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An Assembly Funnel Makes Biomolecular Complex Assembly Efficient

Like protein folding and crystallization, the self-assembly of complexes is a fundamental form of biomolecular organization. While the number of methods for creating synthetic complexes is growing rapidly, most require empirical tuning of assembly conditions and/or produce low yields. We use coarse-grained simulations of the assembly kinetics of complexes to identify generic limitations on yields that arise because of the many simultaneous interactions allowed between the components and intermediates of a complex. Efficient assembly occurs when nucleation is fast and growth pathways are few, i.e. when there is an assembly “funnel”. For typical complexes, an assembly funnel occurs in a narrow window of conditions whose location is highly complex specific. However, by redesigning the components this window can be drastically broadened, so that complexes can form quickly across many conditions. The generality of this approach suggests assembly funnel design as a foundational strategy for robust biomolecular complex synthesis.


Introduction
Within cells, bottom-up phenomena organize biomolecules into structures with sizes ranging from angstroms to microns. Precise control over structure at the angstrom and nanometer scales is important for optimizing catalysis [1,2], the action of molecular machines [3] or molecular recognition [4]. Larger biomolecular structures orchestrate processes such as translation, adhesion, or controlled transport. One goal of chemistry and molecular engineering is therefore to develop analogous bottom-up methods for controlling biomolecular structure across the same range of dimensions [5,6].
Different physical processes are responsible for the in vivo formation of structure across these length scales. Stable nanometer-or angstrom-scale structures generally form as the result of folding a protein or RNA chain with a particular sequence [2]. Folding larger structures from a single chain is difficult because synthesizing long, sequence-specific polymers without errors is a challenge [7] and the potential for a folding process to become frustrated increases quickly with polymer length [8,9]. Larger structures instead form through a hierarchical assembly process in which folded components self-assemble together into a larger complex. Examples of such complexes include the ribosome, proteasome and antibodies. Some complexes, including the nuclear pore complex [10], cell adhesions [11] or the kinetochore [12] can contain hundreds of components and reach sizes of more than a micron. Complex formation is ubiquitous: in Escherichia coli, for example, more than 20% of known polypeptides become reported members of protein complexes [13].
While the development of strategies for the design of synthetic self-assembling complexes have long lagged behind the design of folding processes, recently, a wealth of designed complexes assembled from proteins [14], nucleic acids [15] and other components [16,17] has spurred interest in developing rules and general strategies for designing complexes [18][19][20]. Generally, design methods attempt to maximize complex yield by maximizing the thermodynamic stability of the complex or the free energy difference between the complex and other potential structures with the inherent assumption that thermodynamic equilibrium will be achieved [20][21][22][23]. Yet, in practice, complexes that are thermodynamically stable often assemble with low yields or may take as long as weeks to assemble properly [24][25][26]. While unaccounted-for experimental effects such as stoichiometric imbalances between components [27] might explain lower yields or slower than expected assembly times, kinetic factors that could limit yield are rarely investigated. To improve yields and dynamics, there are currently few strategies other than complex redesign [28] when thermodynamic design considerations fail.
Here, we test the assumption that self-assembly processes for biomolecular complexes generally reach a high-yield equilibrium state by simulating the kinetics of a variety of generic, idealized assembly reactions. We find that for typical biomolecular complex self-assembly reaction rates and component concentrations [29][30][31][32], it may take days or weeks to reach a state close to equilibrium, even when equilibrium yields are low. Thus, design processes that rely solely on thermodynamics to predict yields may meet with mixed success because yield is limited kinetically rather than thermodynamically. Our simulations also identify two key reasons why some self-assembly processes can be slow. First, near the melting temperature of the complex, low nucleation rates limit the rate of formation of complexes. Second, far below the melting temperature, assembly may occur rapidly through many different pathways, combinatorially trapping intermediate assembly products. Once assembly reaches this trapped state, complexes can form only after intermediates disassemble, which can be very slow. Avoiding both of these regimes is required to achieve high yield. For many common complexes, this requirement means that complex formation happens efficiently only under a narrow range of physical conditions. We show that designing components that skirt such kinetic pitfalls can significantly speed up assembly and enhance yields.

