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292 results for “docking”
North Temperate Lakes LTER Northern Highland Lake District Coarse Woody Debris Docks
Coarse woody debris (CWD) is an important, but often neglected, component of lake ecosystems. It is ecologically valuable because it creates littoral habitat complexity but it is susceptible to manipulation by riparian process, in particular removal by property owners. The objective of this study is to determine the spatial scales at which human and environmental factors contribute to coarse woody debris input and output dynamics. Coarse woody debris, boat docks, and riparian trees (with the potential of becoming CWD) around the five lakes of the NTL-LTER site (Trout Lake, Allequash Lake (north basin), Sparkling Lake, Crystal Lake, and Big Muskellunge Lake) were measured in 1996 and 1997. Boat docks were located on all 5 lakes. Docks were verified in 1997 using digital orthophotos. GPS positions were adjusted to coincide with the photos. Many of the docks were photographed with a digital camera.
Docked structures from "Optimizing active learning for free energy calculations"
<p>This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ailsci.2022.100050" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ailsci.2022.100050</span></span></a>). AM1-BCC charges are stored in the field "AM1Cache" in the SD file. The charges can be extracted using the code sample below. </p> <p> </p> <pre><code>from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") am1_charges = pickle.loads(base64.b64decode(mol.GetProp("AM1Cache"))) assert len(am1_charges) == mol.GetNumAtoms(), "Charge cache has different number of charges than mol atoms"</code></pre>
Simulation results for Sars-CoV2 3C-like main protease: TRAPP analysis of the binding site flexibility and results of the docking study
<p>Collection of data and scripts related to the paper:</p> <p>Jonas Gossen et al. "A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics" </p> <p>https://www.biorxiv.org/content/10.1101/2020.12.14.422634v2 doi: https://doi.org/10.1101/2020.12.14.422634</p> <p>ACS Pharmacology and Translational Science 2021 DOI: 10.1021/acsptsci.0c00215</p> <p> </p> <p> </p> <p><strong>1. TRAPP simulation results for Sars-CoV2 3C-like main protease:</strong></p> <p>include simulation of the binding pocket druggability, physical-chemical properties, and the binding site composition</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Protease_clean.ipynb">Protease_clean.ipynb</a> - Jupyter Notebook containing analysis of the generated data</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/allTables.zip">allTables.zip</a> - results of TRAPP simulations of the binding site flexibility using LRIP and tConcoord methods</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Every10-ligand_6LU7_R3.5.zip">Every10-ligand_6LU7_R3.5.zip</a> - results of TRAPP pocket analysis on the MD frames</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/PDB-Giulia.zip">PDB-Giulia.zip</a> - TRAPP pocket analysis of 40 PDB complexes of main protease</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/TRAPP_properties_PDB.xlsx">TRAPP_properties_PDB.xlsx</a> - binding pocket properties for 40 PDB complexes of main protease summarized in a table</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/DrugPDB_3structures.xlsx">DrugPDB_3structures.xlsx</a> - binding pocket properties for 3 PDB structures </p> <p><strong>2. Docking & Screening Results</strong></p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/TRAPP_secondSelection_VS.csv">TRAPP_secondSelection_VS.csv</a> - docking/screening of selected structures from TRAPP analysis</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Fred_VS.csv">Fred_VS.csv</a> - docking of PDB structures using Fred</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Glide_VS.csv">Glide_VS.csv</a> - docking of PDB structures using Glide</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS1.xlsx">TableS1.xlsx</a> - Available structures of SARS-CoV-2 Mpro selected for binding site analyses. </p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2A.xlsx">TableS2A.xlsx</a> - SiteScore analysis of all the deposited X-ray crystal structures for the Mpro.</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2B.xlsx">TableS2B.xlsx</a> - SiteScore analysis of the MSM ensemble (4-macrostates).</p>
