Molecular Dynamics Simulation for All Hollingsworth, Scott A.; Dror, Ron O.
Neuron (Cambridge, Mass.),
09/2018, Letnik:
99, Številka:
6
Journal Article
Recenzirano
Odprti dostop
The impact of molecular dynamics (MD) simulations in molecular biology and drug discovery has expanded dramatically in recent years. These simulations capture the behavior of proteins and other ...biomolecules in full atomic detail and at very fine temporal resolution. Major improvements in simulation speed, accuracy, and accessibility, together with the proliferation of experimental structural data, have increased the appeal of biomolecular simulation to experimentalists—a trend particularly noticeable in, although certainly not limited to, neuroscience. Simulations have proven valuable in deciphering functional mechanisms of proteins and other biomolecules, in uncovering the structural basis for disease, and in the design and optimization of small molecules, peptides, and proteins. Here we describe, in practical terms, the types of information MD simulations can provide and the ways in which they typically motivate further experimental work.
Hollingsworth and Dror review modern molecular dynamics (MD) simulations, with an emphasis on how such simulations complement wet-lab experiments. MD simulations capture biomolecular motion in atomic detail and have come into widespread use because of recent technological and scientific advances.
Molecular dynamics (MD) simulation is a valuable tool for characterizing the structural dynamics of folded proteins and should be similarly applicable to disordered proteins and proteins with both ...folded and disordered regions. It has been unclear, however, whether any physical model (force field) used in MD simulations accurately describes both folded and disordered proteins. Here, we select a benchmark set of 21 systems, including folded and disordered proteins, simulate these systems with six state-of-theart force fields, and compare the results to over 9,000 available experimental data points. We find that none of the tested force fields simultaneously provided accurate descriptions of folded proteins, of the dimensions of disordered proteins, and of the secondary structure propensities of disordered proteins. Guided by simulation results on a subset of our benchmark, however, we modified parameters of one force field, achieving excellent agreement with experiment for disordered proteins, while maintaining state-of-the-art accuracy for folded proteins. The resulting force field, a99SB-disp, should thus greatly expand the range of biological systems amenable to MD simulation. A similar approach could be taken to improve other force fields.
Numerical simulations and in-situ measurements represent two important and synergistic pillars for the study of flow and transport in plant canopies. Due to model limitations and parameter ...uncertainty, the alignment of model predictions with actual observations is challenging in practice. The present work proposes a Bayesian uncertainty quantification (UQ) framework that estimates the approaching wind angle parameter for large-eddy simulation (LES) of flow in plant canopies by assimilating data from in-situ measurements. The framework is applied to LES of flow within and above realistic plant canopy, with plant area density derived from light detection and ranging measurements. Uncertainty on approaching wind direction is characterized via a Markov chain Monte Carlo procedure, and propagated through Monte Carlo sampling to wind speed and resolved Reynolds stresses. Given the substantial computational cost of LES, a surrogate model based on an exiguous number of LESs is used for flow simulations within the UQ framework. As a result of the analysis, the UQ solution is given by probability density functions of selected flow statistics at different heights. Profiles of mean ± standard deviation for the considered flow statistics exhibit excellent agreement with corresponding observations, proving that the proposed approach is able to calibrate the approaching wind angle parameter, and that the quantified uncertainty captures discrepancies between observations and model results. Overall, the present work highlights the potential of UQ to enhance predictions of exchange processes between vegetation canopy and atmosphere.
It is difficult to understand the atomistic information on the interaction at the metal/corrosion inhibitor interface experimentally which is a key to understanding the mechanism by which inhibitors ...prevent the corrosion of metals. Atomistic simulations (molecular dynamics and Monte Carlo) are mostly performed in corrosion inhibition research to give deeper insights into the mechanism of inhibition of corrosion inhibitors on metal surfaces at the atomic and molecular time scales. A lot of works on the use of molecular dynamics and Monte Carlo simulation to investigate corrosion inhibition phenomenon have appeared in the literature in recent times. However, there is still a lack of comprehensive review on the understanding of corrosion inhibition mechanism using these atomistic simulation methodologies. In this review paper, we first of all introduce briefly some important molecular modeling simulations methods. Thereafter, the basic theories of molecular dynamics and Monte Carlo simulations are highlighted. Several studies on the use of atomistic simulations as a modern tool in corrosion inhibition research are presented. Some mechanistic and energetic information on how organic corrosion inhibitors interact with iron and copper metals are provided. This atomic and molecular level information could aid in the design, synthesis and development of new and novel corrosion inhibitors for industrial applications.
Early seeds of axion miniclusters Vaquero, Alejandro; Redondo, Javier; Stadler, Julia
Journal of cosmology and astroparticle physics,
04/2019, Letnik:
2019, Številka:
4
Journal Article
Recenzirano
Odprti dostop
We study the small scale structure of axion dark matter in the post-inflationary scenario, which predicts the formation of low-mass, high density clumps of gravitationally bound axions called axion ...miniclusters. To this end we follow numerically the cosmological evolution of the axion field and the network of strings and domain walls until the density contrast is frozen. Our simulations, comprising up to 81923 points, are the largest studies of the axion field evolution in the non-linear regime presented so far. Axitons, pseudo-breathers of the Klein-Gordon equation, are observed to form in our simulation at late times. Studying their properties analytically and numerically, we observe that in particular the earliest axitons contribute to density perturbations at the typical length scale of miniclusters. We analyse the small scale structure of the density field, giving the correlation length, power spectrum and the distribution of high density regions that will collapse into axion miniclusters. The final density field of our simulations can be used to calculate the minicluster mass fraction in simulations including gravity. In particular, we find that typical minicluster progenitors are smaller than previously thought and only of moderate, (1) overdensity. We expect these miniclusters to have a rich sub-structure, emerging from small-scale fluctuations produced in the collapse of the string-wall network and from axitons.
