Microcompartment assembly around multicomponent fluid cargoes

Abstract

This article describes dynamical simulations of the assembly of an icosahedral protein shell around a bicomponent fluid cargo. Our simulations are motivated by bacterial microcompartments, which are protein shells found in bacteria that assemble around a complex of enzymes and other components involved in certain metabolic processes. The simulations demonstrate that the relative interaction strengths among the different cargo species play a key role in determining the amount of each species that is encapsulated, their spatial organization, and the nature of the shell assembly pathways. However, the shell protein-shell protein and shell protein-cargo component interactions that help drive assembly and encapsulation also influence cargo composition within certain parameter regimes. These behaviors are governed by a combination of thermodynamic and kinetic effects. In addition to elucidating how natural microcompartments encapsulate multiple components involved within reaction cascades, these results have implications for efforts in synthetic biology to colocalize alternative sets of molecules within microcompartments to accelerate specific reactions. More broadly, the results suggest that coupling between self-assembly and multicomponent liquid-liquid phase separation may play a role in the organization of the cellular cytoplasm.

FIG. 1.

Description of the model. (a) Each shell subunit contains “Attractors” (green circles) on the perimeter, which define the shape of the subunit. (b) Attractive interactions between Attractors drive subunit dimerization. Complementary pairs of Attractors are indicated by green arrows in (a) for the pentamer–hexamer interface and in (b) for the hexamer–hexamer interface. A combination of Top–Top (T) and Bottom–Bottom (b) repulsions controls the subunit–subunit angle in a complete shell. (c) Bottom pseudoatoms “B” bind cargo molecules (shown as R and G). (d) The cargo molecules have attractive interactions between each other that depend on pair type.

FIG. 2.

The bulk phase behavior of cargo particles (in the absence of shell subunits) as a function of their binding affinities. (a)–(c) The fraction of R particles in the high-density phase, f_R, as a function of the R–R and G–G affinities for different R–G affinities: (a) weak ɛ_RG = 1.0, (b) moderate ɛ_RG = 1.3, and (c) strong ɛ_RG = 1.6. All energies are given in units of the thermal energy, k_BT. The symbols on the plot identify corresponding snapshots below each plot that illustrate the phase behavior at the indicated value of ɛ_RG. (d)–(f) For the same parameters, the fraction of unlike cargo particles in the first solvation shell relative to random mixing, f_UL, defined in the text. Each simulation contains 46 938 total cargo particles, with equal composition of R and G.

FIG. 3.

Assembly pathways. Snapshots from simulation trajectories illustrate the classes of assembly pathways discussed in the text. In each row, the first four snapshots show frames at indicated time points (in units of the nondimensional time τ), while the last snapshot is a cutaway view of the shell to show the encapsulated cargo. The parameters characterizing binding affinities are listed under each row (shell–shell, ɛ_SS; shell–cargo, ɛ_SC; R–R and G–G, ɛ_RR and ɛ_GG; and R–G, ɛ_RG). (a) All affinities are relatively strong, driving rapid and simultaneous coalescence of both cargo types and shell assembly (one-step assembly). The encapsulated cargo is uniformly mixed (Multimedia view). (b) At ɛ_SS = 3.5 k_BT, high ɛ_RR but moderate ɛ_GG and ɛ_RG lead to one-step assembly and coalescence of R cargo, with G cargo essentially excluded from the shell (Multimedia view). (c) At ɛ_SS = 2 k_BT, strong ɛ_RR but moderate ɛ_GG and ɛ_RG lead to two-step assembly around an almost pure R domain (Multimedia view). (d) Strong ɛ_GG and ɛ_RR and moderate ɛ_RG drive two-step assembly, with coupling between shell closure and cargo compositional fluctuations, leading to encapsulation of nearly pure R and G cargo domains in separate shells. The initial globule that becomes separated is shown in the top cutaway view, while the assembled shells are shown in the bottom view (Multimedia view). (e) Very strong ɛ_RR, strong ɛ_GG, and moderate ɛ_RG lead to two-step assembly around an almost pure G domain. The strength of the ɛ_RR interaction prevents the subunits from successfully encapsulating the globule of R cargo, while the G cargo is able to bud off similarly to (d) and form a properly assembled shell. The cutaway shows the state of the assembled shell and G cargo domain within as well as the R cargo globule that is unable to properly close (Multimedia view). Supplemental videos corresponding to each of the respective trajectories are included in the SI. Multimedia views: (a) https://doi.org/10.1063/5.0089556.1; (b) https://doi.org/10.1063/5.0089556.2; (c) https://doi.org/10.1063/5.0089556.3; (d) https://doi.org/10.1063/5.0089556.4; and (e) https://doi.org/10.1063/5.0089556.5

FIG. 4.

