tiling assembly modeled errors for DNA
No framework for studying convergence rates
General model for reversible assembly in 2D: hard
whether infinite tiles form in the percolation problem not known in general case
Challenges GoalMake simplifying assumptions
Study existence of equilibrium in 2D and 3D assembly
Characterize equilibrium
Calculate time to equilibrium
Stochastic Assembly of Self-Assembly ProcessesUrmi Majumder, Sudheer Sahu and John
H. ReifComp. & Theo. Nano., 5,7 1289-1305, 2008
15
Tiling AssemblyN
W T E
SInput1
Inp
ut
2
Outp
ut
2
Output 1
Encode computation as tiles Temperature = 2
Tiling Assembly is Turing Universal
Assembly Rule: Glue type as well as glue strength have to match for assemblyA tile can attach to an assembly iff the combined strength of the “matchings glues” is greater than or equal to the temperature.
x
y
yx
Counter Encoding
⋀
⊕ yx
1 100
Computational Tilesstrength =1
seed y input x input
strength = 2
1
2
3
4
5
6
16
Tiling AssemblyKinetic Model for Errors
rf
rb,2
rf
rb,2rf
rb,1
1
2
7
8
9
Error due to pad mismatch!
17
Stochastic Analysis of Reversible AssemblySolve important subclass of 2D assemblies
Allow only monomer addition (No super-tile assemblies allowed)
Pre-assembled boundary
Same on/off rate for each binding or dissociation event for all tile types
Binding Rule: A tile can bind to a site where it has at least two neighbors
Dissociation Rule: A tile can only dissociate from a growth site where it has at most two neighbors
Binding or dissociation event on one pad of a tile is independent of what’s happening on the remaining three pads
Model Assumptions
18
Stochastic Analysis of Reversible Assembly
Equilibrium Characterization n x n completely addressable square
Let aij denote the fraction of a tile Tij when it is free at top /right
Assume σ = on probability and τ = off probabilityDropping subscripts, let a’ be the next time step value of a. Then
At steady stateOff event On event
Time Convergence:
Multiplicative (<1) decrease in
each time step
Δ(t), distance from equilibrium decays exponentially in t
19
Stochastic Analysis of Reversible AssemblySummary of Results
General characterization of equilibrium for 2D assembly
Yields & Polytime Convergence to Equilibrium
Completely addressable square in 2D and 3D
Periodic Assembles
Algorithmic Assemblies (Distribution of error at near-equilibrium)
Assemblies with Partial Mismatches
Correlation between Rapidly Mixing Markov Chains and Self-Assembly
Stochastic Assembly of Self-Assembly ProcessesUrmi Majumder, Sudheer Sahu and John H. Reif
Comp. & Theo. Nano., 5,7 1289-1305, 2008
20
Compact, Complex, Robust, Flexible,
Scalable, Easily CharacterizedComputing Device
Activatable Tiles (Compact, Robust)
Part III
Stochastic ModelYield & Convergence
Rates(Easily Characterized)
Error MinimizationRedundant Tile Design
Binary Counter(Compact, Robust)
DNA WalkersWalking on 1D & 2D
Lattices(Programmable)
Double-decker tiles Tiling in 3D
(Scalable)
ApplicationsReaction Catalyzation
Enzyme Free DNADoctorIsothermal DNA or RNA Detection
Error Resilience: Previous Approaches ➡ Optimizing physical conditions
‣ Decrease concentration and increase binding strength [Winfree 98]
‣ Shortcoming: Reduces speed
➡ Biochemistry Techniques‣ Strand invasion [Chen et al 04]
‣ Shortcoming: Increase in tile set size
➡ Coding Theory Methods‣ Proofreading Tiles, Snake Tiles, Zig-zag Tiles [Winfree et al,
2003, Chen et al 2004, Schulman et al 2005]
‣ Shortcoming: Increase in tile set size
‣ Compact Redundancy techniques [Reif et al 2004, Sahu et al 2006]
‣ Shortcoming: Ignores nucleation errors
Compact Error Correction of Computational Lattices (Reif, et al 2004)
Initial Computational Tiles:
Error Resilient DNA Tiles:
Self-Propagation of Error Detection
Makes Erroronious Assembly Unstable
24
Error Minimization in Tiling Assembly: in vitroMotivatio
nNatural DNA self-assembly has powerful physical mechanisms for error correction & repair
Artificial self-assembly needs similar mechanisms
Very difficult to build large structures w/o these capabilities
ChallengeMinimize errors
at the same scale as original assembly
w/o modifying tile structure
GoalControl Physical parameters to reduce errors
Annealing Temperature
Relative Stoichiometry of tiles
Perform self-assembly w/o a scaffold
Error Minimization through Optimization of Physical Parameters: Assembly of a Binary Counting Lattice using DNA Cross-Tiles, Thomas H. LaBean, Sung Ha Park,
Urmi Majumder, Masahito Yamamoto, and John H. Reif, in submission (2009).
