Programming Chemical Reaction Networks Using Intramolecular

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Programming Chemical Reaction Networks using Intramolecular Conformational Motions of DNA Wei Lai, Lei Ren, Qian Tang, Xiangmeng Qu, Jiang Li, Lihua Wang, Li Li, Chunhai Fan, and Hao Pei ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.8b02864 • Publication Date (Web): 15 Jun 2018 Downloaded from http://pubs.acs.org on June 21, 2018

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Programming Chemical Reaction Networks using Intramolecular Conformational Motions of DNA Wei Lai,1# Lei Ren,1# Qian Tang,1 Xiangmeng Qu,1 Jiang Li,2 Lihua Wang,2 Li Li,1 Chunhai Fan2 and Hao Pei1*

1

Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of

Chemistry and Molecular Engineering, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, P. R. China 2

Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation

Facility, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, P. R. China #

These authors contributed equally

*

Correspondence: [email protected] (H.P.)

ABSTRACT Programmable regulation of chemical reaction networks (CRNs) represents a major challenge toward the development of complex molecular devices performing sophisticated motions and functions. Nevertheless, regulation of artificial CRNs is generally energy- and time-intensive as compared to natural regulation. Inspired by allosteric regulation in biological CRNs, we herein develop an intramolecular conformational motion strategy (InCMS) for programmable regulation of DNA CRNs. We design a DNA switch as the regulatory element to program the distance between toehold and branch migration domain. The presence of multiple conformational transitions leads to wide-range kinetic regulation spanning over 4 orders of magnitude. Furthermore, the process of energy-cost-free strand exchange accompanied with conformational change discriminate single base mismatches. Our strategy thus provides a simple yet effective approach for dynamic programming of complex CRNs.

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KEYWORDS: DNA nanotechnology, biochemical circuit, kinetic regulation, strand-displacement reaction, chemical reaction networks Complex chemical reaction networks (CRNs) with regulated dynamics largely determine signal-response features of living cells when interacting with genes and proteins.1 Several basic motifs of biochemical circuit topology have been identified in cells,2 including switch,3 oscillation,4 pattern formation,5 and feed-forward/back control.6 However, unraveling the relationship between the topology and spatiotemporal behavior of biological circuits remains a great challenge due to limit of modularity of currently available chemical tools.7 DNA strand-displacement reaction (SDR) networks, as an effective programming language for the designed network behavior,8-12 provide a powerful engineering approach for manipulating temporal distribution of matters under non-equilibrium conditions,13,14 which have been actively exploited to develop smart nano-devices,15 gene regulation,16 image processing,17 and neuron-like computation.18 In a typical SDR process, an invading DNA strand displaces a target strand from a DNA duplex structure,19-21 which initiates from a short single-stranded “toehold” domain,22,23 and results in a strand displacement through a branch migration (BM).9,24,25 Interestingly, rate constants of such toehold-mediated SDR can span over six orders of magnitude by simply modulating the toehold binding strength,24,26 which is a highly important feature that allows the development of complex artificial biomolecular networks.27,28 Although many efforts have been devoted to engineer the kinetics of SDR by varying toehold length and sequence composition,29 responsive hidden toehold,30 dissociative/associative toehold activation,31 and remote toehold regulation,32 the exploitation of kinetic modulation to expand the toolbox of SDR techniques for building complex DNA network systems remains rare.33,34

Figure 1. Intramolecular conformational motion strategy (InCMS) proposed for kinetic modulation in strand displacement. A reconfigurable hairpin loop keeps toehold and BM domain in close proximity (S-ON), which allows the target strand to be displaced from the substrate by an input strand. In contrast, when the loop portion undergoes a spontaneous


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conformational change upon hybridization with a complementary sequence, its rigid, linear double helix structure forces toehold and BM domain apart, thus precluding the strand displacement (S-OFF). In contrast, a mutation on Act would prohibits the strand exchange and keeps S-OFF state.