Effect of cargo–cargo affinities on encapsulation for one-step assembly pathways. (a)–(c) Characterization of the encapsulated cargo as a function of the G–G and R–R affinities, for fixed R–G affinity ɛ_RG = 1.3. (a) The fraction of R particles averaged over all shells f_R to characterize the average composition of encapsulated cargo. Snapshots show cutaway views of assembled shells, to illustrate the cargo morphology at corresponding symbols on the plot. (b) The fraction of unlike cargo particles in first solvation shells, f_UL, averaged over all shells. (c) The mean number of encapsulated cargo particles per shell, to indicate cargo loading efficiency. (d) The fraction of subunits in complete shells, revealing the effect of cargo–cargo affinities on shell assembly. (e) The shell-to-shell variability of cargo composition, σ_R,shell, defined as the standard deviation of f_R over shells, is shown as a function of the R–R and G–G affinities for three indicated values of ɛ_RG. High values of σ_R,shell ≳ 0.4 indicate that different shells have encapsulated pure domains of, respectively, R or G cargo species. To simplify the presentation, we only consider equal R–R and G–G affinities, ɛ_RR = ɛ_GG. For (a)–(e), other parameter values are ɛ_RG = 1.3, shell–cargo affinity ɛ_SC = 6.0, and shell–shell affinity ɛ_SS = 3.5.

FIG. 5.

Comparison of cargo encapsulation for one-step and two-step pathways. The fraction of R cargo in completed shells f_R (a) and the fraction of unlike cargo particles in first solvation shells f_UL (b) are shown as a function of ɛ_RR for the lower and higher shell–shell affinities ɛ_SS = 2.0 and ɛ_SS = 3.5. Other parameters are ɛ_GG = 1.7, ɛ_RG = 1.3, and ɛ_SC = 6.0. Histograms of cargo composition within individual shells to illustrate shell-to-shell variability, for the peak in panel (a) at ɛ_RR = ɛ_GG = 1.7 and ɛ_RG = 1.7, for (c) one-step and (d) two-step pathways.

FIG. 6.

Fraction of shell subunits in complete shells in the two-step assembly pathway regime, ɛ_SS = 2.0 and ɛ_RG = 1.3.

FIG. 7.

Effects of shell–cargo and shell–shell affinity on cargo composition reveal thermodynamic and kinetic influences on encapsulation. (a) The fraction of encapsulated R particles f_R as a function of shell–cargo affinity ɛ_SC, for shell–shell affinities ɛ_SS = 2.0, and ɛ_SS = 3.5. To assess the importance of dynamics on these results, f_R is also shown for the “equilibrium” simulations in which cargo can exchange between shell interiors and the bulk. Other parameters are ɛ_GG = 1.7 and ɛ_RR = ɛ_RG = 1.3. (b) Cargo composition f_R as a function of shell–shell affinity ɛ_SS, at a constant shell–cargo affinity of ɛ_SC = 6.0. Other parameters are ɛ_RR = 1.3, with ɛ_GG = 1.7 and ɛ_RG = 1.3. The “equilibrium” results are also shown, as a straight line since they do not depend on ɛ_SS.

FIG. 8.

Comparison of encapsulated cargo between equilibrium and dynamical simulations. (a) and (b) The fraction of R particles (a) and mixing (b) are shown as a function of R–R and G–G affinities. Other parameters are ɛ_RG = 1.3 and ɛ_SC = 6.0, as in Fig. 4. (c) and (d) Difference between cargo encapsulation in dynamical assembly simulations and equilibrium. The plots show the differences between the quantities f_R and f_UL for the dynamical simulations in Fig. 4 (with ɛ_SS = 3.5) and equilibrium simulations [panels (a) and (b) of this figure].