Single molecule fluorescence resonance energy transfer (smFRET) experiments probe molecular distances on the nanometer scale. In such experiments, distances are recorded from FRET transfer efficiencies via the Förster formula, E=1/(1+(R/R0)6).The energy transfer however also depends on the mutual orientation of the two dyes used as distance reporter. Since this information is typically inaccessible in FRET experiments, one has to rely on approximations, which reduce the accuracy of these distance measurements. A common approximation is an isotropic and uncorrelated dye orientation distribution.To assess the impact of such approximations, we present the algorithms and implementation of a computational toolkit for the simulation of smFRET on the basis of molecular dynamics (MD) trajectory ensembles. In this study, the dye orientation dynamics, which are used to determine dynamic FRET efficiencies, are extracted from MD simulations. In a subsequent step, photons and bursts are generated using a Monte Carlo algorithm.The application of the developed toolkit on a poly-proline system demonstrated good agreement between smFRET simulations and experimental results and therefore confirms our computational method. Furthermore, it enabled the identification of the structural basis of measured heterogeneity.The presented computational toolkit is written in Python, available as open-source, applicable to arbitrary systems and can easily be extended and adapted to further problems. Program summaryProgram title: md2fretCatalogue identifier: AENV_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENV_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GPLv3, the bundled SIMD friendly Mersenne twister implementation [1] is provided under the SFMT-License.No. of lines in distributed program, including test data, etc.: 317880No. of bytes in distributed program, including test data, etc.: 54774217Distribution format: tar.gzProgramming language: Python, Cython, C (ANSI C99).Computer: Any (see memory requirements).Operating system: Any OS with CPython distribution (e.g. Linux, MacOSX, Windows).Has the code been vectorised or parallelized?: Yes, in Ref. [2], 4 CPU cores were used.RAM: About 700MB per process for the simulation setup in Ref. [2].Classification: 16.1, 16.7, 23.External routines: Calculation of Rκ2-trajectories from GROMACS [3] MD trajectories requires the GromPy Python module described in Ref. [4] or a GROMACS 4.6 installation. The md2fret program uses a standard Python interpreter (CPython) v2.6+ and < v3.0 as well as the NumPy module. The analysis examples require the Matplotlib Python module.Nature of problem:Simulation and interpretation of single molecule FRET experiments.Solution method:Combination of force-field based molecular dynamics (MD) simulating the dye dynamics and Monte Carlo sampling to obtain photon statistics of FRET kinetics.Additional comments:!!!!! The distribution file for this program is over 50 Mbytes and therefore is not delivered directly when download or Email is requested. Instead a html file giving details of how the program can be obtained is sent. !!!!!Running time:A single run in Ref. [2] takes about 10 min on a Quad Core Intel Xeon CPU W3520 2.67GHz with 6GB physical RAM
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