A simple model of multicomponent biomolecular complex self-assembly
To characterize the kinetics of complex assembly, we use a simple model of assembly in which rigid components of a generic complex bind to one another via orientation-specific pairs of complementary interfaces (Fig. 1). We assume that all components have identical interaction energy at each interface and the same initial concentration. Interaction between non-matching interfaces, or crosstalk, is neglected, reflecting rapid advances in the design of specific biomolecular interfaces [14,[24][25][26]33,34]. Multiple different rigid components and their unique interfaces could be easily fabricated from DNA, for example, using existing techniques such as DNA origami [27] or DNA bricks [25]. In our model, we consider all binary reactions that produce a complex or any connected subset of components, which we call intermediates (see Supporting Methods).
Our simulations use on and off rates similar to those measured for oligonucleotide [32], protein [30], DNA tile [31], and ribosomal subunit-RNA [29] reactions. Simulated assembly protocols are simple and modeled after those in broad experimental use [26][27][28]. Assembly timescales are realistic, ranging from t~1, or about 30 minutes to t~1000, or about 2.7 weeks for 1 nM of components (i.e., concentrations typical for large (megadalton) DNA nanostructures [27]), or 30 minutes for 1 uM of components (see Equation 1). To model the interplay of changes in bond energy that could result from multi-bond reactions (e.g., from entropic or allosteric effects [35]), we introduce a dimensionless bond coupling term a that determines how the free energy of interaction scales with the number of bonds formed (see Equation 2). We use the dimensionless parameter g:log 10 ( kon koff ,1 ½X 0 ) as an analog for inverse temperature (e.g., high values of g correspond to low temperatures and strong interactions and vice versa) and define yield as the fraction of total material in complete complexes (see Equation 3).
The goal of our study is to understand how yields of selfassembled biomolecular complexes vary with complex size (in terms of number of components), geometry and reaction parameters (e.g., k off ,b , a 0 ) by using kinetic simulations and as a result, learn how to design complexes and assembly protocols to increase yields. In order to elucidate general principles, we focus on a set of generic complexes: 1-dimensional ''line'' complexes of different lengths, 2-dimensional square ''grid'' complexes with different numbers of components on a side and a 3-dimensional ''cube'' complex.
Estimating the yield of a complex by considering its free energy relative to the free energies of other potential products is a standard method of estimating the yield of a self-assembly reaction [36], but such estimates are relevant only when assembly reactions are close to equilibrium. To determine whether typical reactions approach equilibrium, we modeled the kinetics of assembly using component concentrations, reaction times and rates typical of experimental self-assembly reactions [29][30][31][32]. To understand the effects of temperature, we initially studied reactions that take place at a single temperature (a single value of g). Isothermal assembly of 1D line complexes quickly achieved yields near those predicted at thermodynamic equilibrium for all interaction energies considered ( Fig. 2a and Figs. S3 and S4). The system as a whole also approached equilibrium, as demonstrated by the concentrations of both complexes and intermediates (Fig. S5). Yields of line complexes were highest when the interactions between components were strongest, in agreement with both thermodynamic predictions and similar studies of self-assembly kinetics [37].

Strong interactions maximize yield for 1-dimensional systems only
While strong interactions maximize the yield of line complexes, strong interactions in even small grid or cube complexes with no bond coupling (a 0~1 ) produced yields far lower than yields expected at equilibrium for simulated reaction times as long as weeks (t~1000, ,2.7 weeks for ½X 0~1 nM) (see Figs. 2b-f and Figs. S8 and S9). Further, after a certain point, increasing reaction time only marginally increases yield (Figs. S11, S22). For example, increasing the assembly time from t~100 to t~1000 increased the yield of 363 grid complexes by at most ,10%. Similarly marginal increases in yield were observed when assembly times were increased further to t~10000 (Fig. S10). These results suggest that these self-assembly processes rarely approach the equilibrium state in practice.

