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873 results for “ligands”
OpenFE Ligand Networks for the Protein Ligand Benchmark set
<p><span>These are the OpenFE LigandNetworks for the 15 systems from the ProteinLigandBenchmark v0.3 (https://github.com/openforcefield/protein-ligand-benchmark)</span></p> <p><span>- Kartograf mapper</span></p> <p><span>- Lomap scorer</span></p> <p><span>- OpenEye AM1BCC ELF10 partial charges</span></p> <p><span>- MST networks</span></p> <p><span>- allowing element changes</span></p> <p> </p> <p><span>There are 10 systems that do not involve charge changing transformations, and 5 systems with transformations that involve a change in the net charge of the system (in folder `net_charge_changes`: cdk8, eg5, syk, thrombin, tnks2).</span></p> <p> </p> <p><span>These .graphml files can be read into OpenFE using:</span></p> <p><span>```</span></p> <p><span>from openfe.setup import LigandNetwork</span></p> <p><span>file = 'ligand_network.graphml'</span></p> <p><span>with open(file) as f:</span></p> <p><span><span> </span>graphml = f.read()</span></p> <p><span>network = LigandNetwork.from_graphml(graphml)</span></p> <p><span>```</span></p>
Graph neural networks for predicting metal–ligand coordination of transition metal complexes
<p>Supporting Information data for associated publication "Graph neural networks for predicting metal–ligand coordination of transition metal complexes".</p>
Molecular Dynamics Trajectories for GPR6 with Ligand S1P
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p>This folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame. Periodic boundary condition (pbc) can be restored using VMD's standard pbc commands.</p> <p>Please cite us if you find this data useful!</p>
Data for "Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions"
<p>Data used in "<em>Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions</em>"<br> by F. Pellicani, D. Dal Ben, A. Perali, S. Pilati</p> <p>If you use these data or the python script for your research or other activities, please cite the corresponding journal article.</p> <p> </p> <p>====================</p> <p>Uncompressing the zipped file <em>DataSFUnicam.zip</em> provies the following files and folders:</p> <p><br> <strong>DataSFUnicam/</strong></p> <p> </p> <p> ExperimentalDataPDBFiles/<br> <em>This folder contains 2408 .pdb files of experimental complex structures. The files are named with a univocal code corresponding to the protein-ligand complex.</em></p> <p> </p> <p> ExperimentalDataXLSXFile.xlsx<br> <em>This Excel file reports the experimental protein-ligand chemical information. In the sheet named “Foglio1”, the first column contains the univocal code of the protein-ligand complex, the second column contains the experimentally measured pK_d.</em></p> <p> </p> <p> SyntheticDataPDBFiles/<br> <em>This folder contains the .pdb files of the synthetic complex structures. The .pdb files are grouped in 17 folders according to just as many target proteins. The folders are named after the corresponding protein. Each folder contains the .pdb files for the best position of each protein-ligand pair according to the MOE docking score. The files are named with a univocal code.</em></p> <p> </p> <p> SyntheticDataXLSXFiles/<br> <em> The folder contains 17 Excel files with the chemical information of the synthetic protein-ligand complexes. The files are named after the corresponding target protein. In the sheet named “Foglio1” of each .xlsx file, the first column contains a univocal code of the protein-ligand complex in each conformation, the second column contains an auxiliary numerical code corresponding to the protein-ligand pair, the third column contains the experimentally measured pK_i, and the fourth column contains the docking score provided by the MOE software.</em></p> <p>====================</p> <p>USER GUIDE FOR THE PYTHON SCRIPT</p> <p>Download and uncompress the zipped file "<em>SFUnicam.zip</em>" with a command like "<em>unzip SFUnicam.zip</em>". </p> <p>The following file structure is created:</p> <p><em>SFUnicam/</em></p> <p> <em>ComplexToBePredictedFolder/4ey5_30.pdb <br> MaxAssMatrix.npy<br> my_model<br> devStndSynt.npy<br> mediaSynt.npy<br> UnicamSF13prot.py<br> README.txt</em><br> <br> The subfolder "<em>ComplexToBePredictedFolder/</em>" contains the example PDB file "<em>4ey5_30.pdb</em>".