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292 results for “docks”
Benchmarking of PROTAC docking and virtual screening tools - dataset
<p>This repository includes all the input files for the PROTAC docking and virtual screening benchmark. The raw data files are available on request (all raw data files combined is ~70GB and compressed ~47GB). Researchers can access and download the data to reproduce the results. Details about files/folders is included in the README.txt.</p>
Healing progression of tail docking and ear tag wounds in lambs
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The maternal-offspring relationship in tailed and docked ewes and their lambs
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Sexual attractivity and receptivity in tailed and docked ewes
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Molecular docking COVID-19
<p>Coronavirus disease 2019 drug discovery through molecular docking</p>
HADDOCK Coarse-Grained Protein-DNA Docking Dataset
<p>This dataset is the result of the benchmarking done in the publication <a href="https://www.frontiersin.org/articles/10.3389/fmolb.2019.00102/full"><em>MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing</em></a> in which HADDOCK's coarse-grain support is extended to Protein-DNA docking.</p> <p>It contains both the All-atom and Coarse-Grained output of 46 macromolecular complexes derived from the <a href="https://haddock.science.uu.nl/dna/benchmark.html">Protein-DNA benchmark</a> (<a href="https://doi.org/10.1093/nar/gkn386">10.1093/nar/gkn386</a>) as well as 6 complexes with experimental information, CAPRI Target 95 (PRC1 ubiquitination module bound to the nucleosome) and all new parameters added to HADDOCK.</p>
Illustration of Protein-Protein Docking
<p>Conceptual representation of protein-protein docking. Two proteins, represented as surfaces, moving towards each other with a computer server rack in the background. Background image sourced from Wikimedia, original author Victorgrigas.</p>
Protein-protein docking with significant backbone flexibility
<p>The trajectory of a single replica from the protein-protein docking simulation of barnase/barstar system. The movie shows the barnase receptor in surface representation and the barstar ligand in ribbon. The presented replica reached the model with interface RMSD value 1.9 Angstrom from the complex X-ray structure, shown as transparent ribbon. </p>
St Katharine Docks Stones
Two stones from the original dock now carved into a sculpture at St Katharine Docks, London. Title: "Here The Sea Shells." Series 1997- 1999. Artist: Paula Haughney. http://www.paulahaughney.co.uk/ 149 photos taken in October 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Greenland Dock Mooring Post
An old mooring post at the far east end of Greenland Dock, Rotherhithe, London. 131 photos taken in November 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery
<p>All of the individual docking data and ESSENCE-Dock consensus results for 21 diverse DUD-E targets as presented in the paper "ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery".</p> <p>The data is sorted per DUD-E target. It contains the prepared data that was used for the docking calculations (in the Undocked directory), as well as our docking results. Finally, our ESSENCE-Dock Consensus results are included as well</p> <p>Docking calculations were performed using:</p> <ul> <li><a href="https://github.com/bio-hpc/metascreener">Metascreener (V1.1)</a> (Gnina and LeadFinder Calculations; prefix VS_GN_ and VS_LF_ respectively)</li> <li><a href="https://github.com/Jnelen/DiffDockHPC/tree/DiffDockHPCv1.0">DiffDockHPC (v1.0)</a> (DiffDock calculations; prefix VS_DD_ )</li> </ul> <p>The consensus calculations were performed using ESSENCE-Dock, available via <a href="https://github.com/bio-hpc/metascreener">Metascreener </a>as well.</p> <p>The whole methodology and all of the details are described in the ESSENCE-Dock paper: <a href="https://doi.org/10.1021/acs.jcim.3c01982">https://doi.org/10.1021/acs.jcim.3c01982</a></p> <p><strong>Paper Abstract</strong></p> <p>Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose, however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches, which combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking.<br>In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening, while also being designed for use in high-performance computing (HPC) environments.</p>
Data from: A maximum of two readily releasable vesicles per docking site at a cerebellar single active zone synapse