Cellulose being the most widely available biopolymer on Earth is attracting significant interest from the industry and research communities. While molecular simulations can be used to understand ...fundamental aspects of cellulose nanocrystal self-assembly, a model that can perform on the experimental scale is currently missing. In our study we develop a supra coarse-grained (sCG) model of cellulose nanocrystal which aims to bridge the gap between molecular simulations and experiments. The sCG model is based on atomistic molecular dynamics simulations and it is developed with the force-matching coarse-graining procedure. The validity of the model is shown through comparison with experimental and simulation results of the elastic modulus, self-diffusion coefficients and cellulose fiber twisting angle. We also present two representative case studies, self-assembly of nanocrystal during solvent evaporation and simulation of a chiral nematic phase ordering. Finally, we discuss possible future applications for our model.
Graphic abstract
Abstract
The trans-Neptunian object 2014 MU69, named Arrokoth, is the most recent evidence that planetesimals did not form by successive collisions of smaller objects, but by the direct gravitational ...collapse of a pebble cloud. But what process sets the physical scales on which this collapse may occur? Star formation has the Jeans mass, that is, when gravity is stronger than thermal pressure, helping us to understand the mass of our Sun. But what controls mass and size in the case of planetesimal formation? Both asteroids and Kuiper Belt objects show a kink in their size distribution at 100 km. Here we derive a gravitational collapse criterion for a pebble cloud to fragment to planetesimals, showing that a critical mass is needed for the clump to overcome turbulent diffusion. We successfully tested the validity of this criterion in direct numerical simulations of planetesimal formation triggered by the streaming instability. Our result can therefore explain the sizes for planetesimals found forming in streaming instability simulations in the literature, while not addressing the detailed size distribution. We find that the observed characteristic diameter of ∼100 km corresponds to the critical mass of a pebble cloud set by the strength of turbulent diffusion stemming from streaming instability for a wide region of a solar nebula model from 2 to 60 au, with a tendency to allow for smaller objects at distances beyond and at late times, when the nebula gas gets depleted.
Abstract
Large-eddy simulations (LES) and implicit LES (ILES) are wise and affordable alternatives to the unfeasible direct numerical simulations of turbulent flows at high Reynolds (Re) numbers. ...However, for systems with few observational constraints, it is a formidable challenge to determine if these strategies adequately capture the physics of the system. Here, we address this problem by analyzing numerical convergence of ILES of turbulent convection in 2D, with resolutions between 64
2
and 2048
2
grid points, along with the estimation of their effective viscosities, resulting in effective Reynolds numbers between 1 and ∼10
4
. The thermodynamic structure of our model resembles the solar interior, including a fraction of the radiative zone and the convection zone. In the convective layer, the ILES solutions converge for the simulations with ≥512
2
grid points, as evidenced by the integral properties of the flow and its power spectra. Most importantly, we found that even a resolution of 128
2
grid points,
Re
∼
10
, is sufficient to capture the dynamics of the large scales accurately. This is a consequence of the ILES method allowing the energy contained in these scales to be the same in simulations with low and high resolution. Special attention is needed in regions with a small density scale height driving the formation of fine structures unresolved by the numerical grid. In the stable layer, we found the excitation of internal gravity waves, yet high resolution is needed to capture their development and interaction.
Modeling galaxy formation in a cosmological context presents one of the greatest challenges in astrophysics today due to the vast range of scales and numerous physical processes involved. Here we ...review the current status of models that employ two leading techniques to simulate the physics of galaxy formation: semianalytic models and numerical hydrodynamic simulations. We focus on a set of observational targets that describe the evolution of the global and structural properties of galaxies from roughly cosmic high noon (
z
∼ 2-3) to the present. Although minor discrepancies remain, overall, models show remarkable convergence among different methods and make predictions that are in qualitative agreement with observations. Modelers have converged on a core set of physical processes that are critical for shaping galaxy properties. This core set includes cosmological accretion, strong stellar-driven winds that are more efficient at low masses, black hole feedback that preferentially suppresses star formation at high masses, and structural and morphological evolution through merging and environmental processes. However, all cosmological models currently adopt phenomenological implementations of many of these core processes, which must be tuned to observations. Many details of how these diverse processes interact within a hierarchical structure formation setting remain poorly understood. Emerging multiscale simulations are helping to bridge the gap between stellar and cosmological scales, placing models on a firmer, more physically grounded footing. Concurrently, upcoming telescope facilities will provide new challenges and constraints for models, particularly by directly constraining inflows and outflows through observations of gas in and around galaxies.
In biocatalysis, structural knowledge regarding an enzyme and its substrate interactions complements and guides experimental investigations. Structural knowledge regarding an enzyme or a biocatalytic ...reaction system can be generated through computational techniques, such as homology- or molecular modeling. For this type of computational work, a computer program developed for molecular modeling of proteins is required. Here, we describe the use of the program YASARA Structure. Protocols for two specific biocatalytic applications, including both homology modeling and molecular modeling such as energy minimization, molecular docking simulations and molecular dynamics simulations, are shown. The applications are chosen to give realistic examples showing how structural knowledge through homology and molecular modeling is used to guide biocatalytic investigations and protein engineering studies.
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