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10 results for “transport tunnel”
Water will find its way: transport through narrow tunnels in hydrolases (Epx protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Epx protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond and contact analyses are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Epx.dat</strong> Parsed database of transport events for Epx system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Epx_tt.ini -o Epx.dat</em></li> </ol>
Water will find its way: transport through narrow tunnels in hydrolases (hEpx protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (hEpx protein)</strong></p> <p>The input files and results used for the paper "<em>Water will find its way: transport through narrow tunnels in hydrolases</em>" are separated in the different files depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the "<em>Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results for hEpx and E470G systems together, since we employed the comparative approach to analyze the results with TransportTools (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the *minimum sphere event* is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>hEpx_E470G.dat</strong> Parsed database of transport events for hEpx_E470G system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/hEpx_E470G_tt.ini -o hEpx_E470G.dat</em></li> </ol> <p> </p>
Water will find its way: transport through narrow tunnels in hydrolases (Lip protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Lip protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Lip.dat</strong> Parsed database of transport events for Lip system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Lip_tt.ini -o Lip.dat</em></li> </ol>
Water will find its way: transport through narrow tunnels in hydrolases (Hal protein with different MD simulation settings)
<h1>Hal_2fs.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_2fs protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_2fs.dat</em>: Parsed database of transport events for Hal system. Command used: <br><em>python3 01_build_database.py -c 04_TransportTools/Hal_2fs.ini -o Hal_2fs.dat</em></p> <p> </p> <h1>Hal_300K.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_300K protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_300K.dat:</em> Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_300K.ini -o Hal_300K.dat</em></p> <p> </p> <h1>Hal_TIP3P.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (TIP3P protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</p> <p> 2. <em>Hal_TIP3P.dat</em>: Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_TIP3P.ini -o Hal_TIP3P.dat</em></p>
Data example and code used in the publication "Is transport of microplastics different from that of mineral dust? Results from idealized wind tunnel studies"
<p>Background</p> <p>The code labels microspheres and counts them. Further, the code determines which microspheres are independent of microsphere-microsphere collisions by their relative position to the other microspheres in an image. Images were taken with a full-frame visual camera (Sony Alpha 7RII) with a long-distance-microscopy lens (K2 DistaMax).</p> <p>Description of the dataset</p> <ul> <li>image_data_all.zip contains 228 tif-format images taken in a single experiment <ul> <li>the images show borosilicate microspheres with diameters from 63 to 75 µm</li> <li>during the experiment, the microspheres are detached from the substrate and are transported out of the image</li> </ul> </li> <li>functions_particle_labeling.jl contains all necessary functions for particle labeling</li> <li>analysis_protocol.jl is an example, that first determines a color threshold, and then labels all microspheres in all images stored in "image_data_all/substrate_a/image_data_single_experiment"</li> <li>post_processing_visualisation.R is an r-script, that reads the output of analysis_protocol.jl and demonstrates how logistic functions were fitted to the data</li> </ul> <p> </p> <p>We used julia 1.8.5 and R 4.3.0.</p> <p> </p>
06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, <em>run_adaptiveMD.py</em> : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p>
Water will find its way: transport through narrow tunnels in hydrolases (water-protein interaction analysis)
<p>Repository including information on the analyses of water-protein interactions by MM/GBSA.</p> <p><strong>hal_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Hal.<br><strong>epx_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Epx.<br><strong>lip_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results<strong> </strong>for Lip<strong>.</strong></p> <p>In each archive structure is:<br> {supercluster_id}: - supercluster directory<br> {event_nr}_{rounded_radius}: - transport event directory<br> renum.pdb -pdb file containing protein, water of interest and 8 Angstrom radius sphere of surrounding waters<br> water.txt - file denoting water ID for water of interest in the renum.pdb file<br> binding_[implicit|explicit].dat - file containing MMGBSA results for full implicit and partial explicit system<br> decomposition_[implicit|explicit].dat - file containing MMGBSA decomposition results for full implicit and partial explicit system<br> info_{supercluster_id}.csv - file containing descriptions of transport events in supercluster (columns: event_name, radius, frame, simulation, water_id)</p> <p><strong>results.tar.gz </strong>- contains csv files generated by <em>script collect_data_minimal.py</em> for each protein for full implicit and partial explicit solvent. Columns in those csv file are: event_name, supercluster, radius, frame, simulation, water_id, delta_total, delta_van_der_waals, delta_electrostatic, delta_G_solv</p> <p><strong>scripts.tar.gz</strong> - contains following scripts<br> read_transport_events.py<br> prep_inputs.py<br> run_MMGBSA_single.sh<br> collect_data_minimal.py<br> plot_bin.py</p> <p>To recreate MMGBSA calculations with provided inputs one should:<br>$ python read_transport_events.py [Hal.dat|Epx.dat|Lip.dat] > selection.txt<br>Prepare transport events information in a compatible format. Binary transport events databases are available in the repository.</p> <p>$ python prep_inputs.py selection.txt - {absolute output path} {supercluster_id} 1 <br>Prepare parm and coordinate file for each supercluster id included in computation. Last argument 1 point script to resume preparations from renum.pdb files.</p> <p>$ bash run_MMGBSA_single.sh {residue_count}<br>Runs MMGBSA for both partial explicit and fully implicit systems. Needs to be run for each event in each supercluster.<br>residue_count should be set to 293 for hal, 319 for epx, and 534 for lip.</p> <p>$ python collect_data_minimal.py [hal|epx|lip]/{supercluster_id} [hal|epx|lip]/info_{supercluster_id}.csv [0|1] >> output.csv<br>Gathers data from MMGBSA calculations for a single supercluster into csv file. <br>Last argument should be 0 for fully implicit system, 1 for partial explicit system.</p> <p>$ python plot_bin.py {hal_implicit_csv} {hal_explicit_csv} {epx_implicit_csv} {epx_explicit_csv} {lip_implicit_csv} {lip_explicit_csv}<br>Results visualization. Produces "combined.png".</p>
Water will find its way: transport through narrow tunnels in hydrolases (E470G protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (E470G protein)</strong></p> <p>The input files and results used for the paper "<em>Water will find its way: transport through narrow tunnels in hydrolases</em>" are separated in the different files depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the "<em>Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> We employed the comparative approach to analyze the results with TransportTools (https://doi.org/10.1093/bioinformatics/btab872). Please see the README file inside for more explanations. Results can be found at Zenodo repository https://doi.org/10.5281/zenodo.7965661</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the *minimum sphere event* is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>hEpx_E470G.dat</strong> Parsed database of transport events for hEpx_E470G system.</li> </ol>
Water will find its way: transport through narrow tunnels in hydrolases (Hal protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Hal protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond and contact analyses are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Hal.dat</strong> Parsed database of transport events for Hal system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Hal_tt.ini -o Hal.dat</em></li> </ol>
Femoral Tunnel Position on Conventional MRI After Anterior Cruciate Ligament Reconstruction-Transtibial Technique Versus Transportal Technique
ClinicalTrials.gov study NCT01513434. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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