Published March 8, 2023 | Version v3
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Data supporting "Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study"

  • 1. University of Milan, Department of Biosciences

Description

Simulation data and input files in support of the Manuscript:"Machine Learning-based Modeling of Olfactory Receptors: Human OR51E2 as a Case Study".

The archive is organized in 6 different folders:

1. 7x7_rmsd, which contains a tcl script (to be run in VMD) to compute the 7x7 RMSD matrix (see Wang et al. J Struct. Bio, (2017)).
2. a100_plumed, which contains PLUMED input files to compute A100 index on OR51E2 trajectories.
3. initial_structures, which contains the 6 different conformation for hOR51E2 obtained by the different predictors in the pdb format.
4. mdp_files, which contains the GROMACS mdp files to perform all the protocol described in the paper.
5. topologies, which contains the 6 different topologies (in .top format) and the initial conformation (in .gro format) for the hOR51E2 embedded in the membrane and solvated.
6. trajectories, which contains the 18 (6 systems, 3 replicas per system) different trajectories with the sodium in place close to D692.50 without solvent and ions (in .xtc format with a frame every 100 ps) and a reference conformation (in .gro format). Here we have also added the trajectory for the SwissModel-derived simulation without sodium ion in place, where we observed the ion binding. This last trajectory contains also solvent and ions, but with a lower printing frequency (1 ns).

Files

Files (13.7 GB)

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md5:0f15132a6a5d6c4c33c0f0937ad9bc77
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Additional details

Related works

Is supplement to
Preprint: 10.1101/2023.02.22.529484 (DOI)