Molecular Dynamics and Multiscale Simulations of Nanoindentation and Nanoscratch

2010 ◽  
Author(s):  
Peng-zhe Zhu ◽  
Hui Wang ◽  
Yuan-zhong Hu
Keyword(s):  
Molecular Dynamics ◽  
Computational Cost ◽  
Three Dimensional ◽  
Multiscale Model ◽  
Md Simulations ◽  
System Size ◽  
Multiscale Simulations ◽  
First Case ◽  
Chip Volume ◽  
Indenter Shape

Three-dimensional molecular dynamics (MD) simulations have been performed to investigate behaviors of nanoindentation and nano-scratch. The first case concerns the effects of material defect on the nanoindentation of nickel thin film. The defect is modeled by a spherical void embedded in the substrate and located under the surface of indentation. The simulation results reveal that compared to the case without defect, the presence of the void softens the material and allows for larger indentation depth at a given load. MD simulations are then performed for nano-scratch of single crystal copper, with emphasis on the effect of indenter shape (sharp and blunt) on the substrate deformation. The results show that the blunt indenter causes larger deformation region and much more dislocations at both the indentation and scratch stages. It is also found that during the scratching stage the blunt indenter results in larger chip volume in front of the indenter and gives rise to more friction than the sharp indenter. The scope of the simulations has been extended by introducing a multiscale model which couples MD simulations with Finite Element Method (FEM), and multiscale simulations are performed for two-dimensional nanoindentation of copper. The model has been validated by well-consistent load-depth curves obtained from both multiscale and full MD simulations, and by good continuity of deformation observed in the handshake region. The simulations also reveal that indenter radius and indentation velocity significantly affect the nanoindentation behavior. By use of multiscale method, the system size to be explored can be greatly expanded without increasing much computational cost.

Hertz Contact at the Nanoscale with a 3D Multiscale Model

2016 ◽  
Vol 846 ◽  
pp. 306-311 ◽  
Author(s):  
Jie Zhang ◽  
Guillaume Michal ◽  
Ahn Kiet Tieu ◽  
Hong Tao Zhu ◽  
Guan Yu Deng
Keyword(s):  
Computational Cost ◽  
Three Dimensional ◽  
Computation Time ◽  
Multiscale Model ◽  
Md Simulations ◽  
Normal Displacement ◽  
Contact Radius ◽  
Asperity Contact ◽  
Fem Model ◽  
Low Computational Cost

This paper presents a three-dimensional multiscale computational model, which is proposed to combine the simplicity of FEM model and the atomistic interactions between two solids. A significant advantage of the model is that atoms are populated in the contact regions, which saves significant computation time compared to fully MD simulations. The model is used in the case of asperity contact. The normal displacement, contact radius and pressure distribution are compared with those from Hertz’s solution and atomistic simulations in the literature. Some important features of nanoscale contacts obtained by MD simulations can be caught by the model with acceptable accuracy and low computational cost.

scholarly journals An Analysis Based on Molecular Docking and Molecular Dynamics Simulation Study of Bromelain as Anti-SARS-CoV-2 Variants

2021 ◽  
Vol 12 ◽  
Author(s):  
Trina Ekawati Tallei ◽  
Fatimawali ◽  
Afriza Yelnetty ◽  
Rinaldi Idroes ◽  
Diah Kusumawaty ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Three Dimensional ◽  
Md Simulations ◽  
Inhibitory Effect ◽  
Dynamics Simulation ◽  
Novel Coronavirus

The rapid spread of a novel coronavirus known as SARS-CoV-2 has compelled the entire world to seek ways to weaken this virus, prevent its spread and also eliminate it. However, no drug has been approved to treat COVID-19. Furthermore, the receptor-binding domain (RBD) on this viral spike protein, as well as several other important parts of this virus, have recently undergone mutations, resulting in new virus variants. While no treatment is currently available, a naturally derived molecule with known antiviral properties could be used as a potential treatment. Bromelain is an enzyme found in the fruit and stem of pineapples. This substance has been shown to have a broad antiviral activity. In this article, we analyse the ability of bromelain to counteract various variants of the SARS-CoV-2 by targeting bromelain binding on the side of this viral interaction with human angiotensin-converting enzyme 2 (hACE2) using molecular docking and molecular dynamics simulation approaches. We have succeeded in making three-dimensional configurations of various RBD variants using protein modelling. Bromelain exhibited good binding affinity toward various variants of RBDs and binds right at the binding site between RBDs and hACE2. This result is also presented in the modelling between Bromelain, RBD, and hACE2. The molecular dynamics (MD) simulations study revealed significant stability of the bromelain and RBD proteins separately up to 100 ns with an RMSD value of 2 Å. Furthermore, despite increases in RMSD and changes in Rog values of complexes, which are likely due to some destabilized interactions between bromelain and RBD proteins, two proteins in each complex remained bonded, and the site where the two proteins bind remained unchanged. This finding indicated that bromelain could have an inhibitory effect on different SARS-CoV-2 variants, paving the way for a new SARS-CoV-2 inhibitor drug. However, more in vitro and in vivo research on this potential mechanism of action is required.

