An assembly funnel makes biomolecular complex assembly efficient

Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists

Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society's most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales-molecular, circuit/network, cell/cell-free systems, biological communities, and societal-giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists.

Discovering optimal kinetic pathways for self-assembly using automatic differentiation

Macromolecular complexes are often composed of diverse subunits. The self-assembly of these subunits is inherently nonequilibrium and must avoid kinetic traps to achieve high yield over feasible timescales. We show how the kinetics of self-assembly benefits from diversity in subunits because it generates an expansive parameter space that naturally improves the "expressivity" of self-assembly, much like a deeper neural network. By using automatic differentiation algorithms commonly used in deep learning, we searched the parameter spaces of mass-action kinetic models to identify classes of kinetic protocols that mimic biological solutions for productive self-assembly. Our results reveal how high-yield complexes that easily become kinetically trapped in incomplete intermediates can instead be steered by internal design of rate-constants or external and active control of subunits to efficiently assemble. Internal design of a hierarchy of subunit binding rates generates self-assembly that can robustly avoid kinetic traps for all concentrations and energetics, but it places strict constraints on selection of relative rates. External control via subunit titration is more versatile, avoiding kinetic traps for any system without requiring molecular engineering of binding rates, albeit less efficiently and robustly. We derive theoretical expressions for the timescales of kinetic traps, and we demonstrate our optimization method applies not just for design but inference, extracting intersubunit binding rates from observations of yield-vs.-time for a heterotetramer. Overall, we identify optimal kinetic protocols for self-assembly as a powerful mechanism to achieve efficient and high-yield assembly in synthetic systems whether robustness or ease of "designability" is preferred.

Discovering optimal kinetic pathways for self-assembly using automatic differentiation

During self-assembly of macromolecules ranging from ribosomes to viral capsids, the formation of long-lived intermediates or kinetic traps can dramatically reduce yield of the functional products. Understanding biological mechanisms for avoiding traps and efficiently assembling is essential for designing synthetic assembly systems, but learning optimal solutions requires numerical searches in high-dimensional parameter spaces. Here, we exploit powerful automatic differentiation algorithms commonly employed by deep learning frameworks to optimize physical models of reversible self-assembly, discovering diverse solutions in the space of rate constants for 3-7 subunit complexes. We define two biologically-inspired protocols that prevent kinetic trapping through either internal design of subunit binding kinetics or external design of subunit titration in time. Our third protocol acts to recycle intermediates, mimicking energy-consuming enzymes. Preventative solutions via interface design are the most efficient and scale better with more subunits, but external control via titration or recycling are effective even for poorly evolved binding kinetics. Whilst all protocols can produce good solutions, diverse subunits always helps; these complexes access more efficient solutions when following external control protocols, and are simpler to design for internal control, as molecular interfaces do not need modification during assembly given sufficient variation in dimerization rates. Our results identify universal scaling in the cost of kinetic trapping, and provide multiple strategies for eliminating trapping and maximizing assembly yield across large parameter spaces.

Failure Mechanisms in DNA Self-Assembly: Barriers to Single-Fold Yield

Understanding the folding process of DNA origami is a critical stepping stone to the broader implementation of nucleic acid nanofabrication technology but is notably nontrivial. Origami are formed by several hundred cooperative hybridization events-folds-between spatially separate domains of a scaffold, derived from a viral genome, and oligomeric staples. Individual events are difficult to detect. Here, we present a real-time probe of the unit operation of origami assembly, a single fold, across the scaffold as a function of hybridization domain separation-fold distance-and staple/scaffold ratio. This approach to the folding problem elucidates a predicted but previously unobserved blocked state that acts as a limit on yield for single folds, which may manifest as a barrier in whole origami assembly.

Typical Stochastic Paths in the Transient Assembly of Fibrous Materials

When chemically fueled, molecular self-assembly can sustain dynamic aggregates of polymeric fibers-hydrogels-with tunable properties. If the fuel supply is finite, the hydrogel is transient, as competing reactions switch molecular subunits between active and inactive states, drive fiber growth and collapse, and dissipate energy. Because the process is away from equilibrium, the structure and mechanical properties can reflect the history of preparation. As a result, the formation of these active materials is not readily susceptible to a statistical treatment in which the configuration and properties of the molecular building blocks specify the resulting material structure. Here, we illustrate a stochastic-thermodynamic and information-theoretic framework for this purpose and apply it to these self-annihilating materials. Among the possible paths, the framework variationally identifies those that are typical-loosely, the minimum number with the majority of the probability. We derive these paths from computer simulations of experimentally-informed stochastic chemical kinetics and a physical kinetics model for the growth of an active hydrogel. The model reproduces features observed by confocal microscopy, including the fiber length, lifetime, and abundance as well as the observation of fast fiber growth and stochastic fiber collapse. The typical mesoscopic paths we extract are less than 0.23% of those possible, but they accurately reproduce material properties such as mean fiber length.

