Modeling Crowded Environment in Molecular Simulations

Ostrowska, Natalia; Feig, Michael; Trylska, Joanna

doi:10.3389/fmolb.2019.00086

Cited by 39 publications

(35 citation statements)

References 47 publications

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“…Besides such “hard” interactions (steric, entropic effects), “soft” interactions (transient, enthalpic effects) contribute significantly to the structural, dynamic, and thermodynamic properties of biomolecules in crowded environments as well [ 10 , 11 , 12 ]. Crowding and confinement of biomolecules in concentrated solutions have attracted considerable attention in recent years for unveiling the underlying behaviors and mechanisms via both experimental and computational approaches [ 7 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Atomistic Simulation of Lysozyme in Solutions Crowded by Tetraethylene Glycol: Force Field Dependence

Liu

Qiu

et al. 2022

Molecules

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The behavior of biomolecules in crowded environments remains largely unknown due to the accuracy of simulation models and the limited experimental data for comparison. Here we chose a small crowder of tetraethylene glycol (PEG-4) to investigate the self-crowding of PEG-4 solutions and molecular crowding effects on the structure and diffusion of lysozyme at varied concentrations from dilute water to pure PEG-4 liquid. Two Amber-like force fields of Amber14SB and a99SB-disp were examined with TIP3P (fast diffusivity and low viscosity) and a99SB-disp (slow diffusivity and high viscosity) water models, respectively. Compared to the Amber14SB protein simulations, the a99SB-disp model yields more coordinated water and less PEG-4 molecules, less intramolecular hydrogen bonds (HBs), more protein–water HBs, and less protein–PEG HBs as well as stronger interactions and more hydrophilic and less hydrophobic contacts with solvent molecules. The a99SB-disp model offers comparable protein–solvent interactions in concentrated PEG-4 solutions to that in pure water. The PEG-4 crowding leads to a slow-down in the diffusivity of water, PEG-4, and protein, and the decline in the diffusion from atomistic simulations is close to or faster than the hard sphere model that neglects attractive interactions. Despite these differences, the overall structure of lysozyme appears to be maintained well at different PEG-4 concentrations for both force fields, except a slightly large deviation at 370 K at low concentrations with the a99SB-disp model. This is mainly attributed to the strong intramolecular interactions of the protein in the Amber14SB force field and to the large viscosity of the a99SB-disp water model. The results indicate that the protein force fields and the viscosity of crowder solutions affect the simulation of biomolecules under crowding conditions.

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Section: Introductionmentioning

confidence: 99%

Atomistic Simulation of Lysozyme in Solutions Crowded by Tetraethylene Glycol: Force Field Dependence

Liu

Qiu

et al. 2022

Molecules

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“…In living organisms, the ideal diluted and well mixed lab conditions are not present, which indeed are wrong thoughtful [7]. Instead of that, there exist systems highly concentrated, not well mixed and highly tortuous, this could be a real definition of what should be thought about molecular crowding [8,9]. Therein exist water molecules, ions, metabolites, proteins, nucleic acids, lipids, carbohydrates, etc.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the crowding effect is noticeably in protein fragments possessing intrinsic disorder or moieties with high conformational variation due to function, e.g. mobility in ligand clefts, chaperones and transporter/motor proteins such as kinesins, and due to their own existence, this applies to the majority of proteins [9,14]. Moreover, protein-crowded systems are directly responsible of further selectivity and even specificity due to the enhancement of deeper thermodynamic holes in the potential energy surface [15].…”

Section: Introductionmentioning

confidence: 99%

Improving Coarse Grain Models of Protein Folding through Weighting of Polar-polar/hydrophobic-hydrophobic Interactions into Crowded Spaces

Ppg

2021

Preprint

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In this piece of work were tested 7 Hydrophobic-Polar sequences in two types of 2D-square space lattices, homogeneous and correlated, the latter simulating molecular crowding included as a geometric boundary restriction. The optimization of the 2D structures was carried out using a variant of Dill's model, inspired by the convex function, which takes into account both the hydrophobic (Dill’s model) and polar interactions, aimed to include more structural information to reach better folding solutions. While using correlated networks, the degrees of freedom in the folding of sequences were limited, and as a result in all cases more successful structural trials were found in comparison to the homogeneous lattice. In particular, the S5 sequence turned out to be the most difficult sequence of the seven folded, this perhaps due to the intrinsic i) degrees of freedom and ii) motifs of the expected 2D HP structure. Regarding S2 and S6 sequences, although optimal folding was not achieved for neither of the two approaches, folding with correlated network approach not only produced better results than homogeneous space, but for both sequences the best values found with crowding were very close to the expected optimal fitness. The sequences S1-S4 and S6 were better folded with medium lattice units for the correlated media, instead, S5 and S7 were better folded with a bit larger degree of lattice unit, revealing that depending on the degrees of freedom and particular folding motifs in each sequence would require particular crowding to achieve better folding. Finally, we claim that in all folded sequences in crowded spaces achieve better results than homogeneous ones.

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“…Nowadays, increases in computing power mean that large protein complexes comprising multiple subunits and/or ligands and protein-membrane systems can be simulated [9]. There is also increasing interest in simulating proteins in crowded conditions reminiscent of the interior of a cell [10,11]. While these developments are exciting, the trade-off between system size, the temporal and spatial scale of the motions of interest, and the computational effort required remains.…”

Section: Introductionmentioning

confidence: 99%

Computational methods for exploring protein conformations

Allison

2020

Biochemical Society Transactions

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Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.

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Modeling Crowded Environment in Molecular Simulations

Cited by 39 publications

References 47 publications

Atomistic Simulation of Lysozyme in Solutions Crowded by Tetraethylene Glycol: Force Field Dependence

Atomistic Simulation of Lysozyme in Solutions Crowded by Tetraethylene Glycol: Force Field Dependence

Improving Coarse Grain Models of Protein Folding through Weighting of Polar-polar/hydrophobic-hydrophobic Interactions into Crowded Spaces

Computational methods for exploring protein conformations

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