Maximizing resource usage in multifold molecular dynamics with rCUDA

The full-understanding of the dynamics of molecular systems at the atomic scale is of great relevance in the fields of chemistry, physics, materials science, and drug discovery just to name a few. Molecular dynamics (MD) is a widely used computer tool for simulating the dynamical behavior of molecules. However, the computational horsepower required by MD simulations is too high to obtain conclusive results in real-world scenarios. This is mainly motivated by two factors: (1) the long execution time required by each MD simulation (usually in the nanoseconds and microseconds scale, and beyond) and (2) the large number of simulations required in drug discovery to study the interactions between a large library of compounds and a given protein target. To deal with the former, graphics processing units (GPUs) have come up into the scene. The latter has been traditionally approached by launching large amounts of simulations in computing clusters that may contain several GPUs on each node. However, GPUs are targeted as a single node that only runs one MD instance at a time, which translates into low GPU occupancy ratios and therefore low throughput. In this work, we propose a strategy to increase the overall throughput of MD simulations by increasing the GPU occupancy through virtualized GPUs. We use the remote CUDA (rCUDA) middleware as a tool to decouple GPUs from CPUs, and thus enabling multi-tenancy of the virtual GPUs. As a working test in the drug discovery field, we studied the binding process of a novel flavonol to DNA with the GROningen MAchine for Chemical Simulations (GROMACS) MD package. Our results show that the use of rCUDA provides with a 1.21× speed-up factor compared to the CUDA counterpart version while requiring a similar power budget.

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Fast and Flexible GPU Accelerated Binding Free Energy Calculations within the AMBER Molecular Dynamics Package

10.1101/247692 ◽

2018 ◽

Author(s):

Daniel J. Mermelstein ◽

Lin Charles ◽

Nelson Gard ◽

Kretsch Rachael ◽

J. Andrew McCammon ◽

...

Keyword(s):

Molecular Dynamics ◽

Free Energy ◽

Graphics Processing Units ◽

Early Stage ◽

Binding Free Energy ◽

Md Simulations ◽

Free Energy Calculations ◽

Energy Calculations ◽

Design And Optimization ◽

Time Required

AbstractAlchemical free energy calculations (AFE) based on molecular dynamics (MD) simulations are key tools in both improving our understanding of a wide variety of biological processes and accelerating the design and optimization of therapeutics for numerous diseases. Computing power and theory have, however, long been insufficient to enable AFE calculations to be routinely applied in early stage drug discovery. One of the major difficulties in performing AFE calculations is the length of time required for calculations to converge to an ensemble average. CPU implementations of MD based free energy algorithms can effectively only reach tens of nanoseconds per day for systems on the order of 50,000 atoms, even running on massively parallel supercomputers. Therefore, converged free energy calculations on large numbers of potential lead compounds are often untenable, preventing researchers from gaining crucial insight into molecular recognition, potential druggability, and other crucial areas of interest. Graphics Processing Units (GPUs) can help address this. We present here a seamless GPU implementation, within the PMEMD module of the AMBER molecular dynamics package, of thermodynamic integration (TI) capable of reaching speeds of >140 ns/day for a 44,907-atom system, with accuracy equivalent to the existing CPU implementation in AMBER. The implementation described here is currently part of the AMBER 18 beta code and will be an integral part of the upcoming version 18 release of AMBER.

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Hydrogenation Dynamics of Biphenylene Carbon (Graphenylene) Membranes

MRS Advances ◽

10.1557/adv.2017.239 ◽

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.