25
Characteristics of the experiment
No nucleating structure used
Result comparable to previous demonstration of Binary Counter (Barish et al, 2005)
Error Minimization
Second step annealing temperature tuned based on melting data of tiles forming grids and ribbons
Relative stoichiometry of tiles tuned based on a fixed size binary counting pattern
Use of a pre-assembled nucleating structure
Minimize spontaneous nucleation
Information about which lattices to analyze under AFM
Summary of Results
Error Minimization in Tiling Assembly: in vitro
Error Minimization through Optimization of Physical Parameters: Assembly of a Binary Counting Lattice using DNA Cross-Tiles
Thomas H. LaBean, Sung Ha Park, Urmi Majumder, Masahito Yamamoto, and John H. Reif
Manuscript
26
Error Minimization in Tiling Assembly: in vitroTemperature Control
After: Counting!
Before:Single tile association
BC2 BC3
27
Error Minimization in Tiling Assembly: in vitroStoichiometry Control
Before
After: 70% reduction in Error
Tile # RatioBC1 20 10
BC2 40 20
BC3 22 11
BC4 18 9
28
Error Minimization in Tiling Assembly: in vitroUse of pre-assembled nucleating structure
Minimize spontaneous nucleation
Information about which lattices to analyze under AFM
29
Compact, Complex, Robust, Flexible,
Scalable, Easily CharacterizedComputing Device
Activatable Tiles (Compact, Robust)
Part IV
Stochastic ModelYield & Convergence
Rates(Easily Characterized)
Error MinimizationRedundant Tile Design
Binary Counter(Compact, Robust)
Double-decker tiles Tiling in 3D
(Scalable)
DNA WalkersWalking on 1D & 2D
Lattices(Programmable)
ApplicationsReaction CatalyzationDNAzyme DNADoctor
Isothermal DNA or RNA Detection
30
Error Minimization in Tiling Assembly: in silico Types of Error
Mismatch ErrorModel assumes directional growth (i/p to o/p)
Model assumes T=2 rule (at least two correct binding required)
Also known as error by insufficient attachment
Spontaneous Nucleation ErrorAssembly in absence of seed
Challenge Minimize errors
At the same scale as original assembly
Use already existing DNA nanostructures with minimal modifications
Handle all kinds of errors (related to the tile assembly model)
Goals
Enforce model assumptions at the same scale as original assembly
Activatable Tiles: Basic Idea
➡ Tiles are initially inactive ‣ o/p pads protected and not available
for hybridization➡ Tiles transition to active state and
o/p pads are exposed only when the correct neighbors bind to its input pads
32
Error Minimization in Tiling Assembly: in silico
Error by insufficient attachment (T=2)
Activatable Tiles: Working Principle
➡Tiles are initially inactive ‣o/p pads protected and not available for hybridization
➡Tiles transition to active state and o/p pads are exposed only when the correct neighbors bind to its input pads
33
Activatable Tile Correct Growth
Error Minimization in Tiling Assembly: in silicoOne correct i/p match induces
the other i/p deprotection
34
Activatable Tile prevents errors by
insufficient attachment
Second i/p is not deprotected
Error Minimization in Tiling Assembly: in silico
35
Error Minimization in Tiling Assembly: in silicoSmall probability of error from the tiles
that leave a growth site after being completely deprotected.
