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Rotational and Translational Diffusion of Proteins as a Function of Concentration
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-5522-1810
University of Science and Technology Beijing, Peoples R China.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
2019 (English)In: ACS OMEGA, E-ISSN 2470-1343, Vol. 4, no 24, p. 20654-20664Article in journal (Refereed) Published
Abstract [en]

Atomistic simulations of three different proteins at different concentrations are performed to obtain insight into protein mobility as a function of protein concentration. We report on simulations of proteins from diluted to the physiological water concentration (about 70% of the mass). First, the viscosity was computed and found to increase by a factor of 7-9 going from pure water to the highest protein concentration, in excellent agreement with in vivo nuclear magnetic resonance results. At a physiological concentration of proteins, the translational diffusion is found to be slowed down to about 30% of the in vitro values. The slow-down of diffusion found here using atomistic models is slightly more than that of a hard sphere model that neglects the electrostatic interactions. Interestingly, rotational diffusion of proteins is slowed down somewhat more (by about 80-95% compared to in vitro values) than translational diffusion, in line with experimental findings and consistent with the increased viscosity. The finding that rotation is retarded more than translation is attributed to solvent-separated clustering. No direct interactions between the proteins are found, and the clustering can likely be attributed to dispersion interactions that are stronger between proteins than between protein and water. Based on these simulations, we can also conclude that the internal dynamics of the proteins in our study are affected only marginally under crowding conditions, and the proteins become somewhat more stable at higher concentrations. Simulations were performed using a force field that was tuned for dealing with crowding conditions by strengthening the protein-water interactions. This force field seems to lead to a reproducible partial unfolding of an alpha-helix in one of the proteins, an effect that was not observed in the unmodified force field.

Place, publisher, year, edition, pages
2019. Vol. 4, no 24, p. 20654-20664
National Category
Biophysics
Identifiers
URN: urn:nbn:se:uu:diva-395115DOI: 10.1021/acsomega.9b02835ISI: 000502130800028PubMedID: 31858051OAI: oai:DiVA.org:uu-395115DiVA, id: diva2:1360413
Funder
Swedish Research Council, 2013-5947Swedish National Infrastructure for Computing (SNIC), SNIC2017-12-41Available from: 2019-10-12 Created: 2019-10-12 Last updated: 2020-01-23Bibliographically approved
In thesis
1. Effect of Macromolecular Crowding on Diffusive Processes
Open this publication in new window or tab >>Effect of Macromolecular Crowding on Diffusive Processes
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Macromolecular crowding are innate to cellular environment. Understanding their effect on cellular components and processes is essential. This is often neglected in dilute experimental setup both in vitro and in silico.

In this thesis I have dealt with challenges in biomolecular simulations at two levels of modeling, Brownian Dynamics (BD) and Molecular Dynamics (MD).

Conventional BD simulations become inefficient since most of the computational time is spent propagating the particles towards each other before any reaction takes place. Event-driven algorithms have proven to be several orders of magnitude faster than conventional BD algorithms. However, the presence of diffusion-limited reactions in biochemical networks lead to multiple rebindings in case of a reversible reaction which deteriorates the efficiency of these types of algorithms. In this thesis, I modeled a reversible reaction coupled with diffusion in order to incorporate multiple rebindings. I implemented a Green's Function Reaction Dynamics (GFRD) algorithm by using the analytical solution of the reversible reaction diffusion equation. I show that the algorithm performance is independent of the number of rebindings.

Nevertheless, the gain in computational power still deteriorates when it comes to the simulation of crowded systems. However, given the effects of macromolecular crowding on diffusion coefficient and kinetic parameters are known, one can implicitly incorporate the effect of crowding into coarse-grain algorithms by choosing right parameters. Therefore, understanding the effect of crowding at atomistic resolution would be beneficial.

I studied the effect of high concentration of macromolecules on diffusive properties at atomistic level with MD simulations. The findings emphasize the effect of chemical interactions at atomistic level on mobility of macromolecules.

Simulating macromolecules in high concentration raised challenges for atomistic physical models. Current force fields lead to aggregation of proteins at high concentration. I probed scenarios based on weakening and strengthening protein-protein and protein-water interactions, respectively. Furthermore, I built a cytoplasmic model at atomistic level based on the data available on Escherichia coli cytoplasm. This model was simulated in time and space by MD simulation package, GROMACS. Through this model, it is possible to study structural and dynamical properties under cellular like environment at physiological concentration.

Place, publisher, year, edition, pages
Uppsala: Uppsala University, 2019. p. 50
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1871
National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-395119 (URN)978-91-513-0785-5 (ISBN)
Public defence
2019-12-09, BMC: A1:111a, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2019-11-18 Created: 2019-10-14 Last updated: 2019-11-18

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