DNA Nanoassembly & Autonomous Nanodevices: Challenges, Research Progress, and Applications

1. DNA Nanoassembly & Autonomous Nanodevices: Challenges, Research Progress, and Applications • John Reif • Duke University DNA Nanostructure Group John H Reif & Thomas H. LaBean Graduate Students: Harish Chandran and Nikhil Gopalkrishnan Recent Graduated Phds: Urmi Majumder, Sudheer Sahu, & Peng Yin

2. DNA Structural Nanotechnology John Reif Duke University DNA Nanostructure Group John Reif & Thomas H. LaBean Graduate Students: Harish Chandran and Nikhil Gopalkrishnan Recent Graduated Phds: Urmi Majumder, Sudheer Sahu, & Peng Yin

3. Self-Assembly in Nature Spontaneous organization of components into stable superstructures due to local interactions

4. A A A A A A A A A A Key to DNA Self-Assembly Hybridization 3’ 5’ T T G T T T A A C C T C T T G G A 5’ 3’ 3’ 5’ T T G T T T A A C C T C T T G G A 5’ 3’

5. 2D Periodic Grid Lattices Tube Lattices (Lui et al PNAS 04) Duke (Yin et al Science 08) Duke&Caltech (Yan et al Nature03) Duke Univ (He et al 05) 1D Algorithmic Assembly Base Pairing (Rothemund 06) Origami - 2D Addressable Lattices cool cool (Mao et al: Nature00)NYU&Duke Univ (Park, et al 05) Duke Univ 3D Cube sticky end (Chen and Seeman, 91) (Rothemund et al 04) Hybridization for superstructures Barcode patterning (Yan et al: PNAS 03) Duke Univ Barcode 2D Hierarchal Assembled Lattices (Park et al: Angewandte Chemie06) Duke Univ 2D Algorithmic Assembly

6. Error Minimization Redundant Tile Design Binary Counter (Compact, Robust) Activatable Tiles (Compact, Robust) Stochastic Model Yield & Convergence Rates (Easily Characterized) Part I DNA Walkers Walking on 1D & 2D Lattices (Programmable) Applications Reaction Catalyzation DNAzyme DNADoctor Isothermal DNA or RNA Detection Compact, Complex, Robust, Flexible, Scalable, Easily Characterized Computing Device Double-decker tiles Tiling in 3D (Scalable)

7. Goal Motivation Challenges • Design a motif that can tile in 2D as well as 3D • Protein Crystallization: original goal of DNA nanotechnology • Molecular sieve, 3D computing, host guest molecules • No tile rigid enough to create 3D periodic lattices • Difficult to characterize Double-decker tiles: Route to Assembly in 3D Double-decker tiles: Route to Assembly in 3D Urmi Majumder, Abhijit Rangnekar, Thomas H. LaBean and John H. Reif in preparation

8. Branched Junction Figures adopted from He et al, 2005 Symmetric Tile Cross tiles: Grid Assembly in 2D Cross Tile Corrugation creates enormous lattices

9. 2 cross tiles held together by branched junctions Branched Junction Double-decker tiles: Route to Assembly in 3D sticky ends 4 identical arms

10. Double-decker tiles: Route to Assembly in 3D 2D Corrugation 2D Pad Programming of Double-Decker Tiles Corrugation cancels curvature of lattice => creates enormous lattices

11. Yeilds: Extremely Large, Regular 2D Grids with Predominant Unidirectional Banding Double-decker tiles: Route to Assembly in 3D 2D Lattices 2D Programmed Double-Decker Tiles 10 um

12. Double-decker tiles: Route to Assembly in 3D 3D Programming of Double-Decker Tiles 3D Generalized Corrugation cancels curvature of lattice in all 3 dimensions !