Input deprotection reversibleOutput deprotection irreversible
Source of Error
DNA Implementation
➡ Strand Displacement for Input Deprotection
➡ DNA polymerization for Output Deprotection
‣ Particularly Effective over long distances (e.g. tile cores)
37
Strand DisplacementDNA
Polymerization
Strand displacing DNA Polymerization
DNA Strand Displacement
Using Polymerase Phi 29 for Strand Displacement: - Replicative polymerase from bacteriophage Phi29 - Phi29 polymerase can travel at the rate of 2000 nucleotides per minute at room temperature
- This polymerase has exceptional strand displacement and processive synthesis properties
A Reaction PathwayStage 0
S2 S1A S1B H
S1A’ S1B’P’M
S2’5’
3’
3’5’
Tile Core
ETile 1(Protected)
5’3’
Tile 2(Unprotected)H’S1A’ S1B’
S3
Stage 2
Hybridization of sticky ends by displacement of the protection strand
S2 S1A S1B H
S1A’ S1B’P’
MS2’5’
3’
3’ Tile Core
E
3’
H’
S1A’ S1B’
S3
Stage 1
S2 S1A S1B
HS1A’ S1B’P’M
S2’5’
3’
3’ Tile Core
E
H’
S1A’
S1B’
3’
S3
Toehold hybridization
5’
Polymerase
Stage 5
S2 S1A S1B H
S1A’
S1B’
P’MS2’
5’3’
3’ Tile Core
E
5’3’
H’S1A’ S1B’ S3
Complete polymerization of the primer and dehybridization of protection strand from the output
sticky endS1AM’
Exposed output sticky end
5’
P5’
3’
S2 S1A S1B H
S1A’
S1B’
P’MS2’5’
3’
3’ Tile Core
E
5’3’
H’S1A’ S1B’ S3
Primer polymerization and gradual de-protection of output sticky end due to the stripping of the template strand
S1AM’
Stage 4
5’
3’
P
P
S2 S1A S1BH
S1A’ S1B’
P’MS2’5’
3’
3’ Tile Core
E
5’
3’H’S1A’ S1B’ S3
Primer binding to now available template (protection strand)Stage 3
5’
5’3’ PPrimer
42
Error Minimization in Tiling Assembly: in silico
GSOriginal∝ E2
GS: Growth SpeedE: Error Rate
GS2x2∝ E
GSActivatable GSOriginal
>e-∊Gse
EActivatable EOriginal
= e-ɣGse
0<ɣ<<∊<1
43
New kind of tile : Activatable tile
Tile set size same as before
Basic nanostructure: existing tile types
Errors handled
Minimizes error due to insufficient attachment (proof)
Minimizes nucleation error
Allows self-healing (proof)
Summary of Results
Error Minimization in Tiling Assembly: in silico
Protection / deprotetcion mechanism
through strand displacement + polymerization
DNA Design of 1D/2D activatable tile system
Applications beyond computing
Concentration System
Reaction CatalyzationActivatable Tiles for Compact, Robust Programmable Assembly and other Applications
Urmi Majumder, Thomas H. LaBean, and John H. Reif
DNA 13, LNCS 4848, 15-25, 2007
Summary➡ Activatable tiles reduce error in
assembly by virtue of physical design of the tiles (use of DNA strand displacement and DNA polymerization)
➡ Other Potential Applications:‣ A Chemical Concentration Probing System‣ Chemical Reaction Catalytic System
➡ Current Work: ‣ Test a 1D Deprotection System
➡ Open Question: ‣ Overlay Redundancy Technique+ Activatable
Tiles
45
Compact, Complex, Robust, Flexible,
Scalable, Easily CharacterizedComputing Device
Part VI
Stochastic ModelYield & Convergence
Rates(Easily Characterized)
Error MinimizationRedundant Tile Design
Binary Counter(Compact, Robust)
Activatable Tiles (Compact, Robust)
in silico
DNA WalkersWalking on 1D & 2D
Lattices(Programmable)
Double-decker tiles Tiling in 3D
(Scalable)
ApplicationsReaction CatalyzationDNAzyme DNADoctor
Isothermal DNA or RNA Detection
DNA Walker Devices: Formulation & First Designs [Reif, 2002]Designs for the first autonomous DNA nanomechanical devices that execute cycles of motion without external environmental changes. Walking DNA device Rolling DNA deviceUse ATP consumption Use hybridization energy
These DNA devices translate across a circular strand of ssDNA and rotate simultaneously. Generate random bidirectional movements that acquire after n steps an
expected translational deviation of O(n1/2).
Bidirectional Translational& Rotational Movement
dsDNAWalker:
ssDNARoad:
Walking DNADevice
Bidirectional RandomTranslational& RotationalMovement
ssDNARoller:ssDNA
Road:
Rolling DNADevice
First Autonomous DNA Walker 2004: Peng Yin, Hao Yan, Xiaoju G. Daniel, Andrew J. Turberfield, John H. Reif, A Unidirectional DNA Walker Moving Autonomously Along a Linear Track, Angewandte Chemie Volume 43, Number 37, Sept. 20, 2004, pp 4906-4911.