Here, we report an intramolecular conformational motion strategy (InCMS) for kinetic modulation in strand displacement for dynamic programming of DNA reaction networks. The operating principle of this InCMS-based regulation of DNA CRNs is illustrated in Figure 1. It involves the introduction of a molecular switch23 as the regulatory element. The change in distance between toehold and BM domain can greatly influence the kinetics of SDR. We designed a reconfigurable hairpin loop34 to connect toehold and BM domain with two termini. The stem keeps these two domains in close proximity (conformationally active form, S-ON), leading to BM by accelerating internal diffusion rate as well as favoring thermodynamics. When the loop portion undergoes a spontaneous conformational change upon hybridization with a complementary sequence, its rigid, linear double helix structure forces toehold and BM domain apart (conformationally inactive form, S-OFF), thus precluding the strand displacement.

RESULTS AND DISCUSSION We first proposed a mechanistic kinetic model consisting of a sequential reaction path (Figure 2). Initially, the inactive substrate complex (S-OFF) transformed into active intermediate (S'-ON) by adding an activator strand (Act), which enables the rigid, double helix structure of regulatory portion change into flexible, single-stranded structure via toehold-mediated displacement. This step allows switching from S-OFF to S'-ON that can be approximated as a second-order reaction with a rate constant of kh. Then, the regulatory domain undergoes a spontaneous conformational change and forms the stem-loop structure (S-ON) with a rate constant of ks. Finally, the input strand (I-ON) docks to the substrate complexes through hybridization of its toehold domain, followed by an internal diffusion step that initiates the branch migration process by which the target strand (O) is released. As illustrated in detail in Figure S1, an internal diffusion step here is required to align the displacement domains, which is similar to the mechanism described for remote-toehold strand displacement by Andrew J. Turberfield.32 Despite the multiple reaction steps, the strand displacement reaction can be well modeled by a single reaction with an overall rate constant kH=kh·ks, where kH can be obtained by fitting the fluorescence traces (explained in detail in S1, Supporting Information). Previous reports have shown that the toehold docking and dissociation rates can be controlled through the concentrations of the reactants and toehold binding strength.32 We therefore first systematically studied the effect of the concentration of input strands and activator strands (Figure S2a-c) and chose a high concentration of input strands and 14-nt long toeholds to ensure a fast docking step and effectively irreversible forward reaction.32 Thus, we consider the conformational motion of hairpin structure as the rate-limiting step. Drastic


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adjustment of the strand displacement rate can therefore be made by changing the stem length: lengthening the stem length from 5 to 7 nt considerably speeds up the displacement over 315 times (from 3.8 × 103 M-1·s-1 to 1.2 × 106 M-1·s-1); further lengthening to 8 nt slows the displacement slightly, which is likely due to a longer internal diffusion distance (Figure 2b). On the other hand, when the system is initiated from the step3, we observed nearly the same reaction rate in systems with different stem lengths (Figure S2d), further proving that the conformational motion of hairpin structure plays an essential role in the strand displacement.

Figure 2．．Activation of SDR by InCMS. Mechanism proposed for conformational motions regulating DNA displacement. The dominant reaction path is sequential, including three steps: (1) activation of the substrate complex; (2) conformational switch of regulator domain into stem-loop structure; (3) toehold docking followed by branch migration and completion of strand displacement. The DNA SDR can be modeled by a single reaction S-OFF + I-ON + Act → O + W + H with an overall rate constant kH=kh·ks. Displacement is reported by fluorescence recovery from the output strand O. Fluorescence responses showing the fine-tuning of the strand displacement rate by adjusting the stem length from 5 to 8 nt. Initial concentrations: [I-ON] = 50 nM.

Next, the reversible conformational switching of regulatory element allows for dynamic SDR kinetic modulation.35,36 The substrate complexes can be dynamically switched between its conformationally active (S-ON) and inactive forms (S-OFF). The S-ON can be converted into S-OFF by adding a repressor strand (Rep) that is fully complementary to the regulatory domain (Figure 3a). The designed Rep strands carried toeholds can be removed from the regulators by adding complementary activator strands (Act), which allows for a reversible switch of S-OFF and S-ON. During this process, the consumption of Act and Rep therefore provides the energy required to achieve a reversible DNA reaction network. The displacement rate is dramatically increased when the regulatory domain formed the stem-loop structure by addition of Act strands, and the reaction can be effectively inhibited by addition of Rep strands. Figure 3b shows the displacement rates as modulated by addition of Act or Rep at various time points (shown by spikes). Given the excess concentrations of


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substrate complexes, we estimate the second-order rate constant in active states (S-ON) to be roughly 1.3 × 106 M-1·s-1, and that in inactive states to be lower than 1.5 × 102 M-1·s-1, indicating a much more time-consuming S-ON to S-OFF transition (Figure S3). The sharp change in rates upon the alternate addition of Act or Rep strands indicated that our proposed strategy allows reversible and real-time regulation of SDR kinetics (Figure 3).