13. DNA 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 Summary of Results Double-decker tiles: Route to Assembly in 3D Urmi Majumder, Abhijit Rangnekar, Thomas H. LaBean and John H. Reif in preparation

14. Error Minimization Redundant Tile Design Binary Counter (Compact, Robust) Activatable Tiles (Compact, Robust) Stochastic Model Yield & Convergence Rates (Easily Characterized) Double-decker tiles Tiling in 3D (Scalable) Part II DNA Walkers Walking on 1D & 2D Lattices (Programmable) Applications Reaction Catalyzation DNAzyme DNA Doctor Isothermal DNA or RNA Detection Compact, Complex, Robust, Flexible, Scalable, Easily Characterized Computing Device

15. Every chemical reaction is reversible Reversible Assembly close to reality Information about time complexity, assembly yields Challenges Goal • Make simplifying assumptions • Study existence of equilibrium in 2D and 3D assembly • Characterize equilibrium • Calculate time to equilibrium • 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 Stochastic Analysis of Reversible Assembly Motivation Stochastic Assembly of Self-Assembly Processes Urmi Majumder, Sudheer Sahu and John H. Reif Comp. & Theo. Nano., 5,7 1289-1305, 2008

16. x input strength =1 6 Assembly Rule: Counter Encoding Computational Tiles y input N seed x Output 1 ⊕ y strength = 2 Glue type as well as glue strength have to match for assembly x 5 0 1 0 1 Input 2 T W E x Output 2 ⋀ y Input1 4 S y A tile can attach to an assembly iff the combined strength of the “matchings glues” is greater than or equal to the temperature. Tiling Assembly Encode computation as tiles Temperature = 2 3 2 1 Tiling Assembly is Turing Universal

17. rf 8 9 rf rb,2 7 rf rb,1 rb,2 2 Error due to pad mismatch! 1 Tiling AssemblyKinetic Model for Errors

18. Stochastic Analysis of Reversible Assembly Model Assumptions • Solve 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

19. Multiplicative (<1) decrease in each time step Time Convergence: Δ(t), distance from equilibriumdecays exponentially in t Stochastic Analysis of Reversible Assembly n x n completely addressable square Equilibrium Characterization Let aij denote the fraction of a tile Tij when it is free at top /right Assume σ = on probability and τ = off probability Dropping subscripts, let a’ be the next time step value of a. Then On event Off event At steady state

20. Stochastic Analysis of Reversible Assembly Summary 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 Processes Urmi Majumder, Sudheer Sahu and John H. Reif Comp. & Theo. Nano., 5,7 1289-1305, 2008

21. Error Minimization Redundant Tile Design Binary Counter (Compact, Robust) Activatable Tiles (Compact, Robust) Stochastic Model Yield & Convergence Rates (Easily Characterized) Double-decker tiles Tiling in 3D (Scalable) Part III DNA Walkers Walking on 1D & 2D Lattices (Programmable) Applications Reaction Catalyzation Enzyme Free DNADoctor Isothermal DNA or RNA Detection Compact, Complex, Robust, Flexible, Scalable, Easily Characterized Computing Device

22. Computational Errors (Winfree) Computationaltiles Seedtile Error! Frametiles

23. 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

24. 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

25. Challenge Goal • Control Physical parameters to reduce errors • Annealing Temperature • Relative Stoichiometry of tiles • Perform self-assembly w/o a scaffold • Minimize errors • at the same scale as original assembly • w/o modifying tile structure Error Minimization in Tiling Assembly: in vitro Motivation 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). • Natural 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

26. 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 Error Minimization in Tiling Assembly: in vitro Summary of Results 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

27. BC3 BC2 Before: Single tile association After: Counting! Error Minimization in Tiling Assembly: in vitro Temperature Control

28. Before After: 70% reduction in Error Error Minimization in Tiling Assembly: in vitro Stoichiometry Control

29. Use of pre-assembled nucleating structure Error Minimization in Tiling Assembly: in vitro • Minimize spontaneous nucleation • Information about which lattices to analyze under AFM

30. Double-decker tiles Tiling in 3D (Scalable) Part IV Error Minimization Redundant Tile Design Binary Counter (Compact, Robust) DNA Walkers Walking on 1D & 2D Lattices (Programmable) Applications Reaction Catalyzation DNAzyme DNADoctor Isothermal DNA or RNA Detection Compact, Complex, Robust, Flexible, Scalable, Easily Characterized Computing Device Stochastic Model Yield & Convergence Rates (Easily Characterized) Activatable Tiles (Compact, Robust)