Slow nucleation and molecular rearrangement rates can limit yield
To understand why grid and cube complexes assembled so slowly, we investigated the composition of the simulated solution after the completion of the reaction (t~1000) under many isothermal assembly conditions (Fig. 2, and Figs. S13, S15 and S23). Above the melting temperature of a given complex, no complexes form. Just below the melting temperature, the most abundant species aside from complexes were components, suggesting that yield under these highly reversible conditions is limited by the long times required to nucleate intermediates. Under effectively irreversible conditions (i.e., high values of g), intermediates that cannot interact with one another to form complexes were the most common species, including the four 3component intermediates in the 262 square grid complex and the 5 to 8-component intermediates in the 363 square grid complex (Fig. S14). Under these conditions, components or smaller intermediates must detach from a larger intermediate and attach to another intermediate, or ''rearrange'', in order to complete a complex, which is an energetically unfavorable and therefore slow process. This rearrangement-limited regime is present for the assembly of grid and cube complexes but not line complexes because the intermediates to line complexes never need to rearrange to produce complexes. These results are corroborated by studies of viral capsid assembly [38] as well as homomeric [39]and ring-like protein complex assembly [40], where nucleation and rearrangement rates were found to influence assembly efficiency and fidelity.
The results thus far indicate that the self-assembly of grid and cube complexes could occur with high yields when bond strengths are neither too weak for fast nucleation nor too strong to prevent components in intermediates from rearranging. Indeed, our simulations show that there is a small window of medium component-component interaction strength where complexes are stable and assemble with high yields without requiring infeasibly long assembly times. We called this regime the ''assembly funnel'' regime, because in this regime the energy landscape contains a small number of smooth downward paths to complete complex formation, similar to a protein folding funnel [41] or a protein binding funnel [42]. This regime for grid and cube complexes is generally near k on ½X 0~k off ,1 or g~0. In our simulations of 262 to 565 square grid complexes, we found that increasing complex size shrinks the size of the already small assembly funnel regime by disfavoring forward conditions (i.e., where gw0). Increasing complex size increases the number of ways components can become ''stuck'' in incompatible intermediates, so completing a larger complex requires more molecular rearrangement on average than completing a smaller one.

Reaction conditions determine the set of possible assembly pathways
To further understand the influence of pathways on complex formation, we examined the kinds of intermediates that tend to arise and persist by measuring the conformational entropy, or distribution of species sizes and free energies, of the system. The conformational entropy is given by S~{R P f ij is the fraction of species with energy i and j components. Higher values of conformational entropy correspond to broader distributions of assembly sizes and free energies. Under rearrangementlimited conditions, conformational entropy initially increases as many different intermediates form, and then plateaus (Fig. 3a). The species that form and remain are those that are most easily accessible via reaction pathways rather than those that are energetically favorable (Fig. 3b, c). In contrast, assembly in the assembly funnel regime favors the production of a relatively small number of intermediates, those lowest in free energy, so conformational entropy decreases with time as these low-energy intermediates and complexes form. Because complex size and geometry determine the possible reaction pathways and the types of assembly intermediates that can form [43], they also control the propensity of an assembly process to become ''stuck'' under a given set of reaction conditions.
The time spent in the assembly funnel regime determines the yield While complexes form quickly in the assembly funnel regime, the specific reaction conditions that generate an assembly funnel depend on the set of possible reaction pathways as well as kinetic and thermodynamic parameters that are generally unknown and difficult to estimate. One solution to this problem is to assemble via annealing. A typical annealing protocol begins at a temperature above the melting temperature of the complex, which is then gradually decreased until effectively irreversible conditions are achieved. To determine how yields using this protocol compare to those during isothermal assembly, we simulated annealing for square grid complexes. We found that yields during an anneal are predominately determined by the amount of the time spent in the assembly funnel regime. As the temperature decreases, few complexes form before the assembly funnel regime is reached. Within the funnel regime, complexes form rapidly, primarily through thermodynamic pathways (Figs. 3, 4 and Figs. S16-S18). After the annealing moves out of the assembly funnel regime, complexes are stabilized, but relatively few new complexes form. Thus, assembly via annealing is relatively efficient even when it is not known which conditions that generate an assembly funnel, which is in agreement to recent computational findings on DNA brick self-assembly [44]. However, to produce high yields, an anneal must be slower than a comparable isothermal assembly process in the assembly funnel regime because complex formation is slow for the majority of the anneal. This effect becomes more pronounced as complex size increases because the range of reaction conditions that produce an assembly funnel decreases. Thus, for very large complexes, it may be important to find ideal isothermal conditions, even when annealing is a practical option for assembly [28].
