Diffusive Dynamics of Bacterial Proteome as a Proxy of Cell Death

Abstract

Temperature variations have a big impact on bacterial metabolism and death, yet an exhaustive molecular picture of these processes is still missing. For instance, whether thermal death is determined by the deterioration of the whole or a specific part of the proteome is hotly debated. Here, by monitoring the proteome dynamics of E. coli, we clearly show that only a minor fraction of the proteome unfolds at the cell death. First, we prove that the dynamical state of the E. coli proteome is an excellent proxy for temperature-dependent bacterial metabolism and death. The proteome diffusive dynamics peaks at about the bacterial optimal growth temperature, then a dramatic dynamical slowdown is observed that starts just below the cell's death temperature. Next, we show that this slowdown is caused by the unfolding of just a small fraction of proteins that establish an entangling interprotein network, dominated by hydrophobic interactions, across the cytoplasm. Finally, the deduced progress of the proteome unfolding and its diffusive dynamics are both key to correctly reproduce the E. coli growth rate.

Optimized OPEP Force Field for Simulation of Crowded Protein Solutions

“…In Figure 6A, we report the data from the previous simulation of the same model but based on the earlier version of OPEP and using the 1.8/2.1 scaling factor. 34 The computed diffusion coefficients already show a good agreement with the model 42 derived from experimental data; see the solid line in Figure 6A. In Figure 6B, we report the results obtained with OPEPv7, which also works very well, reproducing the experimental trend.…”

“…To improve the description of crowded systems with the OPEP force field, we benchmarked its predictions of translational diffusion coefficients in homogeneous solutions of bovine serum albumin (BSA) and binary solutions of chymotrypsin inhibitor 2 (CI2) against neutron-scattering (NS) and nuclear magnetic resonance (NMR) data, respectively, obtained for these solutions. , We found that choosing a smaller empirical scaling factor removed the gelation issue and improved the behavior of the protein solutions. Namely, the 1.8/2.1 scaling was used to model the E. coli cytoplasm, and the recovered diffusivities of the proteins were found in good agreement with the experimental distribution of the diffusion coefficients, as a function of protein molecular mass in E. coli ; see ref . However, when considering our benchmark systems no single scaling factor ensured the perfect agreement with experimental data for all the model systems (see Figures S3 and S4 in the Supporting Information).…”

“… 32 , 33 We found that choosing a smaller empirical scaling factor removed the gelation issue and improved the behavior of the protein solutions. Namely, the 1.8/2.1 scaling was used to model the E. coli cytoplasm, and the recovered diffusivities of the proteins were found in good agreement with the experimental distribution of the diffusion coefficients, as a function of protein molecular mass in E. coli ; see ref ( 34 ). However, when considering our benchmark systems no single scaling factor ensured the perfect agreement with experimental data for all the model systems (see Figures S3 and S4 in the Supporting Information).…”

“…The diffusion coefficient was estimated by fitting the mean square displacement in the 30–50 ns time window. In Figure A, we report the data from the previous simulation of the same model but based on the earlier version of OPEP and using the 1.8/2.1 scaling factor . The computed diffusion coefficients already show a good agreement with the model derived from experimental data; see the solid line in Figure A.…”

“…The model is composed of 197 proteins belonging to 35 different protein families, and simulated in a cubic box of 400 Å at a solution concentration of 279 g/L. Details on the system preparation are given in ref ( 34 ). The system was evolved for 1 μs using LBMD.…”