Kateřina Tomášková¹, Dalibor Trapl^1*, Vojtěch Spiwok^1*

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:j9-0n [version v3]

Publication date: Jan 26, 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 2021.22 (2021), doi: 10.24435/materialscloud:j9-0n.

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.

Materials Cloud sections using this data

Files

File name	Size	Description
rosetta_mtd.zip MD5md5:54d65f663b98755791d10b44c0a7ed94	12.5 MiB	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.
README.txt MD5md5:2fb78e3d844c6b59a6bee5e86c4960cf	2.3 KiB	README.txt file

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

Keywords

metadynamics machine learning protein folding scoring function collective variable