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Study on Nanoimprint Lithography of the Polymer Chains (CH2)n With Anti-Adhesion Layer on Stamp by Molecular Dynamics Method

ASME 2008 6th International Conference on Nanochannels, Microchannels, and Minichannels ◽

10.1115/icnmm2008-62172 ◽

2008 ◽

Author(s):

Quang-Cherng Hsu ◽

Chien-Liang Lin ◽

Te-Hua Fang

Keyword(s):

Molecular Dynamics ◽

Polymer Material ◽

Nanoimprint Lithography ◽

Minimum Energy ◽

Md Simulations ◽

Atomic Scale ◽

Polymer Materials ◽

Energy Status ◽

Adhesion Layer ◽

System Energy

This paper aims at the study on nanoimprint lithography (NIL) of the polymer material in (CH2)n Chains. The simulation codes were built based on molecular dynamics (MD) method for observing material deformation behaviors in atomic scale. The deformation mechanism of NIL of polymer material (CH2)n pressed by silicon stamp was first studied, by which the effects of critical punch tip width, imprint depth, temperature, and adhesion effect were studied. Next, the nanoimprint processes with stamp tips covered by anti-adhesion material, which is a self-assembled monolayer (SAM), were studied to compare to those processes without having anti-adhesion layer. When deforming polymer material at or above room temperature, adhesion problems occur between stamp and polymer. Polymer materials adhere to stamp more severe than they adhere to each others because potential energies between long chains of polymers are smaller than those between polymer and stamp. From the relation between system energy and stamp translation based on the MD simulations, the system energy increases when stamp moves gradually. When unloading, the system energy will return to its minimum energy status and remains stable. However, when punch leaves polymer materials, energy fluctuation occurs due to some polymer materials adhere to the stamp. Finally, the analysis of stamp with and without SAM based on the MD method was conducted and discussed.

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New View of Low-Temperature Sintering Phenomenon of Nanometer-Size Particles Based on Molecular Dynamics Study

MRS Advances ◽

10.1557/adv.2016.321 ◽

2016 ◽

Vol 1 (30) ◽

pp. 2167-2172

Author(s):

Norie Matsubara ◽

Shinji Munetoh ◽

Osamu Furukimi

Keyword(s):

Molecular Dynamics ◽

Surface Layer ◽

Diffusion Coefficient ◽

Heating Temperature ◽

Md Simulations ◽

Atomic Scale ◽

Self Diffusion ◽

The Mean ◽

The Relationship ◽

Self Diffusion Coefficient

ABSTRACTIn this study, we have investigated a behavior of particle with diameter several ten nanometers size at the time of heating on an atomic scale by numerical analysis using the molecular dynamics (MD) simulation. On solving the equation of motion, the Langevin equation was adopted. The Finnis-Sinclair potential, which can well reproduce the mechanical properties of a BCC-metal, was used as the interatomic force. We determined the relationship between the melting point (Tm) of the nano-sized particles and its diameter by MD simulations. We have also investigated the self-diffusion coefficient of each atom-forming at a temperature larger or less than Tm of the submicron-size metal particles . As a result, even in case of heating at a temperature larger than Tm, the mean self-diffusion coefficient at the center of a particle was 10-7–10-6 cm2/sec. On the other hand, at the surface layer of the particle was two to three orders of magnitude larger than that at the center. Those particles were in a quasi-molten state. It is conceivable that the thickness of the surface layer can explain a phenomenon that sintering progresses as the heating temperature increases.

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Data-Driven Molecular Dynamics: A Multifaceted Challenge

Pharmaceuticals ◽

10.3390/ph13090253 ◽

2020 ◽

Vol 13 (9) ◽

pp. 253

Author(s):

Mattia Bernetti ◽

Martina Bertazzo ◽

Matteo Masetti

Keyword(s):

Molecular Dynamics ◽

Big Data ◽

Drug Discovery ◽

Drug Design ◽

Md Simulations ◽

Biomolecular Systems ◽

Biomolecular Simulations ◽

The Impact ◽

Opening Up ◽

Computational Drug Discovery

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

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DDT - Drug Discovery Tool: a fast and intuitive graphics user interface for docking and molecular dynamics analysis

Bioinformatics ◽

10.1093/bioinformatics/btz543 ◽

2019 ◽

Author(s):

