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

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          Abstract

          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

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          Much of the work that has been done to understand how cells work has involved studying parts of a cell in isolation. This is particularly true of studies that have examined the arrangement of atoms in large molecules with elaborate structures like proteins or DNA. However, cells are densely packed with many different molecules and there is little proof that proteins keep the same structures inside cells that they have when they are studied alone.

          To really understand how cells work, new ways to understand how molecules behave inside cells are needed. While this cannot be achieved directly, technology has now reached the stage where we can, to some extent, study living cells by recreating them virtually. Simulated cells can copy the atomic details of all the molecules in a cell and can estimate how different molecules might behave together.

          Yu et al. have now developed a computer simulation of part of a cell from the bacterium, Mycoplasma genitalium, one of the simplest forms of life on Earth. This model suggested new possible interactions between molecules inside cells that cannot currently be studied in real cells. The model shows that some proteins have a much less rigid structure in cells than they do in isolation, whilst others are able to work together more closely to carry out certain tasks. Finally, the model predicted that small molecules such as food, water and drugs would move more slowly through cells as they become stuck or trapped by larger molecules.

          These results could be particularly important in helping to improve drug design. Currently the simulations are limited, and can only model parts of simple cells for less than a thousandth of a second. However, in future it should be possible to recreate larger and more complex cells, including human cells, for longer periods of time. These could be used to better study human diseases and help to design new treatments. The ultimate goal is to simulate a whole cell in full detail by combining all the available experimental data.

          DOI: http://dx.doi.org/10.7554/eLife.19274.002

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          Brownian dynamics with hydrodynamic interactions

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            Stokesian Dynamics

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              Interaction between particles suspended in solutions of macromolecules

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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                01 November 2016
                2016
                : 5
                : e19274
                Affiliations
                [1 ]deptiTHES Research Group , RIKEN , Saitama, Japan
                [2 ]deptTheoretical Molecular Science Laboratory , RIKEN , Saitama, Japan
                [3 ]deptLaboratory for Biomolecular Function Simulation , RIKEN Quantitative Biology Center , Kobe, Japan
                [4 ]deptComputational Biophysics Research Team , RIKEN Advanced Institute for Computational Science , Kobe, Japan
                [5 ]deptDepartment of Biochemistry and Molecular Biology , Michigan State University , East Lansing, United States
                [6]DE Shaw Research , United States
                [7]DE Shaw Research , United States
                Author notes
                Author information
                http://orcid.org/0000-0001-9380-6422
                Article
                19274
                10.7554/eLife.19274
                5089862
                27801646
                7283339d-16aa-4331-9398-01b9465c7ecc
                © 2016, Yu et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 30 June 2016
                : 28 September 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 GM092949
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: MCB 1330560
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: XSEDE TG-MCB090003
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001700, Ministry of Education, Culture, Sports, Science, and Technology;
                Award ID: High Performance Computing Infrastructure Strategic Program
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001700, Ministry of Education, Culture, Sports, Science, and Technology;
                Award ID: Innovative Drug Discovery Infrastructure through Functional Control of Biomolecular Systems
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001700, Ministry of Education, Culture, Sports, Science, and Technology;
                Award ID: Grant-in Aid 26119006
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001700, Ministry of Education, Culture, Sports, Science, and Technology;
                Award ID: Grant-in Aid 25410025
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002241, Japan Science and Technology Agency;
                Award ID: CREST
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100006264, RIKEN;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 GM084953
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Biophysics and Structural Biology
                Computational and Systems Biology
                Research Article
                Custom metadata
                2.5
                Crowding and metabolites in a simulated cellular environment alter protein conformations, modulate interactions of functionally related proteins, and lead to significant dynamic heterogeneity.

                Life sciences
                crowding effects,metabolite dynamics,whole-cell modeling,native state stability,diffusion,quinary interactions,none

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