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Supporting data for the paper:

"Targeting the intrinsically disordered AR-NTD through a machine learn-ing-based enhanced sampling workflow"

Nature Communications Workflow Overview

The training of the models was based on the mlcolvar library.

The REST3 tutorial is available here: https://github.com/mdlab-um/REST3_tutorial.

The SWISH tutorial is available here: https://github.com/Gervasiolab/Gervasio-Protein-Dynamics/tree/master/swish_bootcamp.

All molecular dynamics simulations were conducted using GROMACS 2022.6, integrated with the PLUMED 2.9.0 plugin and the PyTorch 2.2.1 library.

Repo structure

The contents of the repository are organized as follows:

tau5/REST3 : files for REST3 simulations of tau5

  • 0-7: input files for REST simulations
  • setup: script for scaling the top Hamiltonian
  • mlcv: frozen torchscript models for AE CV

tau5/swish : files for SWISH simulations of tau5

  • 1-9: input files for SWISH simulations

tau5/FES : files for Binding FES simulations of tau5

  • 1-9: input files for Binding FES simulations
  • 1-9/states Structural files of the free-energy minima conformations

tau5/a99SBdisp.ff : files for the force field of intrinsically disordered proteins of tau5

c-myc/REST3 : files for REST3 simulations of c-myc

tau5/swish : files for SWISH simulations of c-myc

tau5/FES : files for Binding FES simulations of c-myc

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