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# Markov state models of Folding@home COVID19 simulations In response to the COVID19 pandemic, [Folding@home]( users ran simulations of strategic value for understanding the molecular mechanisms of SARS-CoV-2 and its pathology. For detailed information on this dataset and core highlights, please view our manuscript, ["SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome"]( ## Data Format Systems are listed by organism followed by protein. Within each entry there is a `README.dat` for metadata on Markov State Model construction. ### Markov State Model The Markov State Model is contained within the `model` directory, which contains: - `tprobs.npy` - Transition probability matrix in numpy binary format - `tcounts.npy` - Transition counts matrix in numpy binary format - `prot_masses.pdb` - Protein topology file in PDB format - `populations.npy` - Equilibrium populations for defined states - `full_centers.xtc` - Representative cluster centers stored as a GROMACS compressed XTC file ### cryptic pockets Each system was assesed for cryptic pockets with analysis listed in the `cryptic_pockets` directory. The residues that comprise a cryptic pocket are provided in the text file `pocketXX_resis.dat`, where XX denotes the pocket numbeer. Additionally, cluster centers are ranked by size of cryptic pocket opening and sorted from highest to lowest. `pocketXX_rankings.dat` contains this sorted list, where the first entry is the cluster center with the largest pocket opening, and the last entry is the cluster centers with the smallest pocket opening. ## loading models and data Cluster centers can be visualized using [VMD]( ``` > vmd prot_masses.pdb full_centers.xtc ``` Additionally, structural analysis can be performed in python using the package [MDTraj]( ``` import mdtraj as md # load trajectory trj = md.load("full_centers.xtc", top="prot_masses.pdb") # compute distances between atom indices 0 and 1 distances = md.compute_distances(trj, atom_pairs=[[0, 1]]) # save each cluster center as a pdb for state_num in range(trj.n_frames): trj[state_num].save_pdb("state%d.pdb" % state_num) ```
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