Symplectic Molecular Dynamics Simulations on Specially Designed Parallel Computers

Borštnik, Urban; Janežič, Dušanka

doi:10.1021/ci050216q

Cited by 20 publications

(19 citation statements)

References 31 publications

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“…d) Because every interaction has two equal but opposite forces, only one of them needs to be calculated. Force f 3 can be calculated as f 31…”

Section: The Force Decomposition Methodsmentioning

confidence: 99%

“…28 However, even clusters of PCs are flexible enough to allow adapting the parallel computer to the parallel method being utilized. 29–31 An example of this is the recent use of Graphics Processing Units (GPUs) to speed up MD. 32 However, taking full advantage of GPUs can require adapting the necessary algorithms to the hardware being utilized.…”

Section: Introductionmentioning

confidence: 99%

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The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

et al. 2011

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Parallelization is an effective way to reduce the computational time needed for molecular dynamics simulations. We describe a new parallelization method, the distributed-diagonal force decomposition method, with which we extend and improve the existing force decomposition methods. Our new method requires less data communication during molecular dynamics simulations than replicated data and current force decomposition methods, increasing the parallel efficiency. It also dynamically load-balances the processors' computational load throughout the simulation. The method is readily implemented in existing molecular dynamics codes and it has been incorporated into the CHARMM program, allowing its immediate use in conjunction with the many molecular dynamics simulation techniques that are already present in the program. We also present the design of the Force Decomposition Machine, a cluster of personal computers and networks that is tailored to running molecular dynamics simulations using the distributed diagonal force decomposition method. The design is expandable and provides various degrees of fault resilience. This approach is easily adaptable to computers with Graphics Processing Units because it is independent of the processor type being used.

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“…d) Because every interaction has two equal but opposite forces, only one of them needs to be calculated. Force f 3 can be calculated as f 31…”

Section: The Force Decomposition Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

et al. 2011

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“…Because the studied alkylation reactions do not take place in vacuo, solvation effects were incorporated by the self-consistent reaction field (SCRF) method of Tomasi et al [30] and the Langevin dipole (LD) model of Florian and Warshel [31]. Quantum-mechanical calculations of activation free energies (∆G ‡ ) were performed with the Gaussian 16 on the CROW cluster located at the National Institute of Chemistry in Ljubljana [32,33]. To evaluate computational results, the obtained ∆G ‡ for the reactions of AFBO with XN, and its derivatives IXN, 8-PN, and 6-PN were compared to the experimental ∆G ‡ barriers for the reaction of AFBO with guanine.…”

Section: Computational Analysis Of Alkylation Reactions Between Xn Ixn 8-pn and 6-pn And The Carcinogenic Metabolite Of Afb1-afbomentioning

confidence: 99%

Chemoprotective Effects of Xanthohumol against the Carcinogenic Mycotoxin Aflatoxin B1

Štern

Furlan

Novak

et al. 2021

Foods

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The present study addresses the chemoprotective effects of xanthohumol (XN), a prenylated flavonoid found in the female inflorescences (hops) of the plant Humulus lupulus L., against the carcinogenic food contaminant aflatoxin B1 (AFB1). The chemical reactions of XN and its derivatives (isoxanthohumol (IXN), 8-prenylnaringenin (8-PN), and 6-prenylnaringenin (6-PN)) with the AFB1 metabolite, aflatoxin B1 exo-8,9-epoxide (AFBO), were investigated in silico, by calculating activation free energies (ΔG⧧) at the Hartree–Fock level of theory in combination with the 6-311++G(d,p) basis set and two implicit solvation models. The chemoprotective effects of XN were investigated in vitro in the metabolically competent HepG2 cell line, analyzing its influence on AFB1-induced cytotoxicity using the MTS assay, genotoxicity using the comet and γH2AX assays, and cell cycle modulation using flow cytometry. Our results show that the ΔG⧧ required for the reactions of XN and its derivatives with AFBO are comparable to the ΔG⧧ required for the reaction of AFBO with guanine, indicating that XN, IXN, 8-PN, and 6-PN could act as scavengers of AFBO, preventing DNA adduct formation and DNA damage induction. This was also reflected in the results from the in vitro experiments, where a reduction in AFB1-induced cytotoxicity and DNA single-strand and double-strand breaks was observed in cells exposed to combinations of AFB1 and XN, highlighting the chemoprotective effects of this phytochemical.

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“…Carvalho et al [10] implemented the MPI-based parallel simulation algorithm for Monte Carlo simulation of a molecular system and they enhanced the speed by two-times with four processors. In 2005, Borstnik et al [11] developed a parallel computing program for molecular dynamics (MD) simulation and implemented the split integration symplectic method (SISM). In their approach, the high-frequency vibrational motion corresponding to bonding interactions is treated analytically.…”

Section: Introductionmentioning

confidence: 99%

Parallel implementation of finite volume based method for isoelectric focusing

2009

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A message passing interface (MPI) based parallel simulation algorithm is developed to simulate protein behavior in non-linear isoelectric focusing (IEF). The mathematical model of IEF is formulated based on mass conservation, charge conservation, ionic dissociation-association relations of amphoteric molecules and the electroneutrality condition. First, the concept of parallelism is described for isoelectric focusing, and the isoelectric focusing model is implemented for 96 components: 94 ampholytes and 2 proteins. The parallelisms were implemented for two equations (mass conservation equation and electroneutrality equation). The CPU times are presented according to the increase of the number of processors (1, 2, 4 and 8 nodes). The maximum reduction of CPU time was achieved when four CPUs were employed, regardless of the input components in isoelectric focusing. The speed enhancement was defined for comparison of parallel efficiency. Computational speed was enhanced by maximum of 2.46 times when four CPUs were used with 96 components in isoelectric focusing.

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Symplectic Molecular Dynamics Simulations on Specially Designed Parallel Computers

Cited by 20 publications

References 31 publications

The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

Chemoprotective Effects of Xanthohumol against the Carcinogenic Mycotoxin Aflatoxin B1

Parallel implementation of finite volume based method for isoelectric focusing

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