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

Yu, Isseki; Mori, Takaharu; Ando, Tadashi; Harada, Ryuhei; Jung, Jaewoon; Sugita, Yuji; Feig, Michael

doi:10.7554/elife.19274

Cited by 284 publications

(342 citation statements)

References 57 publications

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“…MD-based techniques can incorporate multiple experimental observables as external forces during the simulation thus obtaining atomistic details of protein structure and dynamics. Notable examples of large complexes that were determined by computer-guided methods and experimental measurements include structures of the ribosome, the proteasome, virus capsids, the cytoplasm and the chemosensory array 30–35 .…”

Section: Introductionmentioning

confidence: 99%

CryoEM Structure Refinement by Integrating NMR Chemical Shifts with Molecular Dynamics Simulations

Perilla

Zhao

et al. 2017

J. Phys. Chem. B

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Single particle cryoEM has emerged as a powerful method for structure determination of proteins and complexes, complementing X-ray crystallography and NMR spectroscopy. Yet, for many systems, the resolution of cryoEM density map has been limited to 4–6 Å, which only allows for resolving bulky amino acids side chains, thus hindering accurate model building from the density map. On the other hand, experimental chemical shifts (CS) from solution and solid state MAS NMR spectra provide atomic level data for each amino acid within a molecule or a complex; however, structure determination of large complexes and assemblies based on NMR data alone remains challenging. Here we present a novel integrated strategy to combine the highly complementary experimental data from cryoEM and NMR computationally by molecular dynamics simulations to derive an atomistic model, which is not attainable by either approach alone. We use the HIV-1 capsid protein (CA) C-terminal domain as well as the large capsid assembly to demonstrate the feasibility of this approach, termed NMR CS-biased cryoEM structure refinement.

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Section: Introductionmentioning

confidence: 99%

CryoEM Structure Refinement by Integrating NMR Chemical Shifts with Molecular Dynamics Simulations

Perilla

Zhao

et al. 2017

J. Phys. Chem. B

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“…In spite of this general picture, most computer simulations have investigated the stability of and binding between compact proteins in cellmimicking environments composed of passively diffusing and inert macromolecules (Cheung et al 2005;Minh et al 2006;Ridgway et al 2008;Wieczorek and Zielenkiewicz 2008;Wojciechowski and Cieplak 2008;Mittal and Best 2010;Wojciechowski et al 2010;Wang and Cheung 2012;Denesyuk and Thirumalai 2013;Qi et al 2014;Naddaf and Sayyed-Ahmad 2014;Starzyk et al 2016;Yu et al 2016).…”

Section: Toward Realistic Molecular Simulations Of Cellular Eventsmentioning

confidence: 99%

“…Driven by high-throughput experiments, Yu et al (2016) built a very large system made of 103 million atoms to represent the flexible biomolecules in the cytoplasm of Mycoplasma genitalium (MG), ions, water, and other small molecules. Diffusion of the GFP was normal, similarly to the other molecules, and compared well with the experimentally measured value of 7.7 μm 2 /s (Elowitz et al 1999;Mullineaux 2016).…”

Section: Computational Models Of Diffusion In the Cytoplasmmentioning

confidence: 99%

Molecular simulations of cellular processes

Trovato

Fumagalli

2017

Biophys Rev

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It is, nowadays, possible to simulate biological processes in conditions that mimic the different cellular compartments. Several groups have performed these calculations using molecular models that vary in performance and accuracy. In many cases, the atomistic degrees of freedom have been eliminated, sacrificing both structural complexity and chemical specificity to be able to explore slow processes. In this review, we will discuss the insights gained from computer simulations on macromolecule diffusion, nuclear body formation, and processes involving the genetic material inside cell-mimicking spaces. We will also discuss the challenges to generate new models suitable for the simulations of biological processes on a cell scale and for cellcycle-long times, including non-equilibrium events such as the co-translational folding, misfolding, and aggregation of proteins. A prominent role will be played by the wise choice of the structural simplifications and, simultaneously, of a relatively complex energetic description. These challenging tasks will rely on the integration of experimental and computational methods, achieved through the application of efficient algorithms.

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“…Their simulated system contained 1000 macromolecules of 50 types (proteins and RNAs), with a composition reflecting that in the cytoplasm of Escherichia Coli bacteria. Recently, Feig et al presented a detailed and extensive model for another bacterial cytoplasm, that of Mycoplasma genitalium, which may serve as a starting point for simulations [63,64]. The model includes proteins, RNAs, protein/RNA complexes, metabolites, ions as well as explicit solvent molecules.…”

Section: Crowding Environmentsmentioning

confidence: 99%

Protein folding/unfolding in the presence of interacting macromolecular crowders

Irbäck

Mohanty

2017

Eur. Phys. J. Spec. Top.

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Abstract. Recent years have seen an increasing number of biophysical studies of proteins being conducted in cells and concentrated protein solutions. In these experiments, compared to dilute-solution data, both stabilization and destabilization of globular proteins have been observed, which cannot be explained in terms of volume exclusion alone. For a fundamental understanding of the observed effects, there is a need for computational modeling beyond the level of hard-sphere crowders. This mini-review discusses recent efforts to simulate folding/unfolding properties of proteins in the presence of explicit macromolecular crowders. A Monte Carlo-based approach by us is described, which we recently applied to study the equilibrium folding thermodynamics of two peptides in the presence of explicit protein crowders.

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

Cited by 284 publications

References 57 publications

CryoEM Structure Refinement by Integrating NMR Chemical Shifts with Molecular Dynamics Simulations

CryoEM Structure Refinement by Integrating NMR Chemical Shifts with Molecular Dynamics Simulations

Molecular simulations of cellular processes

Protein folding/unfolding in the presence of interacting macromolecular crowders

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