Simone Aureli ◽

Daniele Di Marino ◽

Stefano Raniolo ◽

Vittorio Limongelli

Keyword(s):

Molecular Dynamics ◽

User Interface ◽

Drug Discovery ◽

Protein Binding ◽

Protein Complexes ◽

Md Simulations ◽

Supplementary Information ◽

Chemical Information ◽

Binding Interaction ◽

Fast Analysis

Abstract Motivation The ligand/protein binding interaction is typically investigated by docking and molecular dynamics (MD) simulations. In particular, docking-based virtual screening (VS) is used to select the best ligands from database of thousands of compounds, while MD calculations assess the energy stability of the ligand/protein binding complexes. Considering the broad use of these techniques, it is of great demand to have one single software that allows a combined and fast analysis of VS and MD results. With this in mind, we have developed the Drug Discovery Tool (DDT) that is an intuitive graphics user interface able to provide structural data and physico-chemical information on the ligand/protein interaction. Results DDT is designed as a plugin for the Visual Molecular Dynamics (VMD) software and is able to manage a large number of ligand/protein complexes obtained from AutoDock4 (AD4) docking calculations and MD simulations. DDT delivers four main outcomes: i) ligands ranking based on an energy score; ii) ligand ranking based on a ligands’ conformation cluster analysis; iii) identification of the aminoacids forming the most occurrent interactions with the ligands; iv) plot of the ligands’ center-of-mass coordinates in the Cartesian space. The flexibility of the software allows saving the best ligand/protein complexes using a number of user-defined options. Availability and implementation DDT_site_1 (alternative DDT_site_2); the DDT tutorial movie is available here. Supplementary information Supplementary data are available at Bioinformatics online.

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Insights from Molecular Dynamics Simulations for GPCR Drug Discovery

10.20944/preprints201908.0271.v1 ◽

2019 ◽

Author(s):

Ye Zou ◽

John Ewalt ◽

Ho-Leung Ng

Keyword(s):

Molecular Dynamics ◽

Drug Discovery ◽

G Protein ◽

Drug Targets ◽

Conformational Dynamics ◽

Md Simulations ◽

Protein Activation ◽

Downstream Signaling ◽

Dynamics Simulations ◽

G Protein Coupled

G protein-coupled receptors (GPCRs) are critical drug targets. GPCRs convey signals from the extracellular to the intracellular environment through G proteins. There is evidence that some ligands that bind to the GPCRs activate different downstream signaling pathways. G protein activation or -arrestin biased signaling involves ligands binding to receptors and stabilizing conformations that trigger a specific pathway. Molecular dynamics (MD) simulations are especially valuable for obtaining detailed mechanistic information, including identification of allosteric sites and understanding modulators' interactions between receptors and ligands. Here, we highlight recent simulation studies and methods used to study biased G protein-coupled receptor signaling and their conformational dynamics. We also highlight applications of MD simulations to drug discovery.

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A molecular dynamics investigation of CDK8/CycC and ligand binding: conformational flexibility and implication in drug discovery

10.1101/169581 ◽

2017 ◽

Author(s):

Wei Chen ◽

Zhiye Tang ◽

Tim Cholko ◽

Chia-en A. Chang

Keyword(s):