B C D A
Track
AnchorageA
Walker*
LigasePflM I
BstAP I
Restriction enzymes
• autonomous, • programmable, and further require • no protein enzymes. ________________________The basic principle involved is inspired by a simple but
ingenious molecular device due to Mao et al
– Mao used DNAzyme to traverse on a DNA nanostructure, but was not programmable (it did not executed computations).
Programmable Autonomous DNA Nanorobotic Devices Using DNAzymes John H. Reif and Sudheer Sahu
Our DNAzyme based designs1.DNAzyme calculator : a limited ability computational
device
2.DNAzyme FSA: a finite state automata device, that executes finite state transitions using DNAzymes• extensions to probabilistic automata and non-deterministic automata,
3.DNAzyme router: for programmable routing of nanostructures on a 2D DNA addressable lattice
4.DNAzyme porter: for loading and unloading of transported nano-particles
5.DNAzyme doctor : a medical-related application to provide transduction of nucleic acid expression. 1. can be programmed to respond to the under-expression or over-expression
of various strands of RNA, with a response by release of an RNA
2. operates without use of any protein enzymes.
54
A “Molecular-Racecar” on Circular Track Used Power of Strand-displacing Polymerase
Used Polymerase Phi 29 to push wheel W on circular track T
-Protector BQ prevents W from moving on its own-Powerful strand displacement capability of Phi 29 during polymerization dislodges BQ from track=> much faster & forceful movement than other DNA Walkers
Sudheer Sahu, Thom H. LaBean, John H. Reif, A DNA Nanotransport Device Powered by Polymerase Phi29 , Nanoletters, 2008
Polymerase Phi 29Replicative polymerase from bacteriophage Phi29 Phi29 polymerase can travel at the rate of 2000 nucleotides per minute at room temperatureThis polymerase has exceptional strand displacement and processive synthesis properties
-Experimental Demonstrations via FRET and Gel data
55
Compact, Complex, Robust, Flexible,
Scalable, Easily CharacterizedComputing Device
Summary
Stochastic ModelYield & Convergence
Rates(Easily Characterized)
Error MinimizationRedundant Tile Design
Binary Counter(Compact, Robust)
Activatable Tiles (Compact, Robust)
Double-decker tiles Tiling in 3D
(Scalable)
DNA WalkersWalking on 1D & 2D
Lattices(Programmable)
ApplicationsReaction CatalyzationDNAzyme DNADoctor
Isothermal DNA or RNA Detection
61
Application of DNA Nanotechnology: Isothermal DNA and RNA Detection
Isothermal detection protocols: exquisitely sensitive detection of specific DNA or RNA sequences. Report target DNA detection via nanoparticle colorimetric detection.Two Isothermal Detection Techniques Demonstrated: (1) Superlinear Hybridization Chain Reaction (HCR)- Based on linear hybridization chain reactionof (Dirks04).-triggered by target DNA or RNA- New DNA detection protocol using a superlinear hybridization cascade reaction - No use of Enzymes- Superlinear Detection Response- nanoparticle-based colorimetric readout.
(2) Cross-Catalytic Deoxyribozymogen Reaction (DRZ) - No use of Protein Enzymes- Based on cross-catalytic DNAzyme-based reaction of (Levy03)-triggered by target DNA or RNA- Exponential Detection Response- nanoparticle-based colorimetric readout.
Diagnostic Applications:Detectiion of Disease Sequences (DETECTIONChlamydia & HIV)
The modification of the HCR and DRZ methods to detect target sequences in Chlamydia trachomatis bacterial DNA & HIV viral RNA/DNA sequences.
61
62
Compact, Complex, Robust, Flexible,
Scalable, Easily CharacterizedComputing Device
Summary
Stochastic ModelYield & Convergence
Rates(Easily Characterized)
Error MinimizationRedundant Tile Design
Binary Counter(Compact, Robust)
Activatable Tiles (Compact, Robust)
ApplicationsReaction Catalyzation
Enzyme Free DNADoctorIsothermal DNA or RNA Detection
Double-decker tiles Tiling in 3D
(Scalable)
DNA WalkersWalking on 1D & 2D
Lattices(Programmable)