Direct observation and rational design of nucleation behavior in addressable self-assembly

Current efforts aimed at constructing complex supramolecular structures often suffer from low yields or require long assembly protocols. We address these problems by demonstrating a facile strategy for optimizing the nucleation step of a multicomponent self-assembly reaction. By tracking the formation of multisubunit clusters in situ, our experiments show that modifying the critical nucleus required to initiate structure growth can broaden the range of conditions over which self-assembly occurs and, consequently, can dramatically improve the final yield of correctly formed structures. Since varying the design of only a small portion of the target structure optimizes its yield, this strategy provides a practical route to improve the speed and accuracy of self-assembly in biomolecular, colloidal, and nanoparticle systems.

To optimize a self-assembly reaction, it is essential to understand the factors that govern its pathway. Here, we examine the influence of nucleation pathways in a model system for addressable, multicomponent self-assembly based on a prototypical “DNA-brick” structure. By combining temperature-dependent dynamic light scattering and atomic force microscopy with coarse-grained simulations, we show how subtle changes in the nucleation pathway profoundly affect the yield of the correctly formed structures. In particular, we can increase the range of conditions over which self-assembly occurs by using stable multisubunit clusters that lower the nucleation barrier for assembling subunits in the interior of the structure. Consequently, modifying only a small portion of a structure is sufficient to optimize its assembly. Due to the generality of our coarse-grained model and the excellent agreement that we find with our experimental results, the design principles reported here are likely to apply generically to addressable, multicomponent self-assembly.

DNA brick self-assembly with an off-lattice potential

We report Monte Carlo simulations of a simple off-lattice patchy-particle model for DNA 'bricks'. We relate the parameters that characterise this model with the binding free energy of pairs of single-stranded DNA molecules. We verify that an off-lattice potential parameterised in this way reproduces much of the behaviour seen with a simpler lattice model we introduced previously, although the relaxation of the geometric constraints leads to a more error-prone self-assembly pathway. We investigate the self-assembly process as a function of the strength of the non-specific interactions. We show that our off-lattice model for DNA bricks results in robust self-assembly into a variety of target structures.

Nanomanufacturing: A Perspective

Nanomanufacturing, the commercially scalable and economically sustainable mass production of nanoscale materials and devices, represents the tangible outcome of the nanotechnology revolution. In contrast to those used in nanofabrication for research purposes, nanomanufacturing processes must satisfy the additional constraints of cost, throughput, and time to market. Taking silicon integrated circuit manufacturing as a baseline, we consider the factors involved in matching processes with products, examining the characteristics and potential of top-down and bottom-up processes, and their combination. We also discuss how a careful assessment of the way in which function can be made to follow form can enable high-volume manufacturing of nanoscale structures with the desired useful, and exciting, properties.

The Energy Landscape for the Self-Assembly of a Two-Dimensional DNA Origami Complex

While the self-assembly of different types of DNA origami into well-defined complexes could produce nanostructures on which thousands of locations can be independently functionalized with nanometer-scale precision, current assembly processes have low yields. Biomolecular complex formation requires relatively strong interactions and reversible assembly pathways that prevent kinetic trapping. To characterize how these issues control origami complex yields, the equilibrium constants for each possible reaction for the assembly of a heterotetrameric ring, the unit cell of a rectangular lattice, were measured using fluorescence colocalization microscopy. We found that origami interface structure controlled reaction free energies. Cooperativity, measured for the first time for a DNA nanostructure assembly reaction, was weak. Simulations of assembly kinetics suggest assembly occurs via parallel pathways with the primary mechanism of assembly being hierarchical: two dimers form that then bind to one another to complete the ring.

Effects of co-ordination number on the nucleation behaviour in many-component self-assembly

We report canonical and grand-canonical lattice Monte Carlo simulations of the self-assembly of addressable structures comprising hundreds of distinct component types. The nucleation behaviour, in the form of free-energy barriers to nucleation, changes significantly as the co-ordination number of the building blocks is changed from 4 to 8 to 12. Unlike tetrahedral structures – which roughly correspond to DNA bricks that have been studied in experiments – the shapes of the free-energy barriers of higher co-ordination structures depend strongly on the supersaturation, and such structures require a very significant driving force for structure growth before nucleation becomes thermally accessible. Although growth at high supersaturation results in more defects during self-assembly, we show that high co-ordination number structures can still be assembled successfully in computer simulations and that they exhibit self-assembly behaviour analogous to DNA bricks. In particular, the self-assembly remains modular, enabling in principle a wide variety of nanostructures to be assembled, with a greater spatial resolution than is possible in low co-ordination structures.