Figure 3. a) Dynamic conformational switching for SDR kinetic modulation. b) Displacement rates as modulated by addition of Act or Rep at various time points. We next investigated the SDR kinetics on two different forms of substrate complexes (S-ON and S-OFF) by using a series of input strand with different spacer lengths. A fluorophore (F) and quencher (Q) pair tagged at the end of substrate complexes was used as the signal reporter (Figure 4). Under the conformationally inactive form (S-OFF, Figure 4b), the addition of the input strand does not result in any significant signal change, suggesting that no displacement occurs. Conversely, under the conformationally active form (S-ON, Figure 4a), the input strand can effectively trigger the strand displacement process, evidenced by the sharp fluorescence changes. Interestingly, the spacer domain length of the input strand has a notable impact on the kinetics of strand displacement in the S-ON form. Figure 4c displays the rate constants (kH) of the SDR determined from resulting fluorescence responses (Figure S4 and Figure S5) by assuming second-order kinetics.37 The rate constant varies over a factor of 10 by adjusting the spacer domain length from 0 to 20 nt. Specifically, the rate constant increases dramatically from 1.7 × 105 M-1·s-1 to 1.2 × 106 M-1·s-1 as the spacer domain is increased from 0 to 2 nt. Further increasing the spacer domain length from 2 to 20 nt was found to reduce the displacement rates by a factor of 10, to 1.1 × 105 M-1·s-1. Thus, a maximal rate constant (kH = 1.2 × 106 M-1·s-1), corresponding to an optimal switching efficiency (SE = kS-ON/kS-OFF = 8000), was obtained in the presence of input strand with a spacer domain length of 2 nt. Figure 4d displays equilibrium probability maps and secondary structure predications generated by NUPACK online. The overall free-energy change of reaction quantifies the thermodynamic driving force associated with displacement, which Gibbs free


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energy change (∆G) lies in the range of -54.18 ~ -57.64 kcal·mol-1. The minimum free energy (MFE) predictions suggest that a hairpin loop structure starts forming with the shortest diffusion distance of 2-nt spacer domain. It is evident from Figure 4d-3, the internal diffusion distance is the shortest when the spacer domain length is 2 nt, resulting in a linear structure with a minimal Gibbs free energy (-57.64 kcal·mol-1) upon toehold hybridization. Further lengthening the spacer domain length from 2 to 20 nt introduces a redundant internal diffusion step between toehold and BM domain, posing higher energy barrier that slows the reaction. The Gibbs free energy was found to increase along with the increase of spacer domain length, corresponding to a distinct decrease in reaction rate. On the other hand, for proximal input strand with no intervening spacer domain (0-nt), the strand displacement is also energetically less-favorable because of steric hindrance from hairpin loop.38 The simulated ∆G values are plotted in Figure 4e for comparison against the experimentally obtained kH values. Adjusting the predicted ∆G of all reactions by +4 kcal·mol-1 significantly improves the agreement between theory and experiments.26 The significant negative correlation observed between ∆G and rate constant (kH) against the length of the spacer domain further indicated that the spacer domain length is an important design consideration in strand-displacement operated DNA devices.

Figure 4. a) Scheme of SDR kinetics on substrate complexes with two different forms. b) A typical fluorescence signals generated by SDR under the conformationally active and inactive forms. c) Fine-tuning of the SDR rates by adjusting the length of the spacer domain of input strand (from 0 to 20 nt). Experimental results (red dots) correlate well with simulated values (blue histograms) from worm-like chain model (explained in detail in S2, Supporting Information).32 Error bars showing the standard deviation are included. d) Equilibrium probability maps and minimum free energy (MFE) structures of spacer domain predicted by NUPACK. e) Negative correlation between Gibbs free energy and rate constant kH against the length of the spacer domain. Initial concentrations: [Substrate] = 10 nM, [Input] = 50 nM. Input1 = 0 nt, Input2 = 1 nt, Input3 = 2 nt, Input4 = 3 nt, Input5 = 4 nt, Input6 = 5 nt, Input7 = 10 nt, Input8 = 15 nt, Input9 = 20 nt.


