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Keywords:

  • Bioinformatics;
  • Diffusion collision;
  • Macromolecular crowding;
  • Minimal genome;
  • Minimalproteome;
  • Protein–protein interaction network

We constructed and simulated a “minimal proteome” model using Langevin dynamics. It contains 206 essential protein types that were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein–protein interactions in the network. Negatively charged proteins that tend to have larger sizes can provide a large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that “proper” populations of negatively and positively charged proteins are important for maintaining a protein–protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of Escherichia coli may have a larger protein–protein interaction network than that based on the lower organism Mycoplasma pneumoniae.