Molecular Dynamics ◽

Drug Discovery ◽

Ligand Binding ◽

Md Simulations ◽

Electrostatic Energy ◽

Major Protein ◽

Type I ◽

Type Ii ◽

Protein Motions ◽

The Stability

AbstractThe activities of CDK8 with partner Cyclin C (CycC) are a common feature of many diseases, especially cancers. Here we report the study of dynamic behaviors and energy profiles of 13 CDK8/CycC systems, including the DMG-in and DMG-out conformations as well as 5 type I ligands and 5 type II ligands, with all-atom unbiased molecular dynamics (MD) simulations. We observed numerous regional motions within CDK8, which move in concert to form five major protein motions. The motion of the activation loop doesn’t appear to influence the binding of both types of ligands. Type I ligands remarkably reduce the motion of the C-terminal tail through the strong cation-π interaction between the ligands and ARG356, and type II ligands stabilize the αC helix by forming stable hydrogen bonds with GLU66. The MD calculations also confirmed the importance of CycC to the stability of the CDK8 system as well as the ligand binding. The MMPB/SA results show that van der Waals interaction is the main driving force for the binding of both types of ligands, but electrostatic energy and entropy penalty plays important roles in the binding of type II ligands. The volume analysis results indicate that the induced fitting theory applies in the binding of type I ligands. These results would help to improve the affinities of the existing ligands. Our MD work is complementary to crystal structures and may have implications in the development of new CDK8 inhibitors as well as in the field of drug discovery.

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Large-Scale Molecular Dynamics Simulations of Interstitial Defect Diffusion in Silicon

MRS Proceedings ◽

10.1557/proc-731-w9.10 ◽

2002 ◽

Vol 731 ◽

Cited By ~ 1

Author(s):

David A. Richie ◽

Jeongnim Kim ◽

Richard Hennig ◽

Kaden Hazzard ◽

Steve Barr ◽

...

Keyword(s):

Molecular Dynamics ◽

Large Scale ◽

Materials Science ◽

Tight Binding ◽

Md Simulations ◽

Binding Potential ◽

Interstitial Defect ◽

Long Time ◽

Computational Materials ◽

Dynamics Simulations

AbstractThe simulation of defect dynamics and evolution is a technologicaly relevant challenge for computational materials science. The diffusion of small defects in silicon unfolds as a sequence of structural transitions. The relative infrequency of transition events requires simulation over extremely long time scales. We simulate the diffusion of small interstitial clusters (I1, I2, I3) for a range of temperatures using large-scale molecular dynamics (MD) simulations with a realistic tight-binding potential. A total of 0.25 μ sec of simulation time is accumulated for the study. A novel real-time multiresolution analysis (RTMRA) technique extracts stable structures directly from the dynamics without structural relaxation. The discovered structures are relaxed to confirm their stability.

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Study on Lightweight and Strengthening Effect of Carbon Nanotube in Highly Ordered Nanoporous Nickel: A Molecular Dynamics Study

Applied Sciences ◽

10.3390/app9020352 ◽

2019 ◽

Vol 9 (2) ◽

pp. 352 ◽

Cited By ~ 1

Author(s):

Yu Zhou ◽

Wu-Gui Jiang ◽

Duo-Sheng Li ◽

Qing-Hua Qin

Keyword(s):

Mechanical Properties ◽

Molecular Dynamics ◽

Carbon Nanotube ◽

Tensile Modulus ◽

Md Simulations ◽

Absorption Capacity ◽

Atomic Scale ◽

Strengthening Effect ◽

Pore Collapse ◽

Out Of Plane

The mechanical behavior of nanocomposites consisting of highly ordered nanoporous nickel (HONN) and its carbon nanotube (CNT)-reinforced composites (CNHONNs) subjected to a high temperature of 900 K is investigated via molecular dynamics (MD) simulations. The study indicates that, out-of-plane mechanical properties of the HONNs are generally superior to its in-plane mechanical properties. Whereas the CNT shows a significant strengthening effect on the out-of-plane mechanical properties of the CNHONN composites. Compared to pure HONNs, through the addition of CNTs from 1.28 wt‰ to 5.22 wt‰, the weight of the composite can be reduced by 5.83‰ to 2.33% while the tensile modulus, tensile strength, compressive modulus and compressive strength can be increased by 2.2% to 8.8%, 1% to 5.1%, 3.6% to 10.2% and 4.9% to 10.7%, respectively. The energy absorption capacity can also be improved due to the existence of CNTs. Furthermore, the MD simulations provide further insights into the deformation mechanism at the atomic scale, including fracture in tension, pore collapse in compression and local changes in lattice structures due to stacking faults.

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