Just a small amount of bond coupling between components is needed for high yield 2-and 3-dimensional complexes are generally stabilized by the interactions of multiple bonds between components, and the specific free energy changes that result from multi-bond interactions also shape the energy landscape for assembly [45]. To determine how the free energy of multi-bond interactions influences yield, we characterized changes in yield as we altered the coupling between multiple interfaces on a component. Surprisingly, we found that bond coupling was not an important determinant of assembly yield (see Fig. 5 and Figs. S7, S21). Although positive coupling (a 0 w1) slightly broadens the set of conditions where complex yields are high at thermodynamic equilibrium (Figs. S6, S20), it leads neither to increased nucleation rates nor component rearrangement rates and thus does not increase yields in practice. Negative coupling (a 0 v1) does not always reduce yields in the assembly funnel regime and can even marginally enhance yields under rearrangement-limited isothermal conditions by destabilizing some intermediates (Text S5). Thus, high-yield assembly can be obtained under the proper assembly conditions for a wide range of bond coupling values, as any coupling value is subject to equal pressures on nucleation and rearrangement rates.
Components can be designed to assemble efficiently because they assemble via an assembly funnel under most conditions While it is challenging to optimize reaction conditions to produce high yields, might it be possible to create components that broaden the assembly funnel regime and thus self-assemble a desired complex more efficiently? To address this question, we designed components for a 2-dimensional target structure that were expected to have a smaller barrier to nucleation than the Dimensionless reaction time is defined as t~k f ½X 0 t where k f is the macroscopic forward reaction rate constant and ½X 0 is the initial concentration of components. Colored bars and boxes below figures represent the four different assembly regimes (Text S3). The assembly funnel regime is considered to be where the complex is thermodynamically favored (i.e., yield eq w0:5) and assembly is rapid such that yield t~1000 §0:8 : yield eq . Assembly ''snapshots'' (below graphs) are taken at t~1000 and g~{2 (top row), g~0, g~1, and g~6 (bottom row) and comprised of ten random species drawn from the reaction mixture, weighted by concentration (Text S4). Error bars indicate the standard deviation of the reported quantity after 10 simulations and where omitted, are ,1%. Here and elsewhere unless otherwise noted, there is no bond coupling (a 0~1 ). doi:10.1371/journal.pone.0111233.g002  components of the grid complex we studied above. In a ''spiral complex,'' a spiral-shaped growth pathway allows all components to attach to the growing assembly via multiple bonds, so that there is no nucleation barrier to assembly. Because all other growth pathways require that components interact with one another via a single bond, the single spiral-shaped growth pathway is favored (Fig. 6a). Compared to square grid complex counterparts, the 4-, 9-and 16-component spiral complexes assemble faster and even achieve thermodynamic equilibrium in nucleation-limited regimes, broadening the reaction conditions that generate an assembly funnel ( Fig. 6b-d). As a result, an anneal produces complexes more quickly, by almost an order of magnitude (Figs. S11 and S12). While the spiral scheme does not improve yield in the rearrangement-limited regime, this exercise suggests that effective self-assembly design strategies will likely promote rapid, high-yield complex formation by considering reaction pathways as well as nucleation and rearrangement rates.

