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

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.