Abstract
Biomolecules perform their various functions in living cells, namely in an environment that is crowded by many macromolecules. Thus, simulating the dynamics and interactions of biomolecules should take into account not only water and ions but also other binding partners, metabolites, lipids and macromolecules found in cells. In the last decade, research on how to model macromolecular crowders around proteins in order to simulate their dynamics in models of cellular environments has gained a lot of attention. In this mini-review we focus on the models of crowding agents that have been used in computer modeling studies of proteins and peptides, especially via molecular dynamics simulations.
Highlights
Intracellular organelles—in addition to water molecules, ions, metabolites, and other small solutes—typically contain between 200 and 400 g/L of macromolecules such as proteins, nucleic acids, ribosomes, and lipids
There are many reviews about the simulations of crowding, but we focus on the crowder models used in molecular
As a fast and simple solution, spherical crowders became popular and have been adopted in many types of simulations. They are often used in Brownian dynamics (BD) simulations (Cheung et al, 2005; Minh et al, 2006; Stagg et al, 2007; Wieczorek and Zielenkiewicz, 2008; Oh et al, 2014), but they can be used in other methods such as Models dynamics (MD) (Kim et al, 2014; Miller et al, 2016) and Monte Carlo simulations (Kim et al, 2010)
Summary
Intracellular organelles—in addition to water molecules, ions, metabolites, and other small solutes—typically contain between 200 and 400 g/L of macromolecules such as proteins, nucleic acids, ribosomes, and lipids These complex environments may impact biomolecular function in vivo via crowding and confinement. When biomolecules experience crowding, the available volume is decreased and interactions with other biomolecules are unavoidable This influences their diffusion and association pathways. Crowding has the most pronounced effects on proteins with intrinsically disordered fragments or those that undergo significant conformational transitions as part of their function, for example during ligand binding. This applies to a vast majority of proteins. Other reviews cover the overall effects of crowding (Zhou et al, 2008; Christiansen et al, 2013), models of cellular environments at different scales (Feig and Sugita, 2013; Im et al, 2016; Feig et al, 2017), diffusion (Długosz and Trylska, 2011), and protein-protein interactions (Bhattacharya et al, 2013) in crowded environments
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