The Role of Packaging Sites in Efficient and Specific Virus Assembly

Abstract

During the life cycle of many single-stranded RNA viruses, including many human pathogens, a protein shell called the capsid spontaneously assembles around the viral genome. Understanding the mechanisms by which capsid proteins selectively assemble around the viral RNA amidst diverse host RNAs is a key question in virology. In one proposed mechanism, short sequences (packaging sites) within the genomic RNA promote rapid and efficient assembly through specific interactions with the capsid proteins. In this work, we develop a coarse-grained particle-based computational model for capsid proteins and RNA that represents protein-RNA interactions arising both from nonspecific electrostatics and from specific packaging site interactions. Using Brownian dynamics simulations, we explore how the efficiency and specificity of assembly depend on solution conditions (which control protein-protein and nonspecific protein-RNA interactions) and the strength and number of packaging sites. We identify distinct regions in parameter space in which packaging sites lead to highly specific assembly via different mechanisms and others in which packaging sites lead to kinetic traps. We relate these computational predictions to in vitro assays for specificity in which cognate viral RNAs compete against non-cognate RNAs for assembly by capsid proteins.

Keywords: RNA; computer simulation; modeling; self assembly; viral capsid.

FIG. 1

(A) Model schematic showing components responsible for subunit-subunit interactions: subunits are bound together by attractor pseudoatoms (‘A’), and the Top (‘T’) and Bottom (‘B’) pseudoatoms guide the subunits towards the correct geometry (see SI). (B) Schematic with components responsible for attractive interaction with the RNA (drawn in red) and packaging site (‘PS’): positively charged ARM (‘+’) and PS Receptor (‘PSR’). The ‘Excluder’ pseudoatoms, which represent the excluded volume of the capsid shell, are located within the black pentagons; to aid visibility, they are not explicitly drawn here. Snapshots here and throughout the article are colored as follows: blue=excluders, green=attractors, yellow=ARM, red=RNA, orange=PS.

FIG. 2

The effect of PSs and solution conditions on assembly yields and products. (A,B) The most prevalent assembly product is shown as a function of ionic strength (C_salt) and subunit-subunit attraction well-depth (ε_ss) for assembly around (A) a non-cognate RNA (polyelectrolyte without PS), and (B) a cognate RNA with 1 high affinity (HA, ε_PS = 20K_BT) PS and 25 low affinity (LA, ε_PS = 5K_BT) PSs. A legend showing the outcome and a representative simulation snapshot corresponding to each symbol is presented in (C). (D,E) The yield of well-formed capsids assembled around (D) the non-cognate RNA or (E) the cognate RNA with the PS sequence as in (B). In each simulation the RNA length corresponds to the thermodynamic optimal length for the non-cognate at the simulated value of C_salt, and ranges from 350 to 575 RNA segments (see Fig. S7).

FIG. 3

Selectivity for RNA containing 1 HA PS + 25 LA PS competing against a non-cognate RNA at equal concentrations r_ex = 1, (A) estimated from the data in Fig. 2 using Eq. 1 and (B) measured in direct competition simulations. (C) Selectivity for RNA containing 1 HA PS + 25 LA PS competing against excess non-cognate RNA, r_ex = 10. As in Fig 2, in each simulation the optimal RNA length is used based on the results in Fig. S7. In the explicit competition simulations of (B) the concentration of subunits is the same as used in the assembly simulations (Fig. 2). Error bars indicate 95% confidence intervals calculated using bootstrapping.

FIG. 4

Yield as a function of number of PS, N_PS, at low (A) and high (B) salt concentrations. Note that for these parameters yield is zero in the absence of PSs. PSs are either all low affinity (LA) ( ), all high affinity (HA) ( symbols), or the Combo sequence with 1 HA and N_PS LA PSs( symbols). For these cases, the HA PS is placed in the center of the RNA. Results from sets of simulations with the HA PS placed in the terminal position are shown as symbols. The result from simulations with the PS binding site placed in the center of the subunits is shown as a symbol. Note that there are 20 PS binding sites in a complete capsid, so N_PS = 20 is the stoichiometric value. Snapshots illustrate the trend in dominant outcomes with increasing PS number. Error bars indicate 95% confidence intervals calculated using bootstrapping.

FIG. 5

The assembly pathway order parameter n_free measured from simulations for (left) the non-cognate RNA and (center) the cognate RNA with the Combo PS sequence, N_PS = 25. (Right) The change in n_free due to PSs.

FIG. 6

Snapshots from typical assembly trajectories without and with PSs (the cognate RNA here is the combo sequence with 1 HA and 25 LA PSs) for low and high salt concentrations. PSs are depicted as orange spheres with exaggerated size to improve visibility.

FIG. 7

(A–F) Radius of hydration R_H as a function of simulation time steps for assembly trajectories performed at indicated parameter values, for non-cognate RNA ( symbols) and cognate RNA ( symbols). The R_H values before subunits are introduced are shown as ▲ symbols. The subunit-subunit interaction energy ε_ss increases from left to right. In the top row (A–C), the subunit ARM charge is (+5), and the RNA length is 575 segments; in the second row (D–F), the subunit ARM charge is (+10), and the RNA length is 910 segments. (G) Snapshots from simulations corresponding to panel (E), with non-cognate RNA on the left and cognate RNA on the right.

FIG. 8

(A) Path duration during a dynamic trajectory for RNAs within a preassembled capsid. (B) Schematic representation of RNA path within the capsid at intervals of 5 × 10⁷ timesteps. The line indicates the RNA path, with line color and width changing gradually with contour length for clarity. (C) Snapshots and schematics indicating non-optimal RNA paths which lead to stalled assemblies. PSs are shown with exaggerated size to improve visibility. Segments of interest are shown in green.