scholarly journals SIMULATING DRUG-TARGET INTERACTION USING LARGE SCALE MOLECULAR DYNAMICS AND FUZZY-ART

2017 ◽  
Vol 10 (13) ◽  
pp. 399
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan
Keyword(s):  
Molecular Dynamics ◽  
Drug Target ◽  
Large Scale ◽  
Md Simulations ◽  
Experimental Information ◽  
Machine Learning Algorithms ◽  
Kinetic Properties ◽  
Fuzzy Art ◽  
Drug Compound ◽  
New Biomarkers

The examination of bio-molecular associations between a complex drug compound and its target is of foremost significance for the improvement of new biomarkers or bioresponsive compounds. In this paper, we exhibited a combinatorial technique of simulation of molecular dynamics (MD) and fuzzy ART to focus on the coupling factors of in the molecular binding process and its intermediary transitioning state. Here, MD simulations divided into microsecond length enable us to watch a inter-molecular coupling events, taking after different dynamical pathways and accomplishing ordered binding assembly of molecules. Results form such simulations are used to evaluate parameters corresponding to its thermodynamic and molecular kinetic properties, getting a decent concurrence with accessible experimental information. Utilizing machine learning algorithms  in conjunction with MD simulations could enhance the productive for identifying key parts of drug–target binding and localization.

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.

scholarly journals Antimicrobial action of the cationic peptide, chrysophsin-3: a coarse-grained molecular dynamics study

Soft Matter ◽  
2018 ◽  
Vol 14 (15) ◽  
pp. 2796-2807 ◽  
Author(s):  
Andrea Catte ◽  
Mark R. Wilson ◽  
Martin Walker ◽  
Vasily S. Oganesyan
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Md Simulations ◽  
Antimicrobial Action ◽  
Coarse Grained ◽  
Cationic Peptide ◽  
Coarse Grained Molecular Dynamics

Antimicrobial action of a cationic peptide is modelled by large scale MD simulations.

scholarly journals Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

2019 ◽  
Author(s):  
Juan J Galano-Frutos ◽  
Helena García-Cebollada ◽  
Javier Sancho
Keyword(s):  
Molecular Dynamics ◽  
Amino Acid ◽  
Large Scale ◽  
Binary Classification ◽  
Chemical Properties ◽  
Md Simulations ◽  
Single Amino Acid ◽  
Medical Decision ◽  
Evolutionary Information ◽  
Protein Variants

Abstract The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical–chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80–85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore’s law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.

scholarly journals Hydrogenation Dynamics of Biphenylene Carbon (Graphenylene) Membranes

MRS Advances ◽  
2017 ◽  
Vol 2 (29) ◽  
pp. 1571-1576
Author(s):  
Vinicius Splugues ◽  
Pedro Alves da Silva Autreto ◽  
Douglas S. Galvao
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Md Simulations ◽  
Structural Transformations ◽  
Extensive Study ◽  
Massively Parallel ◽  
Porous Membranes ◽  
Two Dimensional ◽  
Reactive Force

ABSTRACTThe advent of graphene created a revolution in materials science. Because of this there is a renewed interest in other carbon-based structures. Graphene is the ultimate (just one atom thick) membrane. It has been proposed that graphene can work as impermeable membrane to standard gases, such argon and helium. Graphene-like porous membranes, but presenting larger porosity and potential selectivity would have many technological applications. Biphenylene carbon (BPC), sometimes called graphenylene, is one of these structures. BPC is a porous two-dimensional (planar) allotrope carbon, with its pores resembling typical sieve cavities and/or some kind of zeolites. In this work, we have investigated the hydrogenation dynamics of BPC membranes under different conditions (hydrogenation plasma density, temperature, etc.). We have carried out an extensive study through fully atomistic molecular dynamics (MD) simulations using the reactive force field ReaxFF, as implemented in the well-known Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. Our results show that the BPC hydrogenation processes exhibit very complex patterns and the formation of correlated domains (hydrogenated islands) observed in the case of graphene hydrogenation was also observed here. MD results also show that under hydrogenation BPC structure undergoes a change in its topology, the pores undergoing structural transformations and extensive hydrogenation can produce significant structural damages, with the formation of large defective areas and large structural holes, leading to structural collapse.

scholarly journals Indentation-induced plastic behaviour of nanotwinned Cu/high entropy alloy FeCoCrNi nanolaminate: an atomic simulation

RSC Advances ◽  
2020 ◽  
Vol 10 (16) ◽  
pp. 9187-9192 ◽  
Author(s):  
Hui Feng ◽  
Jingwen Tang ◽  
Haotian Chen ◽  
Yuanyuan Tian ◽  
Qihong Fang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Md Simulations ◽  
High Entropy Alloy ◽  
High Entropy ◽  
Atomic Simulation ◽  
Layer Number ◽  
Plastic Behaviour

Using large-scale molecular dynamics (MD) simulations, the effects of interface and layer number in the nanoindentation response of experimentally observed nanotwinned Cu/high entropy alloy (HEA) FeCoCrNi nanolaminate are studied.

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 Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Junghoon Chae ◽  
Debsindhu Bhowmik ◽  
Heng Ma ◽  
Arvind Ramanathan ◽  
Chad Steed
Keyword(s):  
Molecular Dynamics ◽  
Visual Analytics ◽  
Large Scale ◽  
Physical Phenomenon ◽  
Living Organism ◽  
Md Simulations ◽  
High Dimensional ◽  
Molecular Characteristics ◽  
Complex Simulation ◽  
Dynamics Simulations

AbstractMolecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

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>

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspective

2020 ◽  
Vol 27 ◽  
Author(s):  
Karim Abbasi ◽  
Parvin Razzaghi ◽  
Antti Poso ◽  
Saber Ghanbari-Ara ◽  
Ali Masoudi-Nejad
Keyword(s):  
Deep Learning ◽  
Drug Target ◽  
Large Scale ◽  
Future Perspective ◽  
Multiple Perspectives ◽  
Comprehensive Overview ◽  
In Vivo Experiments ◽  
Drug Compound

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gotten more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming. Machine learning methods, especially deep learning, are widely applied to DTIs prediction. In this study, the main goal is to provide a comprehensive overview of deep learning-based DTIs prediction approaches. Here, we investigate the existing approaches from multiple perspectives. We explore these approaches to find out which deep network architectures are utilized to extract features from drug compound and protein sequences. Also, the advantages and limitations of each architecture are analyzed and compared. Moreover, we explore the process of how to combine descriptors for drug and protein features. Likewise, a list of datasets that are commonly used in DTIs prediction is investigated. Finally, current challenges are discussed and a short future outlook of deep learning in DTI prediction is given.

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.

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