A new discrete-geometry approach for integrative docking of proteins using chemical crosslinks
<p>This repository pertains to an integrative docking benchmark containing 40 binary protein-protein docking cases. The inputs were monomer structures and chemical crosslinks, varying the numbers, types, false positive rate, sources of input crosslinks, and sources of monomer structures. The discrete geometry method Wall-EASAL and IMP are compared.</p>
Docking of GSH and BPG to the hemoglobin molecule in the oxy and deoxy state
<p>Oxy-Hb contains four bound GSH molecules inside the cavity, two of them at the β-β interface (sites 1 and 2) and two at the α-α chains interface (sites 3 and 4). In deoxy-Hb, two GSH molecules from the β-β subunits interface are released from the sites 1 and 2 allowing BPG to bind. Reoxygenation of Hbb is associated with release of BPG from its binding site and binding of two GSH molecules to the sites 1 and 2. The sites 3 and 4 remain occupied by GSH independent of the Hb conformation.</p>
docking result of androgen
<p> </p> <div>The docking result of androgen and androgenic blocker by using autodock tools and autodock vena </div> <p> </p>
Dataset of molecular docking data of neuropeptides to acid-sensing ion channels
<p>The *.dock4 files are result files of molecular docking with the software Autodock Vina to the human ASIC1a closed state model, of the peptides FRRFa and KNFLRFa (FRRF.dock4, KNFLRF.dock4) that can be visualized with structure viewing programs such as UCSF Chimera on the closed ASIC1a model file (closed_ASIC_pH7.4.pdb). The file “FRRF_KNFLRF_complexes.pdb” provides the structures of selected poses of FRRFa and KNFLRFa peptides docked to the closed conformation of the human ASIC1a model.</p>
Homology modelling, molecular docking and molecular dynamics simulations of wild type and mutant human CYP2J2 with three polyunsaturated fatty acids
<p>This is the "parent" repository for the Data Note : "­Molecular dynamics simulations of the interaction of wild type and mutant human CYP2J2 with polyunsaturated fatty acids" by Abelak, Bishop-Bailey and Nobeli.</p> <p>It contains a document (<strong>Abelak_etal_Methods.pdf</strong>) describing the methods used to produce the data here and the data in all repositories supplementing it.</p> <p>It also contains a shell script (<strong>create_sim4_repeats.sh</strong>) that is typical of those used to set up the molecular dynamics simulations in the repositories supplementing this one.</p> <p>Finally, it contains the results of the homology modelling and docking simulations that formed the starting points for the molecular dynamics simulations in this study.</p> <p>Description of files in this dataset:</p> <p><strong>C2J2_min3_mod_noH.pdb</strong> : Homology model of the wild type CYP2J2 built from an alignment of templates with PDB ids: 1SUO, 2P85, 3EBS and 1Z10.</p> <p><strong>docking_wild_type_C2J2.zip</strong> : Nine docked poses of arachidonic acid docked to the homology model of the wild type CYP2J2.</p> <p>Details of how this data was produced is available in the Abelak_etal_Methods.docx document.</p>
An integrated approach including docking, MD simulations, and network analysis highlights the action mechanism of the cardiac hERG activator RPR260243
<p>500 ns MD trajectories of the hERG bound states (protein and ligand) used for the analyses discussed in the paper. There are three replica for each system. The trajectories can be visualized using molecular visualization programs such as Pymol or VMD uploading the PDB and the DCD file.</p> <p>The PDB and the topology files of the hERG bound state (protein, membrane, ions and ligand) are also included.</p> <p>An example of input file used for the production step of the dynamics has been provided (production_1.conf). </p>
Benchmark Data for Attracting Cavities 2.0 Small-Molecular Docking Program
<p>This repository provides data from the following article:<br> <br> U.F. Roehrig, M. Goullieux, M. Bugnon, V. Zoete,<br> Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking.<br> J. Chem. Inf. Modeling 2023<br> https://doi.org/10.1021/acs.jcim.3c00054<br> <br> </p>
Molecular docking: Hydroxychloroquine alternative to inhibit the COVID-19 main protease (MPro)
