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Manuscript Title: In silico FRET from simulated dye dynamics
Authors: Martin Hoefling, Helmut Grubmüller
Program title: md2fret
Catalogue identifier: AENV_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 184(2013)841
Programming 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].
Keywords: Fluorescence Resonance Energy Transfer, Molecular Conformation, Monte Carlo, Molecular Dynamics, Markov Chain, Single Molecule.
Classification: 16.1, 16.7, 23.

External routines: Calculation of Rk2-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.

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

[1] M. Saito, M. Matsumoto, SIMD-oriented fast mersenne twister: a 128-bit pseudorandom number generator, in: A. Keller, S. Heinrich, H. Niederreiter (Eds.), Monte Carlo and Quasi-Monte Carlo Methods 2006, Springer Berlin Heidelberg, 2008, pp. 607-622.
[2] M. Hoefling, N. Lima, D. Hänni, B. Schuler, C. A. M. Seidel, H. Grubmüller, Structural heterogeneity and quantitative FRET efficiency distributions of polyprolines through a hybrid atomistic simulation and monte carlo approach, PLoS ONE 6 (5) (2011) e19791.
[3] D. V. D. Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark, H. J. C. Berendsen, GROMACS: fast, flexible, and free., J Comput Chem 26 (16) (2005) 1701-1718.
[4] R. Pool, A. Feenstra, M. Hoefling, R. Schulz, J. C. Smith, J. Heringa, Enabling grand-canonical monte carlo: Extending the flexibility of gromacs through the GromPy Python interface module, Journal of Chemical Theory and Computation 33 (12) (2012) 1207-1214.