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Sampling enhancement by metadynamics driven by machine learning and de novo protein modelling

Kateřina Tomášková1, Dalibor Trapl1*, Vojtěch Spiwok1*

1 Department of Biochemistry and Microbiology, Universiy of Chemistry and Technology, Prague, Czech Republic

* Corresponding authors emails: traplda@vscht.cz, spiwokv@vscht.cz
DOI10.24435/materialscloud:f5-fe [version v1]

Publication date: Jan 25, 2021

How to cite this record

Kateřina Tomášková, Dalibor Trapl, Vojtěch Spiwok, Sampling enhancement by metadynamics driven by machine learning and de novo protein modelling, Materials Cloud Archive 2021.16 (2021), https://doi.org/10.24435/materialscloud:f5-fe

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. These 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.

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Files

File name Size Description
README.txt
MD5md5:a5a2c25ea5769ab01abe23c5dfe0f43b
2.0 KiB README.txt file
rosetta_mtd.tar.gz
MD5md5:9c385d9f3933d17e56828eef2f622235
12.6 MiB Input files needed to reproduce simulations using Gromacs 2016.4, Anncolvar 0.8, Plumed 2.4 and OpenMPI 4.0.0.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Manuscript to be submitted where the input data were used.)
K. Tomášková, D. Trapl, V. Spiwok (in preparation)

Keywords

metadynamics machine learning protein folding scoring function collective variable

Version history:

2021.22 (version v3) Jan 26, 2021 DOI10.24435/materialscloud:j9-0n
2021.19 (version v2) Jan 26, 2021 DOI10.24435/materialscloud:je-7c
2021.16 (version v1) [This version] Jan 25, 2021 DOI10.24435/materialscloud:f5-fe