Sampling enhancement by metadynamics driven by machine learning and de novo protein modelling
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{
"created": "2021-01-26T15:31:56.941559+00:00",
"revision": 3,
"metadata": {
"doi": "10.24435/materialscloud:j9-0n",
"references": [
{
"type": "Journal reference",
"comment": "Manuscript to be submitted where the input data were used.",
"citation": "K. Tom\u00e1\u0161kov\u00e1, D. Trapl, V. Spiwok (in preparation)"
}
],
"_oai": {
"id": "oai:materialscloud.org:733"
},
"keywords": [
"metadynamics",
"machine learning",
"protein folding",
"scoring function",
"collective variable"
],
"is_last": true,
"publication_date": "Jan 26, 2021, 18:12:18",
"owner": 299,
"license_addendum": null,
"contributors": [
{
"givennames": "Kate\u0159ina",
"familyname": "Tom\u00e1\u0161kov\u00e1",
"affiliations": [
"Department of Biochemistry and Microbiology, Universiy of Chemistry and Technology, Prague, Czech Republic"
]
},
{
"givennames": "Dalibor",
"email": "traplda@vscht.cz",
"familyname": "Trapl",
"affiliations": [
"Department of Biochemistry and Microbiology, Universiy of Chemistry and Technology, Prague, Czech Republic"
]
},
{
"givennames": "Vojt\u011bch",
"email": "spiwokv@vscht.cz",
"familyname": "Spiwok",
"affiliations": [
"Department of Biochemistry and Microbiology, Universiy of Chemistry and Technology, Prague, Czech Republic"
]
}
],
"description": "Folding of villin miniprotein was studied by parallel tempering metadynamics driven by machine learning. To obtain a training set for machine learning, we generated a large series of structures of the protein by the de novo protein structure prediction package Rosetta. A neural network was trained to approximate the Rosetta score. Parallel tempering metadynamics driven by this approximated Rosetta score successfully predicted the native structure and the free energy surface of the studied system.\nThese files make it possible to rerun all simulations. The directory METAD contains input files for metadynamics (no folding events observed). The directory PT-METAD contains input files for parallel tempering metadynamics. All simulations were done using Gromacs 2016.4, Anncolvar 0.8, Plumed 2.4 and OpenMPI 4.0.0.",
"title": "Sampling enhancement by metadynamics driven by machine learning and de novo protein modelling",
"edited_by": 100,
"license": "Creative Commons Attribution 4.0 International",
"id": "733",
"_files": [
{
"key": "rosetta_mtd.zip",
"description": "Input files needed to reproduce simulations using Gromacs 2016.4, Anncolvar 0.8, Plumed 2.4 and OpenMPI 4.0.0. Uncomment commented METAD and PRINT lines in plumed.dat file for a production run.",
"size": 13136399,
"checksum": "md5:54d65f663b98755791d10b44c0a7ed94"
},
{
"key": "README.txt",
"description": "README.txt file",
"size": 2345,
"checksum": "md5:2fb78e3d844c6b59a6bee5e86c4960cf"
}
],
"mcid": "2021.22",
"version": 3,
"status": "published",
"conceptrecid": "724"
},
"updated": "2021-12-06T13:33:05.597564+00:00",
"id": "733"
}