GROMOS++ Software for the Analysis of Biomolecular Simulation Trajectories

Eichenberger, Andreas P.; Allison, Jane R.; Dolenc, Jožica; Geerke, Daan P.; Horta, Bruno A. C.; Meier, Katharina; Oostenbrink, Chris; Schmid, Nathan; Steiner, Denise; Wang, Dongqi; Gunsteren, Wilfred F. van

doi:10.1021/ct2003622

Cited by 190 publications

(249 citation statements)

References 57 publications

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“…(15), which might adopt nonzero values because V ele not only contains terms due to the φ i , the first two terms on the right in eq. (13), but also due to the φ j , the last two terms on the right in eq.…”

Section: Journal Of Computational Chemistrymentioning

confidence: 99%

“…The architecture and implementation of the GROMOS C++ code is described elsewhere, [12] as are features with regard to biomolecular structure refinement based on experimental NMR or X-ray data, [13] to the computation of relative free energies, [14] and the analysis of MD trajectories using GROMOS. [15] The new simulation functionalities are illustrated using examples.…”

Section: Introductionmentioning

confidence: 99%

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New functionalities in the GROMOS biomolecular simulation software

Kunz¹,

Allison²,

Geerke³

et al. 2011

J Comput Chem

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101

103

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Since the most recent description of the functionalities of the GROMOS software for biomolecular simulation in 2005 many new functions have been implemented. In this article, the new functionalities that involve modified forces in a molecular dynamics (MD) simulation are described: the treatment of electronic polarizability, an implicit surface area and internal volume solvation term to calculate interatomic forces, functions for the GROMOS coarse-grained supramolecular force field, a multiplicative switching function for nonbonded interactions, adiabatic decoupling of a number of degrees of freedom with temperature or force scaling to enhance sampling, and nonequilibrium MD to calculate the dielectric permittivity or viscosity. Examples that illustrate the use of these functionalities are given.

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Section: Journal Of Computational Chemistrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New functionalities in the GROMOS biomolecular simulation software

Kunz¹,

Allison²,

Geerke³

et al. 2011

J Comput Chem

Self Cite

101

103

View full text Add to dashboard Cite

show abstract

“…The calculation of the most stable 1000 conformers was carried out based on the topology of building blocks and structural constraints derived from NMR experiments. Analysis and clustering of the obtained conformations were carried out with GROMOS [32] and GROMACS 4.5.5 [33] analyzing tools. Cluster analysis was based on analysis of the torsion angles of the backbone with a cutoff of ±30°.…”

Section: Structure Calculationsmentioning

confidence: 99%

The structure of cyclolinopeptide A in chloroform refined by RDC measurements

Huben

Jewgiński

Pabis³

et al. 2014

Journal of Peptide Science

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Three-dimensional structures of molecules traditionally assigned from nuclear Overhauser effects and vicinal coupling constants are recently complemented by measurements of residual dipolar couplings. Residual dipolar couplings measured in a stretched poly(dimethylsiloxane) gel were used to determine the structure of cyclolinopeptide A in chloroform solution at -50 °C. After structure refinement, conformational details of main cluster were discussed in relation to crystal and nuclear Overhauser effect derived structures.

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“…The block averaging method has been implemented in many MD analysis packages [67][68][69] , however there is no common method for computing the value at which BSE(n) has converged. For example, the Python MDAnalysis package 68 relies on human inspection of the curve to determine convergence, the GROMOS analysis toolkit 67 uses a numerical derivative based method to identify a turning point, while the GROMACS package 69 includes a method developed by Hess 70 that performs a least squares fit of the BSE(n) curve to a particular functional form based on a number of assumptions e.g.…”

Section: Convergence Analysis Of a Time Series Averagementioning

confidence: 99%

“…improve the overall efficiency of the calculations, the energy for the reference state and the 100 alternative parameter combination HX were calculated simultaneously for each frame using m_ener from the GROMOS++ analysis library 67 . This has the advantage that the pairlist need only be generated once for each frame.…”

Section: Ssp Ensemble Generationmentioning

confidence: 99%

Improving the accuracy of molecular dynamics simulations: parameterisation of interaction potentials for small molecules

Stroet¹

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The ability to accurately describe molecular systems with classical molecular dynamics (MD) is critically dependent on an understanding of the sources of error in the design, evaluation and analysis of the underlying model. One significant source of error is in the degree to which the equations and parameters used to represent atomic interaction can reproduce the experimental properties relevant to a particular application. While highly optimised and well-validated interaction parameters are available for common biomolecules (amino acids, sugars, lipids etc.), this is not the case for heterogeneous small molecules such as co-factors, substrates and drugs. The enormity of the chemical space covered by small molecules necessitates the development of automated approaches to parameterising and validating interaction parameters. Several tools are available that can be used to generate interaction parameters for small molecules compatible with a particular existing (bio)molecular force field. Despite their popularity, many are poorly validated, while some have been shown to be inappropriate for many applications. In other cases, validation studies have demonstrated reasonable performance, however, they also suggest significant improvements can be made. One such tool is the Automated Topology Builder (ATB, http://atb.uq.edu.au/) which provides interaction parameters for small molecules compatible with the GROMOS biomolecular force field.As part of this work, a fully automated protocol for the calculation of solvation energy by thermodynamic integration has been developed and applied to the large-scale validation of the ATB against experimental solvation data in water and hexane. It is shown that the largest errors are due to the Lennard-Jones parameters used for functional groups not present in common biomolecules. Other sources of error included the atomic charges assigned by the ATB for the united atom carbons and incompatibilities between GROMOS Lennard-Jones parameters and the ATB charge model. Significant sources of error were also identified in the evaluation of MD models, both for the calculation of solvation energy as well as for MD simulations of lipid membranes. In particular, the use of multiple-time-step algorithms as a time-saving technique. For the calculation of solvation energy by TI, the twin-range cutoff scheme-commonly used in simulations with the GROMOS force field-was found to introduce a systematic error of about 1 kJ/mol. While for lipid systems, various III changes made to the integration algorithm of the GROMACS simulation code were found to significantly alter the properties of lipid membranes when simulated with identical force fields.Finally, a method for parameterising transferrable interaction potentials with respect to a wide range of target properties is presented. The method is based on an analysis of surfaces corresponding to the difference between calculated and target data as a function of alternative combinations of parameters.The consideration of surfaces in parameter space...

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GROMOS++ Software for the Analysis of Biomolecular Simulation Trajectories

Cited by 190 publications

References 57 publications

New functionalities in the GROMOS biomolecular simulation software

New functionalities in the GROMOS biomolecular simulation software

The structure of cyclolinopeptide A in chloroform refined by RDC measurements

Improving the accuracy of molecular dynamics simulations: parameterisation of interaction potentials for small molecules

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