31. Goals • Enforce model assumptions at the same scale as original assembly Error Minimization in Tiling Assembly: in silico Challenge Types of Error Mismatch Error Model 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 Error Assembly in absence of seed • 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)

32. 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

33. 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 Error Minimization in Tiling Assembly: in silico Error by insufficient attachment (T=2)

34. One correct i/p match induces the other i/p deprotection Error Minimization in Tiling Assembly: in silico Activatable Tile Correct Growth

35. Second i/p is not deprotected Error Minimization in Tiling Assembly: in silico Activatable Tile prevents errors by insufficient attachment

36. Source of Error Error Minimization in Tiling Assembly: in silico Input deprotection reversible Output deprotection irreversible Small probability of error from the tiles that leave a growth site after being completely deprotected.

37. DNA Implementation • Strand Displacement for Input Deprotection • DNA polymerization for Output Deprotection • Particularly Effective over long distances (e.g. tile cores)

38. DNA Polymerization Strand displacing DNA Polymerization DNA Strand Displacement 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

39. Activatable Tiles: Basic Idea in 1D

40. DNA Design of 1D Activatable Tile

41. Stage 1 Stage 0 Stage 2 3’ 3’ 3’ S1A’ 5’ Tile 1(Protected) S1A’ S1B’ E P’ S3 5’ 3’ E M P’ S1B’ M Tile 2(Unprotected) S1A’ S1B’ H’ S1A’ S1B’ S2’ E M P’ 5’ S1A’ S1B’ H’ S2’ 5’ S1A’ S3 S2’ S1B’ H 5’ S3 3’ 5’ S1A S1B H 3’ S2 Tile Core S1A S1B H 3’ S2 Tile Core 3’ S2 Tile Core S1A S1B H’ 3’ Hybridization of sticky ends by displacement of the protection strand Toehold hybridization A Reaction Pathway

42. Stage 4 Stage 3 Stage 5 Complete polymerization of the primer and dehybridization of protection strand from the output sticky end Primer Polymerase Primer binding to now available template (protection strand) Primer polymerization and gradual de-protection of output sticky end due to the stripping of the template strand P 5’ 3’ P S1A 3’ M’ P 5’ S1B’ E S1A’ S1B’ E S1A M’ P 3’ 3’ S1A’ P’ 5’ S1B’ E P’ 5’ M M 3’ S2’ S2’ H’ S3 S1A’ S1B’ H’ S3 S1A’ S1B’ 5’ 3’ S1A’ P’ M 5’ 3’ S2’ S3 H’ 5’ S1A’ S1B’ 3’ H 5’ H 5’ S1B S2 Tile Core S1A 3’ S2 Tile Core S1A 3’ S1B 5’ S1A S1B H 3’ S2 Tile Core 3’ Exposed output sticky end 5’

43. Error Minimization in Tiling Assembly: in silico GS: Growth Speed E: Error Rate GS2x2∝ E GSOriginal∝ E2 GSActivatable e-∊Gse > GSOriginal EActivatable e-ɣGse = EOriginal 0<ɣ<<∊<1

44. 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) Error Minimization in Tiling Assembly: in silico Summary of Results • Protection / deprotetcion mechanism • through strand displacement + polymerization • DNA Design of 1D/2D activatable tile system • Applications beyond computing • Concentration System • Reaction Catalyzation Activatable 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

45. 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

46. Double-decker tiles Tiling in 3D (Scalable) Part VI Error Minimization Redundant Tile Design Binary Counter (Compact, Robust) DNA Walkers Walking on 1D & 2D Lattices (Programmable) Applications Reaction Catalyzation DNAzyme DNADoctor Isothermal DNA or RNA Detection Compact, Complex, Robust, Flexible, Scalable, Easily Characterized Computing Device Stochastic Model Yield & Convergence Rates (Easily Characterized) Activatable Tiles (Compact, Robust) in silico

47. 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 device Use 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).

48. Walker Restriction enzymes Ligase * PflM I BstAP I 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. Anchorage A A B D C Track

49. 53 Autonomous Motion of the Walker

50. Programmable Autonomous DNA Nanorobotic Devices Using DNAzymes John H. Reif and Sudheer Sahu • 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).