<p>Docking study shows best binding affinity against the main protease of COVID-19. As per the docking results top twelve compounds as a MPro inhibitor, Coumermycin A1 (-10.2), Irinotecan (-9.4), Suramin (-9.4), Trovafloxacin (-9.3), Aclarubicin (-9.0), Dactinomycin (-9.0), TG-100801 (-9.0), Raltegravir (-8.9), Digoxin (-8.9), Etoposide (-8.9), Doxorubicin (-8.8), and Venetoclax (-8.8) from the tested compounds.</p> <p>ARULANANDAM, CHARLI DEEPAK (2020): Molecular docking: Hydroxychloroquine alternative to inhibit the COVID-19 main protease (MPro). figshare. Dataset. https://doi.org/10.6084/m9.figshare.12032745.v26</p>
TOP-100 DOCKING POSES OF FDA APPROVED DRUGS AND DRUGS IN CLINICAL INVESTIGATION AT SARS-CoV2 MAIN PROTEASE
<p>7922 compounds were downloaded from NPC database (https://tripod.nih.gov/npc/). In order<br> to eliminate the non-specific binders, some criteria including molecular weight, between 100 to<br> 1000 g/mol; number of rotatable bonds, <100; number of atoms, between 10 and 100; number<br> of aliphatic and aromatic rings, <10; number of hydrogen-bond acceptor and donors, <10 were<br> set and as a result the total number of compounds was decreased to 6654. These ligands were<br> prepared using LigPrep module of Maestro at neutral pH (LigPrep, Schrodinger v.2017). In<br> molecular docking, we used following protein structure: SARS-CoV2 Main Protease, (PDB, 6LU7). The protein<br> was prepared using Protein Preparation module of Maestro. PROPKA was used for<br> determination of protonation states of amino acid residues. Restrained minimization was<br> performed with OPLS3 force field for the protein using 0.3 Å heavy atom convergence.<br> Docking was performed with Glide/SP using default settings. Top-100 docking poses were provided.</p>
TOP-100 DOCKING POSES OF FDA APPROVED AND DRUGS IN CLINICAL INVESTIGATION AT SARS-CoV2 SPIKE/ACE2 INTERFACE
<p>7922 compounds were downloaded from NPC database (https://tripod.nih.gov/npc/). In order<br> to eliminate the non-specific binders, some criteria including molecular weight, between 100 to<br> 1000 g/mol; number of rotatable bonds, <100; number of atoms, between 10 and 100; number<br> of aliphatic and aromatic rings, <10; number of hydrogen-bond acceptor and donors, <10 were<br> set and as a result the total number of compounds was decreased to 6654. These ligands were<br> prepared using LigPrep module of Maestro at neutral pH (LigPrep, Schrodinger v.2017). In<br> molecular docking, we used following protein structure: Spike Protein/ACE-2, (PDB, 6M0J). The protein<br> was prepared using Protein Preparation module of Maestro. PROPKA was used for<br> determination of protonation states of amino acid residues. Restrained minimization was<br> performed with OPLS3 force field for the protein using 0.3 Å heavy atom convergence.<br> Docking was performed with Glide/SP using default settings. Top-100 docking poses were provided.</p>
Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction
<p>The code, dataset, and model weights are described in the paper "Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction."</p> <p> </p> <p><strong>experiment_results.zip:</strong> Contains generated results that can reproduce the result from the reported paper.</p> <p><strong>benchmark.zip:</strong> Contains docking and affinity input data of the interformer. You can use the source code to make predictions and reproduce the number of the reported paper.</p> <p><strong>checkpoints.zip: </strong>Contains one weight for the Energy and four PoseScore and Affinity models.</p> <p><strong>source_code_1.0.zip:</strong> Contains the initial version of the source code.</p> <p><strong>interformer_train.tar.gz:</strong> Contains prepared training data for interformer. poses/ contains all structure need for training, poses/ligand contains the re-docking poses generated by interformer energy, poses/ligand/rcsb contains the conformation of reference ligand, poses/pocket contains all pocket extract by raw PDB from rcsb, poses/uff contains all ligand conformation minimized using UFF from reference ligand, and train/ contains the training csv.</p> <p><strong>baseline_results.tar.gz:</strong> Contains the predictions from three methods: Interformer, DiffDock, and DeepDock. The results align with the exact numbers reported in the paper. For further details, please refer to the <em>eda/ </em>directory.</p> <p> </p> <p>You can also find the newest version of the source code at <a href="https://github.com/tencent-ailab/Interformer" target="_blank" rel="noopener">https://github.com/tencent-ailab/Interformer</a></p> <p> </p>