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Figure 5. a) SDR kinetic modulation by an inhibitor strand for the input strand. b) Kinetic profiles of SDR by addition of input strand in a flexible (I-ON, red curve) and a rigid (I-OFF, blue curve) conformation. c) Fine-tuning of the SDR rates by adjusting the structures of input strand and substrate complexes. Inset: fluorescence responses for I-ON/S-ON and I-OFF/S-ON upon the addition of Input3; I-ON/S-OFF and I-OFF/S-OFF upon the addition of Input15. Initial concentration: [Input] = 50 nM, [Substrate] = 10 nM. Input1 = 0 nt, Input2 = 1 nt, Input3 = 2 nt, Input4 = 3 nt, Input5 = 4 nt, Input6 = 5 nt, Input7 = 10 nt, Input8 = 15 nt, Input9 = 20 nt.

The flexibility of input strands also play an important role in InCMS regulating SDR kinetics (Figure 5a). The rate constant is the highest when the spacer domain of input strand is single-stranded and flexible (I-ON), which facilitates internal diffusion of the invading strand on substrate complexes (Figure 5b, red curve). Because the rigid double-stranded spacer domain increases the steric hindrance,39 an inhibitor strand (Inhibitor) that binds to the spacer domain of input strand (I-OFF) can render the strand displacement more difficult, which significantly depressed the SDR rate (Figure 5b, blue curve). Figure 5c shows four sets of SDR rates by regulating the conformational states of both input strands and substrate complexes, including I-ON/S-ON, I-ON/S-OFF, I-OFF/S-ON, and I-OFF/S-OFF. Each set was obtained with varying the spacer domain length. The rate constants varied over about three orders of magnitude, ranging from 1.2 × 106 M-1·s-1 (I-ON/S-ON, Input3 = 2 nt) to 260 M-1·s-1 (I-OFF/S-OFF, Input15 = 15 nt); whereas the inhibition constant, ki


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(referred to as the rate constants of I-OFF or S-OFF states, i.e., kI-ON/S-OFF, kI-OFF/S-ON, and kI-OFF/S-OFF) ranges from 260 to 1500 M-1·s-1 for different systems. The InCMS strategy also enables a continous fine tuning of the SDR kinetics. For instance, as shown in Figure 5c, the rate constant can be finely regulated from 1.1 × 105 M-1·s-1 (Input9 = 20 nt) to 1.2 × 106 M-1·s-1 (Input3 = 2 nt) in the case of I-ON/S-ON through varying the spacer length of input strand from 0 to 20 nt. Moreover, both I-ON/S-OFF and I-OFF/S-OFF yielded a highly constrained set of strand displacement reactions, because the strand displacement is effectively inhibited when the substrate is converted to the conformationally inactive form (S-OFF). As the strand displacement motion is exquisitely sensitive to ks and kh, our InCMS strategy can implement a discrimination of single nucleotide polymorphism (SNP) for molecular recognition. The SNP variants of Act strand carried toeholds hybridized less favourably to their Rep strand during the toehold exchange process40,41, thus greatly impeding the interconversion of S-OFF to S-ON. The real-time fluorescence signal displayed by the SNP variants increased much faster than that of Act strand with a perfect match (Figure 6). This difference in kinetics can be attributed to the bigger Gibbs free energy of reaction of 5.84 ~ 6.69 kcal•mol-1 (Figure S6). The discrimination factor Q42 was determined to be 74~89, which quantifies the single-base mutation specificity. These results showed that our InCMS strategy can enable near-optimal single-base discrimination with high specificity and sensitivity.

Figure 6. a) Discrimination of SNP by using the InCMS for molecular recognition. b, c) Fluorescence kinetics for three kinds of SNP variants.