<p>Recent research suggests that in central mammalian synapses, active zones contain several docking sites acting in parallel. Before release, one or several synaptic vesicles (SVs) are thought to bind to each docking site, forming the readily releasable pool (RRP). Determining the RRP size per docking site has important implications for short-term synaptic plasticity. Here, we take advantage of recently developed methods to count the number of released SVs at single glutamatergic synapses in response to trains of action potentials. In each recording, the number of docking sites was determined by fitting with a binomial model the number of released SVs in response to individual action potentials. After normalization with respect to the number of docking sites, the summed number of released SVs following a train of action potentials was used to estimate of the RRP size per docking site. To improve this estimate, various steps were taken to maximize the release probability of docked SVs, the occupancy of docking sites, as well as the extent of synaptic depression. Under these conditions, the RRP size reached a maximum value close to two SVs per docking site. The results indicate that each docking site contains two distinct SV binding sites that can simultaneously accommodate up to one SV each. They further suggest that under special experimental conditions, as both sites are close to full occupancy, a maximal RRP size of two SVs per docking site can be reached. More generally, the results validate a sequential two-step docking model previously proposed at this preparation.</p>
Data from: Compromise docking power evaluation of liganded crystal structures of Mpro SARS-CoV-2
<p>A set of 406 liganded SARS-CoV-2 M<sup>pro</sup> crystal structures originally downloaded from RCSB PBD database is provided. Ligand and protein files are processed and corrected for various types of structural errors and are provided in pdbqt and mol2 formats for immediate use in molecular docking programs AutoDock, AutoDock Vina, and PLANTS. Data are utilized in calculations of newly defined compromise docking power to monitor the performance of above-mentioned software. The provided dataset can also be used for benchmarking of other software and molecular docking protocols on liganded SARS-CoV-2 M<sup>pro</sup> systems.</p>
Discovery of Natural Dual Inhibitors from ZINC Database Targeting Thymic Stromal Lymphopoietin (TSLP) and Interleukin-33 (IL-33) as Potential Anti-Allergy Agents - MD Simulation XVG Files and Complex PDB Files (Docking Results)
<p>This repository contains input and output files of MD simulations along with MM/PBSA related files</p> <p>Manuscript Title: Discovery of Natural Dual Inhibitors from ZINC Database Targeting Thymic Stromal Lymphopoietin (TSLP) and Interleukin-33 (IL-33) as Potential Anti-Allergy Agents</p> <p> </p>
Table of Molecular Docking Result
<p>Tabulation of Molecular Docking Result for Journal Article: <strong>COMPUTATIONAL EXAMINATION </strong><strong>OF FLAVONOID COMPOUNDS: UTILIZATION OF MOLECULAR SIMULATION TO DISCOVER DRUG CANDIDATE FOR COVID-19</strong></p>
Baliles Center (Hull Springs) Depth at Dock from 2021-10-30 to 2021-12-04
<p>General Metadata for Hull Springs Dock-YSI Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>HS_YSI_YYYY-MM-DD_metadata.txt HS_dock_pressure_trans_YYYY-MM-DD_metadata.txt</code></pre> <p> </p> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning.</p> <p>File Created</p> <ul> <li>2021-07-14 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added metadata for pressure transducer</li> </ul> <p>Description</p> <p>These data are from the sampling station in Aimes Creek on the Camp House dock (38.125367, -76.659537).</p> <pre><code>* Physical and chemical water data are collected with a YSI EXO2 sonde. * Temperature (dC) and Pressure (KPa) are collected with an Onset HOBO U20-001-01-Ti Water Level Logger</code></pre> <p> </p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The sensors are sampled every 15 minutes</p> <p>Measurements Parameters, units, and Variable Names</p> <ul> <li>date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS)</li> <li>date.time.adj - the date and time that the pressure was measured adjusted to match the time that barometric pressure was collected. (YYYY-MM-DD HH:MM:SS)</li> <li>observation - the number of the observation.</li> <li>timestamp - the date and time as reported by the data logger (MM/DD/YY HH:MM:SS AM/PM)</li> <li>date - the date that the record was collected (MM/DD/YYYY)</li> <li>time - the time that the record was collected (HH:MM:SS)</li> <li>site_name - the ID name provided by the KOR software.</li> <li>unit_ID - the sonde identification number.</li> <li>user_ID - the user that created the sampling template.</li> <li>Temp_dC - the water temperature in degrees C.</li> <li>DO_perc - the dissolved oxygen percent saturation (%).