Isseki Yu

Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm

Biological macromolecules function in highly crowded cellular environments. The structure and dynamics of proteins and nucleic acids are well characterized in vitro, but in vivo crowding effects remain unclear. Using molecular dynamics simulations of a comprehensive atomistic model cytoplasm we found that protein-protein interactions may destabilize native protein structures, whereas metabolite interactions may induce more compact states due to electrostatic screening. Protein-protein interactions also resulted in significant variations in reduced macromolecular diffusion under crowded conditions, while metabolites exhibited significant two-dimensional surface diffusion and altered protein-ligand binding that may reduce the effective concentration of metabolites and ligands in vivo. Metabolic enzymes showed weak non-specific association in cellular environments attributed to solvation and entropic effects. These effects are expected to have broad implications for the in vivo functioning of biomolecules. This work is a first step towards physically realistic in silico whole-cell models that connect molecular with cellular biology. DOI: http://dx.doi.org/10.7554/eLife.19274.001

Complete atomistic model of a bacterial cytoplasm for integrating physics, biochemistry, and systems biology.

A model for the cytoplasm of Mycoplasma genitalium is presented that integrates data from a variety of sources into a physically and biochemically consistent model. Based on gene annotations, core genes expected to be present in the cytoplasm were determined and a metabolic reaction network was reconstructed. The set of cytoplasmic genes and metabolites from the predicted reactions were assembled into a comprehensive atomistic model consisting of proteins with predicted structures, RNA, protein/RNA complexes, metabolites, ions, and solvent. The resulting model bridges between atomistic and cellular scales, between physical and biochemical aspects, and between structural and systems views of cellular systems and is meant as a starting point for a variety of simulation studies.

Crowding in Cellular Environments at an Atomistic Level from Computer Simulations

The effects of crowding in biological environments on biomolecular structure, dynamics, and function remain not well understood. Computer simulations of atomistic models of concentrated peptide and protein systems at different levels of complexity are beginning to provide new insights. Crowding, weak interactions with other macromolecules and metabolites, and altered solvent properties within cellular environments appear to remodel the energy landscape of peptides and proteins in significant ways including the possibility of native state destabilization. Crowding is also seen to affect dynamic properties, both conformational dynamics and diffusional properties of macromolecules. Recent simulations that address these questions are reviewed here and discussed in the context of relevant experiments.

Influence of hydrostatic pressure on dynamics and spatial distribution of protein partial molar volume: time-resolved surficial Kirkwood-Buff approach.

The influence of hydrostatic pressure on the partial molar volume (PMV) of the protein apomyoglobin (AMb) was investigated by all-atom molecular dynamics (MD) simulations. Using the time-resolved Kirkwood-Buff (KB) approach, the dynamic behavior of the PMV was identified. The simulated time average value of the PMV and its reduction by 3000 bar pressurization correlated with experimental data. In addition, with the aid of the surficial KB integral method, we obtained the spatial distributions of the components of PMV to elucidate the detailed mechanism of the PMV reduction. New R-dependent PMV profiles identified the regions that increase or decrease the PMV under the high pressure condition. The results indicate that besides the hydration in the vicinity of the protein surface, the outer space of the first hydration layer also significantly influences the total PMV change. These results provide a direct and detailed picture of pressure induced PMV reduction.

Intrinsic Alterations in the Partial Molar Volume on the Protein Denaturation: Surficial Kirkwood−Buff Approach

The partial molar volume (PMV) of the protein chymotrypsin inhibitor 2 (CI2) was calculated by all-atom MD simulation. Denatured CI2 showed almost the same average PMV value as that of native CI2. This is consistent with the phenomenological question of the protein volume paradox. Furthermore, using the surficial Kirkwood-Buff approach, spatial distributions of PMV were analyzed as a function of the distance from the CI2 surface. The profiles of the new R-dependent PMV indicate that, in denatured CI2, the reduction in the solvent electrostatic interaction volume is canceled out mainly by an increment in thermal volume in the vicinity of its surface. In addition, the PMV of the denatured CI2 was found to increase in the region in which the number density of water atoms is minimum. These results provide a direct and detailed picture of the mechanism of the protein volume paradox suggested by Chalikian et al.

Spatio-temporal characteristics of the transfer free energy of apomyoglobin into the molecular crowding condition with trimethylamine N-oxide: a study with three types of the Kirkwood-Buff integral.

The transfer free energy (TFE) of apomyoglobin (AMb) from pure water into aqueous solution with trimethylamine N-oxide (TMAO) was investigated by all-atom molecular dynamics (MD) simulation combined with the Kirkwood-Buff (KB) integral method. The simulated TFE and the preferential interaction parameter correlated favorably with experimental values. In addition, the time-resolved KB integral revealed that a significant fluctuation in the TFE arose from the alteration in TMAO solvation around AMb. Furthermore, spatial decomposition of the KB integrals revealed how the local elements of the TFE are spatially distributed around AMb. These results revealed the spatio-temporal characteristics of the protein TFE into the molecular crowding condition with TMAO.

Slow-Down in Diffusion in Crowded Protein Solutions Correlates with Transient Cluster Formation

For a long time, the effect of a crowded cellular environment on protein dynamics has been largely ignored. Recent experiments indicate that proteins diffuse more slowly in a living cell than in a diluted solution, and further studies suggest that the diffusion depends on the local surroundings. Here, detailed insight into how diffusion depends on protein-protein contacts is presented based on extensive all-atom molecular dynamics simulations of concentrated villin headpiece solutions. After force field adjustments in the form of increased protein-water interactions to reproduce experimental data, translational and rotational diffusion was analyzed in detail. Although internal protein dynamics remained largely unaltered, rotational diffusion was found to slow down more significantly than translational diffusion as the protein concentration increased. The decrease in diffusion is interpreted in terms of a transient formation of protein clusters. These clusters persist on sub-microsecond time scales and follow distributions that increasingly shift toward larger cluster size with increasing protein concentrations. Weighting diffusion coefficients estimated for different clusters extracted from the simulations with the distribution of clusters largely reproduces the overall observed diffusion rates, suggesting that transient cluster formation is a primary cause for a slow-down in diffusion upon crowding with other proteins.

High-Performance Data Analysis on the Big Trajectory Data of Cellular Scale All-atom Molecular Dynamics Simulations

The inside of a cell is highly crowded with a large number of macromolecules together with solvents and metabolites. To know the molecular-level behaviour of biomolecules in such dense crowding environment, we constructed full atomistic model of the cytoplasm of bacteria, and performed massive all-atom molecular dynamics (MD) simulations. On the other hand, to analyse such big MD data, we need significant computational power and efficient calculation methodology. Here, we introduce what and how we analyse the biomolecule properties from the big trajectory data produced by cellular scale all-atom MD simulations.

Challenges and opportunities in connecting simulations with experiments via molecular dynamics of cellular environments

Computer simulations are widely used to study molecular systems, especially in biology. As simulations have greatly increased in scale reaching cellular levels there are now significant challenges in managing, analyzing, and interpreting such data in comparison with experiments that are being discussed. Management challenges revolve around storing and sharing terabyte to petabyte scale data sets whereas the analysis of simulations of highly complex systems will increasingly require automated machine learning and artificial intelligence approaches. The comparison between simulations and experiments is furthermore complicated not just by the complexity of the data but also by difficulties in interpreting experiments for highly heterogeneous systems. As an example, the interpretation of NMR relaxation measurements and comparison with simulations for highly crowded systems is discussed.