Docked structures
<p>Supplemental data of an article "QUBO Problem Formulation of Fragment-Based Protein–Ligand Flexible Docking" :</p> <p>A SDF file containing top 1000 docked compound structure with the scores</p>
AutoDock and CB-Dock data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands
<p>AutoDock 4.2 and CB-Dock data for (NPA)<sub>6</sub>Zn<sub>3</sub>(H<sub>2</sub>O)<sub>2</sub> with M<sup>pro</sup> from SARS-CoV-2 from PDB Id: 6LU7. </p>
NMR data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands
<p><sup>1</sup>H, <sup>13</sup>C, COSY, HMBC, and HSQC NMR data in fid format for (NPA)<sub>6</sub>Zn<sub>3</sub>(H<sub>2</sub>O)<sub>2</sub> (NPA = 2-(phenylamino) benzoate) in DMSO-<em>d</em><sub>6.</sub></p>
AA-Score: a New Scoring Function Based on Amino Acid Specific Interaction for Molecular Docking
<p>The protein-ligand scoring function plays an important role in computer-aided drug discovery, which is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function, which is a linear combination of empirical energy components, including hydrogen bond, van der Waals, electrostatic, hydrophobic, π-stacking, π-cation, and metal-ligand interaction. Different from previous empirical scoring functions, AA-Score uses several amino acid-specific empirical interaction components. We tested AA-Score on several test sets. The resulting performance shows AA-Score performs well on scoring, docking, and ranking compared with other widely used traditional scoring functions. Our results suggest that AA-Score gains substantial improvements from using detailed protein-ligand interaction components. Besides, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using AA-Score.</p>
Baliles Center (Hull Springs) Dock depth and temperature data from 2021-12-04 to 2022-02-05
<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>NOTE: The YSI sonde failed during this deployment so only the depth and temperature data are available from the pressure transducer.</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>
Pose Selector Workflow - Docking Poses, Absolute Binding Free Energy Estimates and Structure Input Files for Machine Learning
<p>The Pose Selector (PS) workflow calculates absolute binding free energies (ABFEs) for binding poses of protein-ligand complexes. First, it converts the binding poses (both docking poses as well as experimentally observed ligand binding poses), which are provided as a combination of protein PDB file and ligand MOL2 file, into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks and repair steps. Next, the PS workflow post-processes and analyses the last frame of the resulting eight 100 ps trajectories per binding pose with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the ABFE estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the PS workflow was run on docking poses generated for the PDBbind 2020 dataset (http://www.pdbbind.org.cn/index.php), shared in dockingPosesPDBBind2020.tar.gz. This entry and its partner entry 10.5281/zenodo.11397486 also share the intial coordinates used in the MD simulations of >800,000 docking poses of 4022 protein-ligand complexes (structureFiles_dockingPoses1.tar.gz in this entry and structureFiles_dockingPoses2.tar.gz in 10.5281/zenodo.11397486) and of the experimental ligand binding pose of 4549 complexes (structureFiles_experimentalStructures.tar.gz) as well as the corresponding ABFE estimates (absoluteBindingFreeEnergyEstimates.tar.gz). The MD simulations were run on the LUMI and MeluXina supercomputers while the implicit-solvent calculations were carried out on Galileo (Cineca).</p> <p>The README file describes the structure of the shared data in more detail and points out how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates as well as how to use the data provided in this entry to train a machine-learning model predicting the ABFE of binding poses of protein-ligand complexes. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Pose-Selector-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.