Abstract
The cytoplasm is a densely packed environment filled with macromolecules with hindered diffusion. Molecular simulation of the diffusion of biomolecules under such macromolecular crowding conditions requires the definition of a simulation cell with a cytoplasmic-like composition. This has been previously done for prokaryote cells (E. coli) but not for eukaryote cells such as yeast as a model organism. Yeast proteomics datasets vary widely in terms of cell growth conditions, the technique used to determine protein composition, the reported relative abundance of proteins, and the units in which abundances are reported. We determined that the gene ontology profiles of the most abundant proteins across these datasets are similar, but their abundances vary greatly. To overcome this problem, we chose five mass spectrometry proteomics datasets that fulfilled the following criteria: high internal consistency, consistency with published experimental data, and freedom from GFP-tagging artifacts. Using these datasets, the contents of a simulation cell containing a single 80S ribosome were defined, such that the macromolecular density and the mass ratio of ribosomal-to-cytoplasmic proteins were consistent with experiment and chosen datasets. Finally, multiple tRNAs were added, consistent with their experimentally-determined number in the yeast cell. The resulting composition can be readily used in molecular simulations representative of yeast cytoplasmic macromolecular crowding conditions to characterize a variety of phenomena, such as protein diffusion, protein-protein interactions and biological processes such as protein translation.
Highlights
The environment inside cells is densely packed, termed macromolecular crowding, the extent of which varies throughout the different growth and differentiation stages of the cell, as well as according to its type and volume (Nakano et al, 2014)
In order to define the protein composition of a eukaryote molecular simulation cell, the recently published unified yeast proteomics dataset was used (Ho et al, 2018). This covers 5,391 genes with a total protein mass per yeast cell of 2.7 × 1012 Da, which is in good agreement with the total protein mass of a yeast cell previously reported to be 3 × 1012 Da (Sasidharan et al, 2012). This proteomics dataset comprises data integrated from 21 different datasets, which vary in the type of growth medium used to culture cells, their growth phase and the technique used to measure protein abundances
The top 200 most abundant proteins were taken from each of the 21 datasets based on their mass and were found to account for ∼70% of the total cytoplasmic protein mass (Figure 1)
Summary
The environment inside cells is densely packed, termed macromolecular crowding, the extent of which varies throughout the different growth and differentiation stages of the cell, as well as according to its type and volume (Nakano et al, 2014). A typical cell has a macromolecular concentration in the range 100–450 g/L, with 5–40% of its volume being occupied by macromolecules (Feig et al, 2017). The space available for the free diffusion of metabolites and other macromolecules is greatly reduced, leading to what is known as an excluded volume effect. This reduces diffusion and favors more compact protein conformations and protein. Hindered diffusion due to macromolecular crowding, on the other hand, increases the probability of ligands being in the vicinity of their receptors in what is termed caging effect, which enhances reaction rates (Feig et al, 2017). The diffusion of tRNA complexes in the cytoplasmic environment is hindered by crowding, in turn affecting the rate of translation (Klumpp et al, 2013)
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