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<h1>Markov state models of Folding@home COVID19 simulations</h1> <p>In response to the COVID19 pandemic, <a href="https://foldingathome.org/" rel="nofollow">Folding@home</a> 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, <a href="https://www.biorxiv.org/content/10.1101/2020.06.27.175430v3" rel="nofollow">"SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome"</a>.</p> <h2>Data Format</h2> <p>Systems are listed by organism followed by protein. Within each entry there is a <code>README.dat</code> for metadata on Markov State Model construction.</p> <h3>Markov State Model</h3> <p>The Markov State Model is contained within the <code>model</code> directory, which contains:</p> <ul> <li><code>tprobs.npy</code> - Transition probability matrix in numpy binary format</li> <li><code>tcounts.npy</code> - Transition counts matrix in numpy binary format</li> <li><code>prot_masses.pdb</code> - Protein topology file in PDB format</li> <li><code>populations.npy</code> - Equilibrium populations for defined states</li> <li><code>full_centers.xtc</code> - Representative cluster centers stored as a GROMACS compressed XTC file</li> </ul> <h3>cryptic pockets</h3> <p>Each system was assesed for cryptic pockets with analysis listed in the <code>cryptic_pockets</code> directory.</p> <p>The residues that comprise a cryptic pocket are provided in the text file <code>pocketXX_resis.dat</code>, where XX denotes the pocket numbeer. Additionally, cluster centers are ranked by size of cryptic pocket opening and sorted from highest to lowest. <code>pocketXX_rankings.dat</code> 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.</p> <h2>loading models and data</h2> <p>Cluster centers can be visualized using <a href="https://www.ks.uiuc.edu/Research/vmd/" rel="nofollow">VMD</a>:</p> <pre class="highlight"><code>&gt; vmd prot_masses.pdb full_centers.xtc</code></pre> <p>Additionally, structural analysis can be performed in python using the package <a href="http://mdtraj.org/1.9.3/" rel="nofollow">MDTraj</a></p> <pre class="highlight"><code>import mdtraj as md # load trajectory trj = md.load(&quot;full_centers.xtc&quot;, top=&quot;prot_masses.pdb&quot;) # 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(&quot;state%d.pdb&quot; % state_num)</code></pre>
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