Discussion
Most existing strategies for the design and analysis of selfassembly processes use the thermodynamics of a complex as a starting point for predicting structure and yield. This strategy has been successful for understanding the assembly process of homogeneous or periodic crystals and superlattices [46]. While in principle, these strategies can be extended to guide the design of finite, heterogeneous complexes, we find that for a large class of multicomponent assembly processes, these strategies are insufficient because assembly is kinetically limited. Our results are echoed by experimental studies in which complex yields are low even when the desired product is strongly thermodynamically favored [25,27], and in which assembly can be made efficient by assembling at a constant temperature at which assembly is optimal [28], in what we term the assembly funnel regime, if such a regime can be found. In fact, the assembly funnel assembly strategy has been used in the self-assembly homogenous multicompartment micelles [47].
Although optimizing assembly conditions appears difficult, this work suggests that it may be much more productive to design components such that they assemble efficiently through one or a small number of reaction pathways. This strategy of designing components that assemble efficiently appears to be important in vivo, as the components of protein complexes are under evolutionary pressure to assemble via ordered pathways [48].
One major assumption in this work is perfectly formed components: we do not address the challenge to form the components in the first place. In successfully forming biomolecular complexes, components must first be properly synthesized and folded or fabricated before they can associate to form a complex. Components that misfold or degrade can alter the assembly landscape by allowing the possibility of nonspecific interactions (e.g., resulting in aggregated products, as clearly evidenced by diseases such as amyloidosis), which provides another, perhaps even larger, challenge in understanding complex assembly.
While this work will need to be extended to take into account artifacts of assembly such as component defects and differences in component stoichiometry and bond energies, this work adds to growing evidence that the physics of assembly of multicomponent, aperiodic structures is not simply an extension of principles for assembling homogeneous or periodic structures [6]. Assembly of multicomponent lattices and crystals also appear to occur far from equilibrium in general [49,50] even when component depletion is offset by continued production of new components, as happens in in vivo systems. Specific attention to effects that arise in multicomponent systems, such as the interplay between combinatoric and thermodynamic factors explored here, are likely to be important in developing the capacity to self-assemble larger, more intricate structures robustly.

Stochastic kinetic simulations
The dynamics of the reactions to form a complex are determined using Gillespie sampling of stochastic chemical kinetics [51]. While typically stochastic fluctuations are not important to assembly results, the Gillespie algorithm makes it possible to statistically sample kinetic trajectories that would otherwise be inaccessible because the numerical integration of the coupled set of ODEs for mass action kinetics is intractable for most of the complexes we study (Table S1 and Text S2). For small complexes where comparison is possible, stochastic kinetic simulations and mass action kinetics produce nearly identical results (Fig. S21).

Rate constants and physical parameters
For all reactions, the macroscopic on rate constant is assumed to be constant, k on~kf~6 |10 5 1 M : sec , reflecting experimental data for DNA and proteins [29][30][31][32], which additionally simplifies analysis by providing an energy landscape for assembly. Because in practice intermediates and complexes may diffuse more slowly than components due to their increased size, this assumption likely underestimates assembly times. We define dimensionless time.
where k f is the macroscopic on rate constant, ½X 0~0 :1 nM is the initial component concentration and t is dimensional reaction time in seconds.
