A Virtual Reality Environment for Interactive Molecular Dynamics Simulations of 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 a priori. 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 for bioengineering efforts. </p>

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 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 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>

Molecular Basis Explanation of Imatinib Resistance of Bcr-Abl Due to T315I and P-Loop Mutations from Molecular Dynamics Simulations.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2917-2917
Author(s):  
Tai-Sung Lee ◽  
Steven Potts ◽  
Hagop Kantarjian ◽  
Jorge Cortes ◽  
Francis Giles ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Conformational Changes ◽  
Electrostatic Interactions ◽  
Large Scale ◽  
Resistance Mechanism ◽  
Md Simulations ◽  
Resistance Mechanisms ◽  
Static Structure ◽  
Imatinib Resistance ◽  
Dynamics Simulations

Abstract Molecular dynamics (MD) simulations on the complex of imatinib with the wild-type, T315I, and other 10 P-loop mutants of the tyrosine kinase Bcr-Abl have been performed to study the imatinib resistance mechanism at the atomic level. MD simulations show that large scale computational simulations could offer insight information that a static structure or simple homology modeling methods cannot provide for studying the Bcr-Abl imatinib resistance problem, especially in the case of conformational changes due to remote mutations. By utilizing the Molecular Mechanics/Poisson-Boltzmann surface area (MM-PBSA) techniques and analyzing the interactions between imatinib and individual residues, imatinib resistance mechanisms not previously thought have been revealed. Non-directly contacted P-loop mutations either unfavorably change the direct electrostatic interactions with imatinib, or cause the conformational changes influencing the contact energies between imatinib and other non-P-loop residues. We demonstrate that imatinib resistance of T315I mainly comes from the breakdown of the interactions between imatinib and E286 and M290, contradictory to previously suggested that the missing hydrogen bonding is the main contribution. We also demonstrate that except for the mutations of the direct contact residues, such as L248 and Y253, the unfavorable electrostatic interaction between P-loop and imatinib is the main reason for resistance for the P-loop mutations. Furthermore, in Y255H, protonation of the histidin is essential for rendering this mutation resistant to Gleevec. Our results demonstrate that MD is a powerful way to verify and predict clinical response or resistance to imatinib and other potential drugs.

scholarly journals Molecular dynamics simulations disclose early stages of the photo-activation of cryptochrome 4

2018 ◽  
Author(s):  
D. R. Kattnig ◽  
C. Nielsen ◽  
I. A. Solov’yov
Keyword(s):  
Molecular Dynamics ◽  
Conformational Changes ◽  
Large Scale ◽  
Conformational Dynamics ◽  
Md Simulations ◽  
Quantum Processes ◽  
Helical Segment ◽  
High Betweenness ◽  
Dynamics Simulations ◽  
Fad Binding

AbstractBirds appear to be equipped with a light-dependent, radical-pair-based magnetic compass that relies on truly quantum processes. While the identity of the sensory protein has remained speculative, cryptochrome 4 has recently been identified as the most auspicious candidate. Here, we report on allatom molecular dynamics (MD) simulations addressing the structural reorganisations that accompany the photoreduction of the flavin cofactor in a model of the European robin cryptochrome 4 (ErCry4). Extensive MD simulations reveal that the photo-activation of ErCry4 induces large-scale conformational changes on short (hundreds of nanoseconds) timescales. Specifically, the photo-reduction is accompanied with the release of the C-terminal tail, structural rearrangements in the vicinity of the FAD-binding site, and the noteworthy formation of an α-helical segment at the N-terminal part. Some of these rearrangements appear to expose potential phosphorylation sites. We describe the conformational dynamics of the protein using a graph-based approach that is informed by the adjacency of residues and the correlation of their local motions. This approach reveals densely coupled reorganisation communities, which facilitate an efficient signal transduction due to a high density of hubs. These communities are interconnected by a small number of highly important residues characterized by high betweenness centrality. The network approach clearly identifies the sites restructuring upon photoactivation, which appear as protrusions or delicate bridges in the reorganisation network. We also find that, unlike in the homologous cryptochrome from D. melanogaster, the release of the C-terminal domain does not appear to be correlated with the transposition of a histidine residue close to the FAD cofactor.

scholarly journals A Generalised Mechano-Kinetic Model For Use in Multiscale Simulation Protocols

2020 ◽  
Author(s):  
Benjamin S. Hanson ◽  
Daniel J. Read
Keyword(s):  
High Resolution ◽  
Kinetic Model ◽  
Conformational Changes ◽  
Large Scale ◽  
Computational Cost ◽  
Multiscale Simulation ◽  
State Theory ◽  
Coarse Grained ◽  
Simulation Framework ◽  
State Models

