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Computing free energies of fold-switching proteins using MELD x MD

Authors: Sridip Parui; Emiliano Brini; Ken A. Dill;

Computing free energies of fold-switching proteins using MELD x MD

Abstract

In this Zenodo repository, we provide the MELD script and data for a few representative systems in the DP-MELD.zip, GA_GB-MELD.zip, and RfaH-MELD.zip files. The contents of the repository are described below: MELD Simulation: Filename: DP-MELD.zip, GA_GB-MELD.zip, and RfaH-MELD.zip Description: These archives contain the necessary files and scripts for the MELD simulation. Setup Script: Filename: setup.py Description: This script is used to set up the MELD simulation. Trajectory Analysis Script: Filename: Clustering.sh Description: This script analyzes the trajectories obtained from the MELD simulation. Protein Information: Location: TEMPLATES folder Files: Protein topology file: .top Coordinate file: .crd PDB file: .PDB Description: These files provide input information for a few representative proteins. Residue-Residue Contact Information: Files: contact_model1.dat contact_model2.dat Description: These files contain information about the contacts between residues. Replica Trajectory Files: Filename: trajectory.00.dcd Description: These files contain the trajectories obtained from the simulation for the corresponding bottom replica. Clustering Output: Folders: Cluster_6 or Cluster_3.5 Description: These folders contain the results of the clustering analysis, including the computed population and the average conformers for each cluster. Furthermore, we provide an additional archive called unfold.zip: Unfolded Ensemble: Filename: unfold.zip Description: This archive contains the unfolded ensemble, which is used to determine the force required for rebalancing two group springs for the MELD run between two conformers (A and B) before executing the MELD simulation.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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