Our walkers, called molecular spiders, comprise an inert body (streptavidin) and three catalytic “legs”. Legs are adapted from DNA enzyme 8-17 that binds and cleaves oligodeoxynucleotide (henceforth “oligonucleotide”) substrates with a single ribose moiety () into two shorter products that have lower affinities for the enzyme18
. A spider’s interactions with a layer of immobilized substrate and/or product sites can be modeled using a simple ‘memory’ principle19
: Each leg moves independently from sites to accessible neighboring sites, but if a leg is on a site not visited before, it will stay longer on average. Restated in a biochemically more intuitive manner: A deoxyribozyme on a site that was previously converted to a product will dissociate faster, whereas it will stick longer on the substrates and eventually cleave them. Because spiders have multiple legs, a single dissociated leg will quickly reattach to nearby product or substrate. It follows that the body of a spider positioned at the interface between products and substrates will move toward the substrate region, because after cleaving, each leg will explore neighboring sites until it finds another substrate. On a linear track of substrates this mechanism predicts a deviation from an otherwise random walk process, yielding directional movement as the substrates are cleaved. Unlike previously engineered “burnt bridge” mechanisms6-9,11
and those found in nature20
, which render revisiting the same path impossible, spiders will perform Brownian walks on product tracks until they again encounter substrate.
Deoxyribozyme based molecular walker and origami prescriptive landscape schematics
In analogy to the reactive planning used in simple robots4
, the sensor-actuator feedback afforded when legs sense and modify nearby oligonucleotides allows us to design prescriptive landscapes that direct the spiders’ motion along a predefined path (). Prescriptive landscapes were constructed using the DNA origami scaffolding technique13
. The scaffold consists of a 7249-nucleotide single-stranded DNA folded by 202 distinct staple strands into a rectangular shape roughly 65×90×2 nm in size and with 6-nm feature resolution (). Each staple can be extended on its 5′ end with probes that recruit substrates, products, goal and control strands21
We designed pseudo-one dimensional tracks on origami of about spider width (three adjacent rows of substrates, ). Tracks are coded by a sequence of points (A, B, C, D, E; i.e., on an ABD landscape the spider starts at A, and passes through B before ending at D). Staples were modified to position: (1) A START oligonucleotide, used to position a spider at the start of the experiment, that is complementary to a TRIGGER oligonucleotide used to release the spider22
(the “start” action); (2) Substrate TRACK probes to capture the 5′ extension on substrates forming the TRACK (directing the “follow” and “turn” actions); (3) STOP probes complementary to the 5′ extension on STOP strands (non-chimeric and uncleavable analogs of the substrate) that do not influence directional movement but trap spiders to prevent them from walking backwards after completing the track (the “stop” action); (4) CONTROL probes (identical to the STOP, but disconnected from the track), used to assess the extent to which free-floating spiders are captured directly from solution; and (5) MARKER oligonucleotides based on inert dumbbell hairpins, aiding in origami classification within atomic force microscopy (AFM) images (). To position spiders at START sites, we replaced one of the four catalytic legs of the NICK-4.4A12
spider with a tethering oligonucleotide (Supplementary Figs 1-4
and Supplementary Information) partially complementary to the START oligonucleotide.
To estimate the efficiency of spider motion directed by the TRACK, we defined and tested four paths with no (EAC), one (ABD), or two (EABD, EABC) turns ( and Supplementary Figs 8, 11, 14, 17
). Our basic procedure consisted of: (1) Assembling the origami; (2) attaching the spider to the START site; (3) adding TRACK, STOP, and CONTROL strands to complete the landscape; and (4) initiating an experiment by releasing the spider through addition of TRIGGER and 1 mM Zn2+
(Supplementary Figs 6, 25
, and Supplementary Information). We sampled the origami solution before and after spider release, and imaged individual samples by AFM to determine the locations of spiders. We scored only “face-up” origami (substrates projected away from mica) to avoid artifacts and developed procedures to minimize readout bias (see Supplementary Information for details).
Results of spider movement along three tracks with schematics and AFM images of the spider at the start, on the track, and at the stop site
In all samples imaged before spider release, 30-40% of the assembled origami carry at least one spider, 80-95% of which are singly occupied, and of these 80-90% bound their spider at the START position (Supplementary Table 1
and Supplementary Figs 9, 10, 12, 13, 15, 16, 18, and 19
). Upon adding trigger, all four landscapes with substrate tracks showed that the fraction of spiders at the START diminishes with a concomitant increase in spiders observed on the STOP sites ( and Supplementary Fig. 23
). A spider’s ability to reach the STOP sites decreased with increased TRACK length and with decreased time of incubation in solution. In time-lapse experiments on a long path (EABD, spanning ~ 90 nm) we observed a gradual increase of up to 70% of spiders on STOP sites within 60 min (). A short path (ABD, ~ 48 nm) was completed to the same extent within 30 min.
We captured one series of AFM images of a spider moving along an origami track (). The rate of spider movement (~90 nm over 30 min, with approximately 6 nm per three parallel cleavage events) was consistent with the processive cleavage rates (~1 min−1
) of spiders on a 2D surface as obtained by SPR (Supplementary Fig. 6
). More systematic sequential imaging proved difficult due to mica’s inhibitory effects on the spider.
