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4 results for “Python Computation”
Computational results and python files for the work "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling"
<p><br> This repository contains data accompanying the paper "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling".</p> <p>The implementation is based on the python-interface of the NGSolve open source Finite Element library (ngsolve.org).</p> <p>The file solve_problem_allione.py represents a minimum working example where the proposed MCS/HDG (set the use_MCS flag) method is used to solve the problem from the numerics section of the paper.</p> <p>The files FlowTemplates.py and krylovspace_extension.py contain a somewhat larger and more modular implementation of the proposed method that also features preconditioned iterative solvers, including support for the NgsAMG NGSolve extension library as well as the NGSolve-PETSc interface.</p> <p>The files errors_hdg.pickle, errors_mcs.pickle and kappas.pickle contain the raw data the tables and pictures in the paper were generated from.</p> <p>This data was generated with the scripts conv3d_hdg.py, conv3d_mcs.py and calc_kappas.py which use the FlowTemplates.py infrastructure.</p>
Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints
<p><strong>1. Data set for for validation of the Protein-Ligand Interaction Fingerprints, which includes examples of protein structures (original PDB and equilibrated) and molecular dynamics trajectories (equilibration and ligand dissociation generated using Random Acceleration MD simulations, RAMD)</strong></p> <p><strong>mdifp_validation_data.tar.gz - </strong>archive that contains benchmark dataset for evaluation of the protein-ligand IFP protocol (PDB structures of protonated complexes, ligands, and MOL2 files of ligands) published in D. B. Kokha, B. Doser, S. Richter, F. Ormersbach, X. Cheng, R. C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <p>(2020) <a href="https://arxiv.org/abs/2006.11066">arXiv:2006.11066</a> </p> <p><strong>2YKI </strong>- protein-ligand complex , PDB ID 2YKI<br> - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/)<br> - ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/)</p> <p><strong>6EI5</strong> - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5<br> - ref-min.pdb minimized structure<br> - ref.prmtop topology file<br> - moe.mol2 - ligand structure in mol2 format<br> - amber2namd2.dcd generated MD trajectory </p> <p><strong>SAD_3-RAMD-03-2020.pkl </strong>- a pkl dataset with IFPs generated from RAMD dissociation trajectory of the complex PDB ID: 5LQ9 (trajectories from the paper Front. Mol. Biosci., 2019 DOI:10.3389/fmolb.2019.00036)</p> <p><strong>HSP90_Gromacs.zip </strong>- an archive that contains three pkl data sets of protein-ligand IFPs (for three HSP90 complexes; PDB ID: 5J64, 5J86, 5LQ9) generated from RAMD dissociation trajectories simulated using new Gromacs-RAMD engine (https://github.com/HITS-MCM/gromacs-ramd)</p> <p>The rest of the files contains data obtained from simulation of the complex of <strong>GPCR muscarinic receptor M2 (PDB ID:4MQT);</strong> immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE) with a small molecule agonist iperoxo. <br> - <strong>IXO.pdb and moe.mol2 </strong>- PDBand MOL2 structure of iperoxo<br> - <strong>AMBER_eq.tar.gz</strong> - structure of the equilibrated complex generated using AMBER software<br> -<strong> NAMD_eq.tar.gz </strong>- two equilibration trajectories in dcd format generated using NAMD software <br> - <strong>RAMD_eq.tar.gz </strong>- dissociation tarjectoris of iprtoxo from the M2 protein generated from the last snapshot of two NAMD equilibration trajectories (for each case 2 RAMD dissociaiton trajectories are available) </p> <p>( *csv files were added erroneously and do not belong to the project)</p>
A computational workflow for binding free energies in Python
<p>Dataset of distances between a host and six different ligands. The host was beta-cyclodextrin (bCD), while the ligands were phenol, benzene, aspirin, toluene, chlorobenzene and 1,3-dichlorobenzene. No bonds were frozen. </p> <p>The ligand were set to move with a step of 0.25 angstrom from -26 to 26 relative to the bCD (a total of 208 distances). At each distance, a energy biasing potential <span class="math-tex">\(E_{bias}\)</span> was applied the keep two molecules in place. </p> <p><span class="math-tex">\(E_{bias} = \frac{1}{2}\cdot K \cdot (R - R_0)^2\)</span></p> <p>The parameters of the ligands were taken from OpenFF while GLYCAM were used for the host bCD. All of it were applied in Python and the OpenMM framework. Starting parameters, pdb-, and sdf-files can be found in the start folder.</p>
Computer codes (in Python) for 'The reproduction number and its probability distribution for stochastic viral dynamics'
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