DOI: 10.5061/dryad.cc2fqz6h7
title: A Discard-and-Restart MD algorithm for the sampling of protein intermediate states

description: We introduce a Discard‑and‑Restart molecular dynamics (MD) algorithm
 tailored for the sampling of realistic protein intermediate states. It
 aids computational structure‑based drug discovery by reducing the
 simulation times to compute a "quick sketch" of folding pathways
 by up to 2000x. The algorithm iteratively performs short MD simulations
 and measures their proximity to a target state via a collective variable
 (CV) loss, which can be defined in a flexible fashion, locally or
 globally.  Using the loss, if the trajectory proceeds toward the
 target, the MD simulation continues. Otherwise, it is discarded, and a new
 MD simulation is restarted, with new initial velocities randomly drawn
 from a Maxwell‑Boltzmann distribution. The discard‑and‑restart algorithm
 demonstrates efficacy and atomistic accuracy in capturing the folding
 pathways in several contexts: (1) fast‑folding small protein domains; (2)
 the folding intermediate of the prion protein PrP; and (3) the spontaneous
 partial unfolding of α‑Tubulin, a crucial event for microtubule severing.
 During each iteration of the algorithm, we can perform AI-based analysis
 of the transitory conformations to find potential binding pockets, which
 could represent druggable sites. Overall, our algorithm enables systematic
 and computationally efficient exploration of conformational landscapes,
 enhancing the design of ligands targeting dynamic protein states.
description: # A Discard-and-Restart MD algorithm for the sampling of protein
 intermediate states This README file was generated by Alan Ianeselli on
 Feb 27th, 2025 ## DOI 10.5061/dryad.cc2fqz6h7 ## Date of Data Collection
 2023 - 2024 ## Contributors Alan Ianeselli, Yale University, New Haven CT,
 USA Joe Howard, Yale University, New Haven CT, USA Mark B Gerstein, Yale
 University, New Haven CT, USA ## Summary This repository is integral to
 our research paper, and contains the data that support the findings of our
 study in order to ensure transparency and reproducibility. It includes the
 frames and trajectories generated using the discard-and-restart (D&R)
 algorithm as well as the Python script and model files. ## Description of
 the data and file structure The data for each protein is contained in the
 respective folder (beta_hairpin, Fip35, Prion, TrpCage_explicit,
 TrpCage_implicit, Tubulin, Villin_explicit, Villin_implicit).
 Pca_model.pkl is the file for the pca model used to calculate the CV for
 folding; run_folding.py corresponds to the Python script for the D&R
 algorithm; utilities.py contains functions used for the D&R algorithm.
 The following libraries are needed to run the Python script: numpy,
 joblib, mdtraj, sklean, matplotlib, termcolor. It is also necessary to
 have a local installation of GROMACS (&gt;2021 for explicit solvent, 4.6.5
 for implicit solvent). In every folder, information about the native state
 is contained in the folder "NATIVE_FILES", which contains: -the
 energy-minimized structure (em.pdb) -the distogram .pkl file output of
 AlphaFold 2 -the atom indexes used to calculate the CA dihedrals
 (c_index_array.ndx) -the subfolder "data" contains the rmsd
 value of the native structure (rmsd.xvg), the distance matrix between CA
 (mindist.xvg) used to calculate the contact maps. The single trajectories
 are contained in the folders with the name "run#". Each
 "run#" folder contains: -the structure's topology
 (topol.top) -the molecular dynamics protocol (md.mdp) -the CV values of
 the individual short simulations (figure#.txt, figure#.png)
 -OUTPUT_RC.txt, which contains the value of various collective variables
 of the selected configurations (respectively: iteration #, pca comp 0, pca
 comp 1, # of attempts, CA_dih_difference, z, alphafold CV, rmsd)
 -subfolder full_traj/ which contains the frames of the selected
 configurations together with the concatenation (concatenated.pdb)
 -subfolder step_#, which contains the short trajectory and its rmsd over
 time and CA-CA distances over time in the data/ subfolder ## Data formats
 *.pdb , coordinates and atom types of the structures *.xtc , compressed
 coordinate file *.txt , raw data files *.png , image files of the plots
 *.py , Python scripts *.pkl , Python pickle format *.top , GROMACS
 topology file *.mdp , GROMACS protocol file *.xvg , data produced by
 GROMACS *.ndx , index file produced by GROMACS ## Usage, Compatibility,
 and Accessibility We provided the data in their original formats. Other
 researchers are encouraged to use these data for replicating the study, to
 perform further analyses or research and to ensure the reliability of our
 results. Researchers are welcome to contact the authors for further
 assistance with data conversion, analysis and explanation.