We can eliminate deviations from the proposed mechanism of spider motion as major contributors to these results. First, to test that spiders can indeed traverse product tracks by means of unbiased random walks, we challenged spiders with EABD origami in which the substrate was replaced by product on the TRACK. Spiders still reached the STOP sites albeit more slowly (), as expected from purely Brownian spider movement even if individual steps are somewhat faster19
. Second, we wished to confirm that spiders don’t often ‘jump’; if all three legs simultaneously dissociate before any leg reattaches, a spider could completely dissociate from the origami and subsequently reattach elsewhere at random. Evidence against frequent jumping (or an excess of spiders in solution during the initial assembly stage) comes from the low level of spider occupancy at CONTROL sites in both substrate and product track experiments () and the stable proportions of unoccupied and multiply-occupied origami (Supplementary Table 1
; both before and after the addition of trigger, 5-10% of origami displayed more than one spider on its track). In contrast, when spiders were released on ABD landscapes with no TRACK strands, after 30 min we observed an equal distribution between STOP and CONTROL sites (Supplementary Fig. 24
and Supplementary Table 2
), as expected for a process that involves spider dissociation from and random rebinding to the origami. In independent ensemble experiments using surface plasmon resonance, we observed that up to 15% of spiders may dissociate from a non-origami 2D product-covered surface within 60 min under flow conditions (Supplementary Fig. 5
). On similar substrate-covered surfaces, spiders show an average processivity of ~200 substrates before being removed by flow (Supplementary Figs 5 and 6
). Together, these results rule out that spiders move predominantly by jumping; there is insufficient jumping even on product tracks to explain the 50-70% occupation of the STOP sites after walks on ABD, EABC, and EABD substrate tracks.
For a more facile real-time observation of the movement of individual spiders, we applied particle tracking by super-resolution total internal reflection fluorescence (TIRF) video microscopy24
. Four biotin molecules were attached to the underside of the origami for immobilization on the avidin-coated quartz slide. Spiders were covalently labeled with on average 2.3 Cy3 fluorophores, and STOP sites were labeled with 6 Cy5 fluorophores. The labeling allowed us to monitor changes in spider position relative to the STOP site by two-color fluorescent particle tracking25,26
. In a typical experiment, spider-loaded tracks were incubated with TRIGGER and immobilized on the slide (Supplementary Fig. 26
), then Zn2+
was added to promote spider movement via substrate cleavage. Recognizing that the 8-17 activity depends on buffer conditions23
, we obtained the best results from SSC or HEPES with increased Zn2+
concentrations but without Mg2+
(Supplementary Figs 6 and 25
Our resolution was not sufficient to reliably detect turns, so we focused on EAC landscapes. Individual particle traces showed a distribution of behaviors that may result from variations across molecules, idiosyncrasies of the sample preparation, the stochastic nature of the observed process, photobleaching, and/or instrument measurement error (, Supplementary Figs 29-31
, Supplementary Information and Supplementary Table 3
). Despite this variability, moving traces commonly had net displacements between 60 and 140 nm and their mean velocity varied between 1 and 6 nm/min, within error consistent with track length (~90 nm) and deoxyribozyme cleavage rate (~1 min−1
Spiders imaged on origami tracks in real-time using super-resolution TIRF microscopy
To confirm that our particle traces reflect genuine spider movement, we performed tests with and without Zn2+
and/or TRIGGER, both on substrate and product tracks. In each case, RMSD plots varied in a way consistent with the expected corresponding behavior of spiders on origami tracks, despite the inherent noise associated with single particle tracking over tens-of-nanometer length scales and tens-of-minute time scales (). For instance, RMSD plots indicated substantially more movement on substrate tracks in the presence of Zn2+
and trigger than in their individual absence (, Supplementary Figs 30-32
and Supplementary Table 4
). On product tracks, results were consistent with an unbiased random walk with no dependence on Zn2+
. When product tracks were pre-incubated with TRIGGER 30-60 min prior to addition of Zn2+
and onset of imaging (as were substrate tracks), little or no movement was observed (), consistent with spiders having been released and having diffused toward or to the STOP sites prior to imaging. In contrast, when TRIGGER and Zn2+
were both added shortly prior to imaging, substantial movement was observed (), consistent with our AFM results for spiders on product tracks () and with Monte Carlo simulations of spider movement (Supplementary Information and Supplementary Fig. 32
Our single-molecule experiments provided results consistent with random DNA-based walkers guided by their landscapes for as far as 100 nm, for up to 50 cleavage steps, at speeds of roughly 3 nm/min. Still, there are mechanistic limitations: (1) The distance over which a spider can move is confined by dissociation or backtracking, with an increase in processivity achievable only at the cost of a slower velocity12
; (2) the current mechanism consumes substrate, which must be recharged to sustain directed movement; (3) spiders are subject to the stochastic uncertainty as to whether each individual robot can accomplish its task (cf., “faulty” behavior in robotics and “yield” in chemistry); and (4) our walkers are not as fast, efficient, or powerful as protein based walkers with solution phase fuels27
. As candidates for molecular robots, however, they offer the advantages of programmability5,10,28-30
, predictable biophysics5
, and designable landscapes13
. The ability to obtain programmed behavior from the interaction of simple molecular robots with a complex modifiable environment suggests that exploiting stochastic local rules and programming the environment are effective ways to minimize the limitations that molecular construction places on the complexity of robotic behavior at the nanoscale.