AbstractMany biophysical systems and proteins undergo mesoscopic conformational changes in order to perform their biological function. However, these conformational changes often result from a cascade of atomistic interactions within a much larger overall object. For simulations of such systems, the computational cost of retaining high-resolution structural and dynamical information whilst at the same time observing large scale motions over long times is high. Furthermore, this cost is only going to increase as ever larger biological objects are observed experimentally at high resolution.With insight from the theory of Markov state models and transition state theory, we derive a generalised mechano-kinetic simulation framework which aims to compensate for these disparate time scales. The framework models continuous dynamical motion at coarse-grained length scales whilst simultaneously including fine-detail, chemical reactions or conformational changes implicitly using a kinetic model. The kinetic model is coupled to the dynamic model in a highly generalised manner, such that it can be applied to any defined continuous energy landscape, and indeed, any existing dynamic simulation framework. We present a series of analytical examples to validate the framework, and showcase its capabilities for studying more complex systems by designing a simulation of minimalist molecular motor.

scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura
Keyword(s):  
Molecular Dynamics ◽  
Renormalization Group ◽  
Critical Exponent ◽  
Computational Efficiency ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Coarse Graining ◽  
Spatial Dimension ◽  
Deborah Number

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

THEORETICAL INVESTIGATIONS ON ELUCIDATING FUNDAMENTAL MECHANISMS OF CATALYSIS AND DYNAMICS INVOLVED IN TRANSCRIPTION BY RNA POLYMERASE

2013 ◽  
Vol 12 (08) ◽  
pp. 1341005 ◽  
Author(s):  
FÁTIMA PARDO-AVILA ◽  
LIN-TAI DA ◽  
YING WANG ◽  
XUHUI HUANG
Keyword(s):  
Rna Polymerase ◽  
Conformational Changes ◽  
Normal Mode Analysis ◽  
Md Simulations ◽  
Mode Analysis ◽  
Transcription Process ◽  
Nucleotide Addition ◽  
Theoretical Investigations ◽  
State Models ◽  
Elongation Process

RNA polymerase is the enzyme that synthesizes RNA during the transcription process. To understand its mechanism, structural studies have provided us pictures of the series of steps necessary to add a new nucleotide to the nascent RNA chain, the steps altogether known as the nucleotide addition cycle (NAC). However, these static snapshots do not provide dynamic information of these processes involved in NAC, such as the conformational changes of the protein and the atomistic details of the catalysis. Computational studies have made efforts to fill these knowledge gaps. In this review, we provide examples of different computational approaches that have improved our understanding of the transcription elongation process for RNA polymerase, such as normal mode analysis, molecular dynamic (MD) simulations, Markov state models (MSMs). We also point out some unsolved questions that could be addressed using computational tools in the future.

Molecular Dynamics Simulation of a Pullout Test on a Carbon Nanotube in a Polymer Matrix

2014 ◽  
Vol 1700 ◽  
pp. 61-66
Author(s):  
Guttormur Arnar Ingvason ◽  
Virginie Rollin
Keyword(s):  
Molecular Dynamics ◽  
Polymer Matrix ◽  
Large Scale ◽  
Md Simulations ◽  
Dynamics Simulation ◽  
Atomistic Simulations ◽  
Polymer Molecules ◽  
Molecular Potential ◽  
Pull Out ◽  
Walled Carbon Nanotubes

ABSTRACTAdding single walled carbon nanotubes (SWCNT) to a polymer matrix can improve the delamination properties of the composite. Due to the complexity of polymer molecules and the curing process, few 3-D Molecular Dynamics (MD) simulations of a polymer-SWCNT composite have been run. Our model runs on the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), with a COMPASS (Condensed phase Optimized Molecular Potential for Atomistic Simulations Studies) potential. This potential includes non-bonded interactions, as well as bonds, angles and dihedrals to create a MD model for a SWCNT and EPON 862/DETDA (Diethyltoluenediamine) polymer matrix. Two simulations were performed in order to test the implementation of the COMPASS parameters. The first one was a tensile test on a SWCNT, leading to a Young’s modulus of 1.4 TPa at 300K. The second one was a pull-out test of a SWCNT from an originally uncured EPON 862/DETDA matrix.

Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111)

2021 ◽  
Author(s):  
Kai Xu ◽  
Lei Yan ◽  
Bingran You
Keyword(s):  
Molecular Dynamics ◽  
Active Learning ◽  
Force Field ◽  
Density Functional ◽  
Process Model ◽  
Large Scale ◽  
Computational Cost ◽  
Dynamics Simulation ◽  
Potential Energy Landscape ◽  
Three Body

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.

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