scholarly journals A hierarchical Bayesian framework for force field selection in molecular dynamics simulations

2016 ◽  
Vol 374 (2060) ◽  
pp. 20150032 ◽  
Author(s):  
S. Wu ◽  
P. Angelikopoulos ◽  
C. Papadimitriou ◽  
R. Moser ◽  
P. Koumoutsakos
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Force Field ◽  
Computational Cost ◽  
Md Simulations ◽  
Bayesian Framework ◽  
Underlying Structure ◽  
Hierarchical Bayesian ◽  
Wide Range ◽  
Selection Of

We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.

Thermal Rectification in Graphene and Carbon Nanotube Systems Using Molecular Dynamics Simulations

2011 ◽  
Author(s):  
Ajit K. Vallabhaneni ◽  
Jiuning Hu ◽  
Yong P. Chen ◽  
Xiulin Ruan
Keyword(s):  
Molecular Dynamics ◽  
Carbon Nanotube ◽  
Temperature Difference ◽  
Graphene Nanoribbons ◽  
Md Simulations ◽  
System Size ◽  
Heat Current ◽  
Thermal Rectification ◽  
Mean Temperature ◽  
Rectification Factor

We investigate the thermal rectification phenomena in asymmetric graphene and carbon nanotube systems using molecular dynamics (MD) simulations. The effects of various parameters, including mean temperature, temperature difference, and system size on rectification factor have been studied. In homogenous triangular graphene nanoribbons (T-GNR), the heat current is normally higher from wide to narrow end than that in the opposite direction, resulting in a positive rectification factor. The rectification factor increases further for a double layered T-GNR. It is also found that varying the parameters like mean temperature can result in reverse of the sign of thermal rectification factor. In the case of carbon nanotube (CNT)–silicon system, the heat current is higher when heat flows from CNT to silicon. The thermal rectification factor is almost independent of the diameter of CNT. In both cases, the rectification factor increases with the imposed temperature difference.

Mechanical Properties of α-Helices Estimated Using Molecular Dynamics and Finite Element Simulations

2008 ◽  
Author(s):  
Peyman Honarmandi ◽  
Philip Bransford ◽  
Roger D. Kamm
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Finite Element ◽  
Solid Mechanics ◽  
Normal Mode Analysis ◽  
Computational Cost ◽  
Md Simulations ◽  
Elastic Stiffness ◽  
Mode Analysis ◽  
Molecular Volume

Mechanical properties of biomolecules and their response to mechanical forces may be studied using Molecular Dynamics (MD) simulations. However, high computational cost is a primary drawback of MD simulations. This paper presents a computational framework based on the integration of the Finite Element Method (FEM) with MD simulations to calculate the mechanical properties of polyalanine α-helix proteins. In this method, proteins are treated as continuum elastic solids with molecular volume defined exclusively by their atomic surface. Therefore, all solid mechanics theories would be applicable for the presumed elastic media. All-atom normal mode analysis is used to calculate protein’s elastic stiffness as input to the FEM. In addition, constant force molecular dynamics (CFMD) simulations can be used to predict other effective mechanical properties, such as the Poisson’s Ratio. Force versus strain data help elucidate the mechanical behavior of α-helices upon application of constant load. The proposed method may be useful in identifying the mechanical properties of any protein or protein assembly with known atomic structure.

scholarly journals A Virtual Reality Ensemble Molecular Dynamics Workflow to Study Complex Conformational Changes in Proteins

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
A Priori ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals Determination of Protein Structural Ensembles by Hybrid-Resolution SAXS Restrained Molecular Dynamics.

