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.

scholarly journals Constructing 3-Dimensional Atomic-Resolution Models of Nonsulfated Glycosaminoglycans with Arbitrary Lengths Using Conformations from Molecular Dynamics

2020 ◽  
Vol 21 (20) ◽  
pp. 7699 ◽  
Author(s):  
Elizabeth K. Whitmore ◽  
Devon Martin ◽  
Olgun Guvench
Keyword(s):  
Molecular Dynamics ◽  
Md Simulation ◽  
Md Simulations ◽  
Atomic Resolution ◽  
Dihedral Angles ◽  
Backbone Flexibility ◽  
Arbitrary Length ◽  
3 Dimensional ◽  
Conformational Ensembles ◽  
Carbohydrate Components

Glycosaminoglycans (GAGs) are the linear carbohydrate components of proteoglycans (PGs) and are key mediators in the bioactivity of PGs in animal tissue. GAGs are heterogeneous, conformationally complex, and polydisperse, containing up to 200 monosaccharide units. These complexities make studying GAG conformation a challenge for existing experimental and computational methods. We previously described an algorithm we developed that applies conformational parameters (i.e., all bond lengths, bond angles, and dihedral angles) from molecular dynamics (MD) simulations of nonsulfated chondroitin GAG 20-mers to construct 3-D atomic-resolution models of nonsulfated chondroitin GAGs of arbitrary length. In the current study, we applied our algorithm to other GAGs, including hyaluronan and nonsulfated forms of dermatan, keratan, and heparan and expanded our database of MD-generated GAG conformations. Here, we show that individual glycosidic linkages and monosaccharide rings in 10- and 20-mers of hyaluronan and nonsulfated dermatan, keratan, and heparan behave randomly and independently in MD simulation and, therefore, using a database of MD-generated 20-mer conformations, that our algorithm can construct conformational ensembles of 10- and 20-mers of various GAG types that accurately represent the backbone flexibility seen in MD simulations. Furthermore, our algorithm efficiently constructs conformational ensembles of GAG 200-mers that we would reasonably expect from MD simulations.

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.

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.

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.

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 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 Receptor flexibility in molecular cross-docking

2016 ◽  
Author(s):  
Lucia Sessa ◽  
Luigi Di BIasi ◽  
Rosaura Parisi ◽  
Simona Concilio ◽  
Stefano Piotto
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Ligand Binding ◽  
Computational Cost ◽  
Md Simulations ◽  
Binding Pocket ◽  
Receptor Flexibility ◽  
Cross Docking ◽  
Internal Cavities ◽  
The Cross

Motivation Molecular docking is an efficient method to predict the conformations adopted by the ligand within the target binding site. Usually, standard docking protocol involves only one structure to represent the receptor, overlooking the changes in the binding pocket geometry induced by ligand binding. In our previous work, we observed that different conformations of the same target show different volume and shape of the internal cavities (Sessa et al., 2016). Different ligands may stabilize different receptor conformations with different internal cavities. Consequently, the crystallographic data represent the adaptation of a protein to a particular ligand. Cross-docking is a validation procedure consisting in docking a series of ligands into different conformation of the same receptor. Since the structures of the same receptor can be rather different, the cross-docking analyses are typically very poor. In these cases the internal cavity of the buried binding pocket does not have space enough to accommodate all ligands and this can radically affect the outcome and alter the cross-docking results. The changes of the cavity volume might explain the failure of traditional docking method and support the hypothesis that a single representative structure for the receptor is not enough. Keeping target proteins flexible during the docking has a high computational cost. To overcome this limit, our docking strategy is to represent receptor flexibility through an inexpensive method that generates a series of target structures. Starting from a known target structure, we used the molecular dynamics (MD) simulations to explore the conformational changes induced by ligand binding and to collect several snapshots of receptor structures to perform the cross-docking studies. To validate the accuracy of our flexible protocol in docking, we used a set of 10 crystallographic conformations of Androgen Receptor with the same target but with a different ligand. We performed two parallel experiments of docking, one with a rigid protein target and one considering flexible receptor structures. In addition, we compared the results for both experiments in the re-docking and in the cross-docking analysis. Methods Ten receptor structures complexed with a ligand were extracted from the X-ray structures in the PDB database (Berman et al., 2000). Several conformations for each receptor were selected from the molecular dynamics simulations (MD) at regular time intervals (each 500 ps). The MD simulations were performed with the software YASARA Structure 16.2.14 (Krieger & Vriend, 2014) using AMBER14 as force field. The molecular docking simulations were performed using VINA provided in the YASARA package. "Abstract truncated at 3,000 characters - the full version is available in the pdf file"

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 Receptor flexibility in molecular cross-docking

2016 ◽  
Author(s):  
Lucia Sessa ◽  
Luigi Di BIasi ◽  
Rosaura Parisi ◽  
Simona Concilio ◽  
Stefano Piotto
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Ligand Binding ◽  
Computational Cost ◽  
Md Simulations ◽  
Binding Pocket ◽  
Receptor Flexibility ◽  
Cross Docking ◽  
Internal Cavities ◽  
The Cross

Motivation Molecular docking is an efficient method to predict the conformations adopted by the ligand within the target binding site. Usually, standard docking protocol involves only one structure to represent the receptor, overlooking the changes in the binding pocket geometry induced by ligand binding. In our previous work, we observed that different conformations of the same target show different volume and shape of the internal cavities (Sessa et al., 2016). Different ligands may stabilize different receptor conformations with different internal cavities. Consequently, the crystallographic data represent the adaptation of a protein to a particular ligand. Cross-docking is a validation procedure consisting in docking a series of ligands into different conformation of the same receptor. Since the structures of the same receptor can be rather different, the cross-docking analyses are typically very poor. In these cases the internal cavity of the buried binding pocket does not have space enough to accommodate all ligands and this can radically affect the outcome and alter the cross-docking results. The changes of the cavity volume might explain the failure of traditional docking method and support the hypothesis that a single representative structure for the receptor is not enough. Keeping target proteins flexible during the docking has a high computational cost. To overcome this limit, our docking strategy is to represent receptor flexibility through an inexpensive method that generates a series of target structures. Starting from a known target structure, we used the molecular dynamics (MD) simulations to explore the conformational changes induced by ligand binding and to collect several snapshots of receptor structures to perform the cross-docking studies. To validate the accuracy of our flexible protocol in docking, we used a set of 10 crystallographic conformations of Androgen Receptor with the same target but with a different ligand. We performed two parallel experiments of docking, one with a rigid protein target and one considering flexible receptor structures. In addition, we compared the results for both experiments in the re-docking and in the cross-docking analysis. Methods Ten receptor structures complexed with a ligand were extracted from the X-ray structures in the PDB database (Berman et al., 2000). Several conformations for each receptor were selected from the molecular dynamics simulations (MD) at regular time intervals (each 500 ps). The MD simulations were performed with the software YASARA Structure 16.2.14 (Krieger & Vriend, 2014) using AMBER14 as force field. The molecular docking simulations were performed using VINA provided in the YASARA package. "Abstract truncated at 3,000 characters - the full version is available in the pdf file"

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