Recent Progress in Modeling and Simulation of Biomolecular Crowding and Condensation Inside Cells

Abstract

Macromolecular crowding in the cellular cytoplasm can potentially impact diffusion rates of proteins, their intrinsic structural stability, binding of proteins to their corresponding partners as well as biomolecular organization and phase separation. While such intracellular crowding can have a large impact on biomolecular structure and function, the molecular mechanisms and driving forces that determine the effect of crowding on dynamics and conformations of macromolecules are so far not well understood. At a molecular level, computational methods can provide a unique lens to investigate the effect of macromolecular crowding on biomolecular behavior, providing us with a resolution that is challenging to reach with experimental techniques alone. In this review, we focus on the various physics-based and data-driven computational methods developed in the past few years to investigate macromolecular crowding and intracellular protein condensation. We review recent progress in modeling and simulation of biomolecular systems of varying sizes, ranging from single protein molecules to the entire cellular cytoplasm. We further discuss the effects of macromolecular crowding on different phenomena, such as diffusion, protein-ligand binding, and mechanical and viscoelastic properties, such as surface tension of condensates. Finally, we discuss some of the outstanding challenges that we anticipate the community addressing in the next few years in order to investigate biological phenomena in model cellular environments by reproducing in vivo conditions as accurately as possible.

Keywords: Brownian dynamics simulations; cellular cytoplasm; macromolecular crowding; molecular dynamics simulations; phase separation; protein condensation.

Figure 1

Macromolecular crowding within cells dictates various cellular functions, including protein diffusion, protein–protein interactions, protein–ligand association/dissociation, and protein phase separation or condensation. This review explores the molecular modeling and simulation methods developed in past few years to study the effects of macromolecular crowding across various spatiotemporal scales. These methods range from modeling the effect of crowding on protein multimers, understanding the molecular mechanism of protein condensate formation (a) to simulating the cytoplasms of multiple organisms (b) and developing structural models of entire cells (c).

Figure 2

Simulation time as a function of the system size (a), statistics of methods (b) and protein crowders or systems (c) used in recent simulations employed to study crowded and cell-like environments. Data obtained from the studies reported in Table 1. (a) The graph shows the log of the number of crowders used in simulations versus the total time of simulations. Smaller systems are typically simulated for longer time scales due to computational costs. AA-MD: all-atom molecular dynamics; CG-MD: coarse-grained molecular dynamics; BD: Brownian dynamics. (b) Statistics of methods used by works discussed in the review in percentage: Monte Carlo (MC), Brownian dynamics (BD), coarse-grained molecular dynamics (CG-MD) and all-atom molecular dynamics (AA-MD) simulations. (c) Statistics of the usage of different types of crowders or systems discussed in the review in percentage. BSA: bovine serum albumin, PEG: polyethylene glycol.

Figure 3

Properties investigated in simulations of crowded environments (a), cytoplasms and biomolecular condensates (b). (a) The excluded volume and quinary interactions reduce the diffusion rates of proteins in the cytoplasm. Binding of ligands to their target proteins is facilitated as the effective concentration of the ligand increases in crowded conditions. Similarly, protein structures are more stable and compact as the volume available for unfolding of proteins reduces in crowded conditions. (b) Phase separation of cellular materials to form biomolecular condensates can be facilitated by multiple physical parameters, ranging from the nature of the amino-acid sequence of biomolecules to the pH and cooperative electrostatic interactions. This in turn regulates their phase separation propensities, mechanical behavior as well as viscoelastic properties.