Systematic Comparison of Empirical Forcefields for Molecular Dynamic Simulation of Insulin

Todorova, Nevena; Legge, Fiona S.; Treutlein, Herbert; Yarovsky, Irene

doi:10.1021/jp076825d

Cited by 55 publications

(54 citation statements)

References 66 publications

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“…Aliev and Courtier-Murias also found a strong dependence of the structure of small open chain peptides on the force field used [78]. A similar result was found in simulations of insulin using CHARMM, AMBER, OPLS, and GROMOS, as different structural trends are favoured depending on the force field chosen [79]. One of the most detailed evaluation of existing potentials has been performed by Paton and Goodman.…”

Section: Popular Force Fieldssupporting

confidence: 56%

Force fields and molecular dynamics simulations

González

2011

JDN

187

114

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This paper reviews the basic concepts needed to understand Molecular Dynamics simulations and will hopefully serve as an introductory guide for the non-expert into this exciting topic.Abstract. The objective of this review is to serve as an introductory guide for the non-expert to the exciting field of Molecular Dynamics (MD). MD simulations generate a phase space trajectory by integrating the classical equations of motion for a system of N particles. Here I review the basic concepts needed to understand the technique, what are the key elements to perform a simulation and which is the information that can be extracted from it. I will start defining what is a force field, which are the terms composing a classical force field, how the parameters of the potential are optimized, and which are the more popular force fields currently employed and the lines of research to improve them. Then the Molecular Dynamics technique will be introduced, including a general overview of the main algorithms employed to integrate the equations of motion, compute the long-range forces, work on different thermodynamic ensembles, or reduce the computational time. Finally the main properties that can be computed from a MD trajectory are briefly introduced.

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Section: Popular Force Fieldssupporting

confidence: 56%

Force fields and molecular dynamics simulations

González

2011

JDN

187

114

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“…This widely used forcefield has been tested against NMR-spectroscopic data in case of the lysozyme protein in water by Soares et al 66 and has been found to reproduce its solution structure and conformational behavior very well. In a recent work, Todorova et al 67 performed extensive MD simulations on the 51-amino-acid protein insulin and subjected the GROMOS96-43A1 forcefield to a systematic comparison against other popular biomolecular forcefields, including the CHARMM27-, AMBER03-, OPLS-, and GROMOS96-53A6-forcefields. They analyzed in detail the effect of each forcefield on the conformational evolution and structural properties of the protein and compared the results with the available experimental data.…”

Section: B Simulation Details and System Preparation 1 Simulation Dmentioning

confidence: 99%

A novel computer simulation method for simulating the multiscale transduction dynamics of signal proteins

Peter

Dick

Baeurle

2012

The Journal of Chemical Physics

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Signal proteins are able to adapt their response to a change in the environment, governing in this way a broad variety of important cellular processes in living systems. While conventional moleculardynamics (MD) techniques can be used to explore the early signaling pathway of these protein systems at atomistic resolution, the high computational costs limit their usefulness for the elucidation of the multiscale transduction dynamics of most signaling processes, occurring on experimental timescales. To cope with the problem, we present in this paper a novel multiscale-modeling method, based on a combination of the kinetic Monte-Carlo-and MD-technique, and demonstrate its suitability for investigating the signaling behavior of the photoswitch light-oxygen-voltage-2-Jα domain from Avena Sativa (AsLOV2-Jα) and an AsLOV2-Jα-regulated photoactivable Rac1-GTPase (PA-Rac1), recently employed to control the motility of cancer cells through light stimulus. More specifically, we show that their signaling pathways begin with a residual re-arrangement and subsequent H-bond formation of amino acids near to the flavin-mononucleotide chromophore, causing a coupling between β-strands and subsequent detachment of a peripheral α-helix from the AsLOV2-domain. In the case of the PA-Rac1 system we find that this latter process induces the release of the AsLOV2-inhibitor from the switchII-activation site of the GTPase, enabling signal activation through effector-protein binding. These applications demonstrate that our approach reliably reproduces the signaling pathways of complex signal proteins, ranging from nanoseconds up to seconds at affordable computational costs.

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“…This widely used forcefield has been tested against NMR spectroscopic data in case of the hen egg white globular protein lysozyme in water by Soares et al 27 and has been found to reproduce its solution structure and conformational behaviour very well. In a recent work, Todorova et al 28 performed extensive MD simulations on the 51-amino-acid protein, insulin, and subjected the GROMOS43A1 forcefield to a systematic comparison against other popular biomolecular forcefields, including the CHARMM27-, AMBER03-, OPLS-and GROMOS53A6 forcefields. They analysed in detail the effect of each forcefield on the conformational evolution and structural properties of the protein and compared the results with the available experimental data.…”

Section: Simulation Detailsmentioning

confidence: 99%

Mechanism of signal transduction of the LOV2-Jα photosensor from Avena sativa

2010

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Fusion proteins containing blue-light-activable protein domains possess great potential as molecular switches in cell signalling. This has recently been impressively demonstrated by connecting the light oxygen voltage LoV2-Jα-protein domain of A. sativa (AsLoV2-Jα) with the Rac1-GTPase, responsible for regulating the morphology and motility of metazoan cells. However, a target-oriented development of fusion proteins in conjunction with this photosensor is still very challenging, because a detailed understanding of its signal transduction pathway on a molecular level is still lacking. Here, we show through molecular dynamics simulation that, after formation of the cysteinyl-flavin mononucleotide (Fmn) adduct, the signalling pathway begins with a rotational reorientation of the residue glutamine 1029 adjacent to the Fmn chromophore, transmitting stress through the Iβ strand towards the LoV2-Jα interface. This then results in the breakage of two H-bonds, namely, glutamic acid 1034-Gln995 and aspartic acid (Asp) 1056-Gln1013, at opposite sides of the interface between the Jα helix and the LoV2 domain, ultimately leading to a disruption of Jα helix from the LoV2 core.

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Systematic Comparison of Empirical Forcefields for Molecular Dynamic Simulation of Insulin

Cited by 55 publications

References 66 publications

Force fields and molecular dynamics simulations

Force fields and molecular dynamics simulations

A novel computer simulation method for simulating the multiscale transduction dynamics of signal proteins

Mechanism of signal transduction of the LOV2-Jα photosensor from Avena sativa

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