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In summary, we have successfully developed an InCMS for dynamic regulation of strand displacement reactions in DNA device. This strategy provides a simple but effective approach for dynamic programming of high-level DNA reaction networks due to several inherent advantages. First, the single-layer structure of InCMS requires fewer molecular components, which ensures design simplification of complex DNA reaction systems. Second, the system kinetics can be finely modulated over a wide range of rate constants from 102 M-1·s-1 to 106 M-1·s-1 by simple control of the intramolecular conformational motion of substrate complexes as well as input strands. Third, the intramolecular switch efficiency and rates can be differentially regulated by changing the stem length and loop sequences at room temperature. Also, the InCMS allows reversible and real-time regulation of SDR kinetics with the capability of accurate predictions and explanations. Finally, our method with an inserted regulatory domain between the independent toehold and displacement domain32 offered a modular design for allosteric regulation of SDRs, which allows tuning of the reaction rate constant without changing other sequences. In addition, our method can interface with non-DNA chemical signals by adapting other molecular switches.20 Given these advantages, we expect the InCMS could contribute to the development of dynamic complex DNA reaction networks for DNA computers and robots.

METHODS DNA Sequences and Design. The DNA sequences presented here were designed to be minimally interacting (binding between unrelated domains) without significant secondary structures. And the predicted interaction of DNA strands were examined using NUPACK. The nucleotide sequences of each experiment are classified and provided in Table S1. The DNA strands were purchased from Sangon Biotechnology Co. Ltd. (Shanghai, China). The strands modified with 5’-FAM or 3’-DAB were purified via HPLC. Furthermore, Agilent Cary 60 was used to determine the concentration of DNA strands by measuring the absorbance at 260 nm. Buffer Conditions. DNA oligonucleotides were stored in TE buffer (10 mM Tris·HCl, pH balanced to 8.0, with 1 mM EDTA·Na2). 62.5 mM MgCl2 of TE buffer was added to the sample with a ratio of 1:4, achieving a final concentration of 12.5 mM of MgCl2 (of which 1mM is bound to EDTA). This buffer is henceforth known as “TE/Mg2+” buffer. All experiments were performed at 25 ± 0.5 °C, in which the temperature was controlled using an external temperature bath. Annealing. All annealing process were performed with a Longgene A300 fast thermalcycler. The samples (typically at a final duplex concentration of 5 µM) were heated to 95 °C for 5 min and then gradually cooled to room temperature at a constant rate over a period of 2 h. Spectrofluorimetry. Fluorescence measurements were performed with a Hitachi F-7000 fluorometre at 25 °C in 1.0 mL quartz cuvette. Fluorescence trace was recorded with excitation/emission wavelengths of 492 nm/518 nm, a silt width of 2.5 nm, PMT Voltage is 950 V. Before experiment, all cuvettes were washed with deionized water for 10 times, subsequently with 70% ethanol for 5 times, and then 5 times in deionized water. DNA strands were stored in TE buffer, typically 5 µM of


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concentration. The substrate were added to TE/Mg2+ buffer to achieve the final concentration of 10 nM with a total volume of 1.0 mL in the cuvette, while the input DNA strand was 50 nM. Solutions was thoroughly mixed by Eppendorf pipet. Adding reagents and mixing the solutions took about 30 s. Fluorescence normalization. Fluorescence intensity is normalized so that 1 normalized unit (n.u.) of the fluorescence corresponds to 10 nM unquenched fluorophore-labeled strand O.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.10121/acsnano.xxxxxxx. DNA sequences and modifications, mechanism proposed for the internal diffusion step, characterization of different input systems, standard Gibbs free energy for different hybridization combinations, worm-like chain model of the internal displacement rate, calculation of Gibbs free energy using NUPACK.

ACKNOWLEDGEMENTS This work was supported by the National Science Foundation of China (grant numbers 21722502, 21505045, 21705048, U1532119, 21675167, 21390414), National Key R&D Program of China (2016YFA0201200, 2016YFA0400900), Key Research Program of Frontier Sciences, CAS (QYZDJ-SSW-SLH031), the Shanghai Pujiang Talent Project (16PJ1402700).

Author Contributions W.L. and L.R. contributed equally to this work.

Notes The authors declare no competing financial interests.

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Programming Chemical Reaction Networks Using Intramolecular

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