To model the interplay of changes in bond energy that could result from multi-bond reactions, we introduce a dimensionless bond coupling term a that determines how the free energy of interaction scales with the number of bonds formed. This bond coupling term is given by: where a 0 is a dimensionless coupling constant and b is the number of bonds formed in the reaction. Interfaces are energetically independent in the case of zero (a 0~1 ) bond coupling. Negative coupling (0ƒa 0 v1) means that the interaction of multiple bonds is less favorable than the sum of the individual bond energies whereas positive coupling (a 0 w1) means the same interaction is more favorable than the sum of the individual bond energies. The coupling term appears in the macroscopic off rate equation: where DG 0 is the change in standard Gibbs free energy for a component-component interaction through a single bond, T is absolute temperature and R is the universal gas constant. For detailed information on species and reaction enumeration algorithms, as well as kinetic simulation specifics, see Text S1.  not included in our model. In (c), the components do not interact at any edges and would not produce a valid species as a product, and in (d) the reactants share components in the same position, which would in practice block that reaction from happening. (TIF) Figure S3 Yields of 163 to 169 line complexes at various isothermal conditions. Dashed lines indicate thermodynamic equilibrium. Inset diagrams depict the complexes. Here, as in the main text, g:log 10 (k on =k off ,1 ½X 0 ) and t~k f ½X 0 t. For all figures in the Supporting Information, unless otherwise noted, there is no bond coupling (a 0~1 ) and error bars are ,1%.  Figure S15 Size distribution of intermediates for various 2D complexes. Mean size of intermediates (in number of components) after t~1000, N int,t~1000 , normalized by the number of components in the complex, N cplx , for 262, 363, 464 and 565 square grid complexes at different isothermal assembly conditions. Inset diagrams depict the complexes. The mean intermediate size is defined as the mean size of the species in the system, not including complexes or components. Nucleation-limited conditions produce mean intermediate assembly sizes equal or less than half of the size of a complex whereas rearrangement-limited conditions allow intermediates to grow to be, on average, greater than half of the size of a complex. (TIF) Figure S16 During an anneal, most complexes are produced during the phase of the anneal that passes through the assembly funnel. Yield of 262 and 363 square grid complexes during the course of an anneal for various bond coupling constants. Inset diagrams depict the complexes being assembled. The anneal begins from left to right, with the total time of the anneal given as the value of t in the legend. The annealing process is simulated by changing the strength of component-component interactions as the reaction proceeds. At the start of the simulation (t=t~0), g~{6 and over the course of the simulation the interaction strength is logarithmically increased 100 times, in equal reaction time intervals (i.e., t=100), to ultimately obtain g~6 at the end of the simulation (t=t~1). In practice, this annealing protocol corresponds to a linear decrease in temperature over time. Assembly regimes are determined by isothermal assembly (see Figure S6). (TIF) Figure S17 During very long anneals, component depletion can increase the amount of time that the system effectively stays within the assembly funnel regime. Effective reaction propensity is given by g eff :log 10 (k on =k off ,1 ½component) where ½component is the current average component concentration, for the 262 and 363 square grid complexes as a function of annealing conditions after various annealing times. Color bars on the left side of the figures correspond to different assembly regimes. Inset diagrams depict the complexes. Effective reaction propensities for slower anneals remain in the assembly funnel regime for longer periods of time, not only because of their increased time of anneal, but also because components are depleted during annealing. This decrease offsets the effect of the off rate (k off ,1 ) decreasing as the temperature decreases. As a result, during a slow anneal g eff can be in the assembly funnel regime even as g drops into rearrangement-limited conditions. During fast anneals (t~1), the off rate changes much faster than components deplete, accounting for the linear relationship between g and g eff . Dashed line approximates the g eff for an ideal anneal (where t??). In an ideal anneal, components would deplete in proportion to the decrease in the off rate and thus always remain in the assembly funnel regime after initially entering it. (TIF) Figure S18 The time spent in the assembly funnel regime can be used to predict the outcome of an anneal. Yield of 363 square grid complex as a function of reaction time for an isothermal assembly (g~0) and for an anneal. Inset diagram depicts the complex. For a 363 square grid complex, the assembly funnel regime ranges from {1vgv1 (see Figure 2). The red and blue dots are estimated yields calculated by computing the time the anneal spends in the assembly funnel regime and, with this value, estimating yield by linear interpolation of an g~0 isothermal assembly. With no component depletion effects (red), a given anneal of time t anneal , will spend t assembly funnel & t anneal 6 in the assembly funnel regime.

Supporting Information
With component depletion effects (blue, see Figure S16), the time spent in the assembly funnel regime will correspond to the time that the anneal remained {1vg eff v1 so that the slower the anneal, the higher the fraction of total reaction time spent in the assembly funnel regime. For example, when t anneal~1 , t assembly funnel~0 :18t anneal and when t anneal~1 000, t assembly funnel~0 :34t anneal . The method of estimating yield via annealing that includes component depletion effects more closely resembles the actual annealing yield, suggesting that component depletion effects, which serve to increase the time spent in the assembly funnel regime and in turn enhance yields, occurs during annealing. (TIF)   Text S1 Supporting Methods.