</li> <li>DO_percL - the "local" dissolved oxygen percent saturation where the calibration is locally always 100% saturated independent of the barometric pressure (%).</li> <li>DO_mg-L - the concentration of dissolved oxygen (mg/L).</li> <li>SPC_uS-cm - the specific conductance (uS/cm).</li> <li>C_uS-cm - the conductivity (uS/cm).</li> <li>nLFC_uS-cm</li> <li>TDS_mg-L - the total dissolved solids (mg/L).</li> <li>SAL_PSU - the salinity of the water (PSU).</li> <li>pH - the pH of the water.</li> <li>pH_mV - the millivolt measurement of the pH meter (mV).</li> <li>FNU - the turbidity of the water (FNU).</li> <li>TSS_mg-L - the total suspended solids (mg/L).</li> <li>BGA_PC_RFU - the phycocyanin fluorescence level which is an indicator of blue-green algae (RFU).</li> <li>BGC_PC_ug-L - the concentration of phycocyanin in the water, which is an indicator of blue-green algae (ug/L).</li> <li>Chl_RFU - the chlorophyll fluorescence level, which is an indicator of phytoplankton biomass (RFU).</li> <li>Chl_ug-L - the concentration of chlorophyll in the water, which is an indicator of phytoplankton biomass (ug/L).</li> <li>fDOM_RFU - the fluorescent DOM fluorescence level (RFU).</li> <li>fDOM_QSU - the standardized fluorescent DOM concentration (QSU).</li> <li>Wiper_V - the voltage output of the wiper (V).</li> <li>Cabel_V - the voltage output of the cable (V).</li> <li>Batt_V - the voltage output of the internal batteries (V).</li> <li>Pressure - the pressure of the water above the pressure transducer (KPa)</li> <li>Temp - the water temperature reported by the pressure transducer (dC)</li> <li>press_mmHg - the pressure of the water above the pressure transducer (mm Hg)</li> <li>BP_mmHg - the barometric pressure recorded by the weather station at the Yellow House (mm Hg)</li> <li>Z_press_trans - the depth of the water above the pressure transducer (cm)</li> <li>Z - the depth of the water above the sediments (cm)</li> </ul>
Interaction of Surface Glycoprotein of SARS-CoV-2 Variants of Concern with Potential Drug Candidates: A Molecular Docking Study
<p>This dataset contains additional docking images of SARS-CoV-2 Variants of Concern with drugs under study. It also contains a spreadsheet containing binding residues with different types of bonds. Further it includes the raw spike protein sequences, 3D model of spike proteins of VOCs (variant of concern) and potential drugs used in this study. </p>
RL-MLZerD: Multimeric protein docking using reinforcement learning
<p>This is the dataset used in the publication:<strong> RL-MLZerD: Multimeric protein docking using reinforcement learning by Tunde Aderinwale, Charles Christoffer and Daisuke Kihara</strong>, in submission 2022.</p> <p>The dataset included 30 protein complexes targets from 3 to 5 chains used for docking. Each target in the dataset are randomly rotated and shifted three times to remove bias to the starting conformation.</p> <p>We also include the docking results for each target in the corresponding directory for all the methods reported in the manuscript.</p> <p>For each target, 1500 to 12,000 models were provided that were generated by RL-MLZerD, 1700 to 12,000 models for Multi-LZerD, about 100 to 300 models by Combdock, and 5 models each by Alphafold-Multimer, Alphafold-Multimer (no template), and ColabFold respectively.</p> <p>Directory description:</p> <p>Target: contains all the files related to all experiment for that target e.g 1A0R<br> Target/*_mod_run_? contain files for all random rotation experiments and combdock results. e.g 1A0R/1A0R_mod_run_1/<br> Target/*_mod_combine_athird contains files for combined rotation experiment for RL-MLZerD and Multilzerd. e.g 1A0R/1A0R_mod_combine_athird/<br> Target/alphafold_multimer contains prediction files for AlphaFold-Multimer, both regular and no template run. e.g alphafold_multimer/1a0r and alphafold_multimer/1a0r_nodb<br> Target/colabfold contains prediction files for ColabFold results. e.g colabfold/prediction_1a0r</p>
Results for blind docking Tocriscreen 2.0 compounds against AlphaFold2 structural modules of human and parasite proteins
<p>Docking (GNINA 1.0) of 1280 Tocriscreen 2.0 compounds against >4000 AlphaFold structures from humans and the parasitic nematode <em>Brugia malayi</em>. </p> <p>Results are provided as Pickle objects or GZipped CSVs.</p> <p>Tuned machine learning models (random forest and XGBoost) are also included for classifying actives/decoys.</p>
Greenland Dock Mooring Post
An old mooring post at the far east end of Greenland Dock, Rotherhithe, London. 84 photos taken in November 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
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OpenNeuro
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