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Impact of Dispersion Coefficient on Simulations of Proteins and Organic Liquids
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.ORCID iD: 0000-0002-7659-8526
2018 (English)In: Journal of Physical Chemistry B, ISSN 1520-6106, E-ISSN 1520-5207, Vol. 122, no 33, p. 8018-8027Article in journal (Refereed) Published
Abstract [en]

In the context of studies of proteins under crowding conditions, it was found that there is a tendency of simulated proteins to coagulate in a seemingly unphysical manner. This points to an imbalance in the protein-protein or protein-water interactions. One way to resolve this is to strengthen the protein-water Lennard-Jones interactions. However, it has also been suggested that dispersion interactions may have been systematically overestimated in force fields due to parameterization with a short cutoff. Here, we test this proposition by performing simulations of liquids and of proteins in solution with systematically reduced C-6 (dispersion constant in a 12-6 Lennard-Jones potential) and evaluate the properties. We find that simulations of liquids with either a dispersion correction or explicit long-range Lennard-Jones interactions need little or no correction to the dispersion constant to reproduce the experimental density. For simulations of proteins, a significant reduction in the dispersion constant is needed to reduce the coagulation, however. Because the protein- and liquid force fields share atom types, at least to some extent, another solution for the coagulation problem may be needed, either through including explicit polarization or through strengthening protein-water interactions.

Place, publisher, year, edition, pages
2018. Vol. 122, no 33, p. 8018-8027
National Category
Physical Chemistry Biophysics
Identifiers
URN: urn:nbn:se:uu:diva-364048DOI: 10.1021/acs.jpcb.8b05770ISI: 000442959900008PubMedID: 30084244OAI: oai:DiVA.org:uu-364048DiVA, id: diva2:1269201
Available from: 2018-12-10 Created: 2018-12-10 Last updated: 2019-10-14Bibliographically 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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Bashardanesh, Zahedehvan der Spoel, David

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