2019 ◽  
Author(s):  
Cristina Paissoni ◽  
Alexander Jussupow ◽  
Carlo Camilloni
Keyword(s):  
Dynamical Behavior ◽  
Computational Cost ◽  
Three Dimensional ◽  
Md Simulations ◽  
Coarse Grained ◽  
Conformational Ensemble ◽  
Biomolecular Systems ◽  
Conformational Ensembles ◽  
Independent Experimental Data

<div><div><div><p>SAXS experiments provide low-resolution but valuable information about the dynamics of biomolecular systems, which could be ideally integrated in MD simulations to accurately determine conformational ensembles of flexible proteins. The applicability of this strategy is hampered by the high computational cost required to calculate scattering intensities from three-dimensional structures. We previously presented a metainference-based hybrid resolution method that makes atomistic SAXS-restrained MD simulation feasible by adopting a coarse-grained approach to efficiently back-calculate scattering intensities; here, we extend this technique, applying it in the framework of multiple-replica simulations with the aim to investigate the dynamical behavior of flexible biomolecules. The efficacy of the method is assessed on the K63-diubiquitin multi-domain protein, showing that inclusion of SAXS-restraints is effective in generating reliable and heterogenous conformational ensemble, also improving the agreement with independent experimental data.</p></div></div></div>

scholarly journals Development and Application of a Single Neural Network Potential for IRMOF-n (n=1,4,6,7,10)

2021 ◽  
Author(s):  
Omer Tayfuroglu ◽  
Abdul Kadir Kocak ◽  
Yunus Zorlu
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Thermal Expansion ◽  
Density Of States ◽  
Mechanical Strain ◽  
Computational Cost ◽  
Negative Thermal Expansion ◽  
Md Simulations ◽  
Ab Initio Molecular Dynamics ◽  
Lattice Constants

Metal‑organic frameworks (MOFs) with their exceptional porous and organized structures have been subject of numerous applications. Predicting macroscopic properties from atomistic simulations require the most accurate force fields, which is still a major problem due to MOFs’ hybrid structures governed by covalent, ionic and dispersion forces. Application of ab‑initio molecular dynamics to such large periodic systems are thus beyond the current computational power. Therefore, alternative strategies must be developed to reduce computational cost without losing reliability. In this work, we describe the construction of a neural network potential (NNP) for IRMOF‑n series (n=1,4,7,10) trained by PBE-D4/def2-TZVP reference data of MOF fragments. We validated the resulting NNP on both fragments and bulk MOF structures by prediction of properties such as equilibrium lattice constants, phonon density of states and linker orientation. The energy and force RMSE values for the fragments are only 0.0017 eV/atom and 0.15 eV/Å, respectively. The NNP predicted equilibrium lattice constants of bulk structures, which are not included in training, are off by only 0.2-2.4% from experimental results. Moreover, our fragment trained NNP greatly predicts phenylene ring torsional energy barrier, equilibrium bond distances and vibrational density of states of bulk MOFs. Furthermore, NNP allows us to investigate unusual behaviors of selected MOFs such as the thermal expansion properties and the effect of mechanical strain on the adsorption of hydrogen and methane molecules. The NNP based molecular dynamics (MD) simulations suggest the IRMOF‑4 and IRMOF‑7 to have positive‑to‑negative thermal expansion coefficients while the rest to have only negative thermal expansion under the studied temperatures of 200 K to 400 K. The deformation of bulk structure by reduction of unit cell volume has shown to increase volumetric methane uptake in IRMOF‑1 but decrease in IRMOF‑7 due to the steric hindrance.

scholarly journals Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Protein Conformational Changes ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals Efficient Construction of Atomic-Resolution Models of Non-Sulfated Chondroitin Glycosaminoglycan Using Molecular Dynamics Data

Biomolecules ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 537 ◽  
Author(s):  
Elizabeth K. Whitmore ◽  
Gabriel Vesenka ◽  
Hanna Sihler ◽  
Olgun Guvench
Keyword(s):  
Molecular Dynamics ◽  
Computational Cost ◽  
Md Simulations ◽  
Atomic Resolution ◽  
Backbone Flexibility ◽  
Arbitrary Length ◽  
Internal Validation ◽  
Carbohydrate Polymers ◽  
Distance Distributions ◽  
End Distance

Glycosaminoglycans (GAGs) are linear, structurally diverse, conformationally complex carbohydrate polymers that may contain up to 200 monosaccharides. These characteristics present a challenge for studying GAG conformational thermodynamics at atomic resolution using existing experimental methods. Molecular dynamics (MD) simulations can overcome this challenge but are only feasible for short GAG polymers. To address this problem, we developed an algorithm that applies all conformational parameters contributing to GAG backbone flexibility (i.e., bond lengths, bond angles, and dihedral angles) from unbiased all-atom explicit-solvent MD simulations of short GAG polymers to rapidly construct models of GAGs of arbitrary length. The algorithm was used to generate non-sulfated chondroitin 10- and 20-mer ensembles which were compared to MD-generated ensembles for internal validation. End-to-end distance distributions in constructed and MD-generated ensembles have minimal differences, suggesting that our algorithm produces conformational ensembles that mimic the backbone flexibility seen in simulation. Non-sulfated chondroitin 100- and 200-mer ensembles were constructed within a day, demonstrating the efficiency of the algorithm and reduction in time and computational cost compared to simulation.

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