</p> <p>-) To execute the script "<em>UnicamSF13prot.py</em>", Python 3 should be installed with the following libraries and sublibraries:<br> <em>Keras:<br> Regularizers<br> Sequential (keras.models)<br> Conv1D, Dense, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D, AveragePooling1D (keras.layers)<br> Adam (keras.optimizers)<br> Numpy</em><br> <em>Tensorflow</em></p> <p>Operation:<br> -) Copy the .pdb file related to the protein-ligand complex whose affinity is to be predicted in the subfolder “<em>ComplexToBePredictedFolder/</em>”.<br> -) Make sure the following files are in the same folder where the python script is:<br> <em>MaxAssMatrix.npy<br> mediaSynt.npy<br> devStndSynt.npy<br> my_model</em><br> -) Run the code using Python 3 with a command like "<em>python3.x UnicamSF13prot.py</em>".<br> -) Enter the name of the protein-ligand PDB file whose affinity is to be predicted (excluding the extension ".pdb").<br> -) Read the predicted affinity from screen.<br> </p> <p> </p>
Unexpected single ligand occupancy and negative cooperativity in the SARS-CoV-2 Main protease
<p>This directory contains files for the project:<br> Unexpected single ligand occupancy and negative cooperativity in the SARS-CoV-2 Main protease<br> Authors: Simone Albani, Elisa Costanzi, Gia Linh Hoang, Maria Kuzikov, Marcus Frings, Narjes Ansari, Nicola Demitri, The Toan Nguyen, Valerio Rizzi, Jörg Bernhard Schulz, Carsten Bolm, Andrea Zaliani, Paolo Carloni, Paola Storici, Giulia Rossetti <br> Preprint DOI:10.26434/chemrxiv-2023-n1x1n</p> <p>Three sets of simulations were performed. This directory contains input files for simulations of three systems,<br> and link to scripts for water analysis.</p>
Treatment of B-CLL With Autologous IL2 and CD40 Ligand-Expressing Tumor Cells + Lenalidomide
ClinicalTrials.gov study NCT01604031. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validation Study of a New Cytokine-based Dynamic Stratification Based on FLt3 Ligand Plasma Concentration Kinetic Profile and IL-6 Concentration During Induction of Acute Myeloid Leukemia
ClinicalTrials.gov study NCT04641910. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
A Study to Investigate the Whole Body Distribution and Radiation Dosimetry of the Positron Emission Tomography Ligand Fluoride-18 (18F)-JNJ-64326067 in Healthy Participants
ClinicalTrials.gov study NCT03581916. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of CD200 Activation Receptor Ligand (CD200AR-L) and Allogeneic Tumor Lysate Vaccine Immunotherapy for Recurrent Glioblastoma
ClinicalTrials.gov study NCT04642937. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Long-term Outcomes Among Patients With Programmed Death-ligand 1 <1% Metastatic Non-small Cell Lung Cancer Treated With First-line Nivolumab + Ipilimumab + 2 Cycles of Chemotherapy
ClinicalTrials.gov study NCT07024862. IPD Sharing: NO. Countries: 1. Publications: 0.
An Open Label Positron Emission Tomography (PET) Study of GSK618334 in Healthy Male Subjects Using 11C-PHNO as PET Ligand
ClinicalTrials.gov study NCT00814957. IPD Sharing: YES. Countries: 1. Publications: 0.
Ga-68 PSMA Ligand: A Radiopharmaceutical for Localization of Prostate Cancer
ClinicalTrials.gov study NCT03207139. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study to Investigate the Regional Brain Kinetics of the Positron Emission Tomography Ligand [18F]-JNJ-70099731 in Healthy Male Participants
ClinicalTrials.gov study NCT04911543. IPD Sharing: YES. Countries: 1. Publications: 0.
Soluble CD95 Ligand Role in the Pathophysiology of Non-infectious Active Uveitis
ClinicalTrials.gov study NCT04354909. IPD Sharing: NO. Countries: 1. Publications: 0.
Biological, Prospective Study Evaluating the Dosage of Plasma Cytokines Including the FLT3 Ligand and IL6 of Patients Treated With Non-intensive Chemotherapy
ClinicalTrials.gov study NCT06439199. IPD Sharing: NO. Countries: 1. Publications: 0.
Trial Assessing the Inhibitor of Programmed Cell Death Ligand 1 (PD-L1) Immune Checkpoint Atezolizumab
ClinicalTrials.gov study NCT03612791. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effect of Tramadol Versus Morphine on PD1 and PD1-ligand in Patients With Chronic Cancer Pain
ClinicalTrials.gov study NCT04589494. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study to Learn About the Study Medicine Called PF-08046054/SGN-PDL1V Versus Docetaxel in Adult Participants With Previously-treated Programmed Cell Death Ligand 1 (PD-L1) Positive Non-Small-Cell Lun
ClinicalTrials.gov study NCT07144280. IPD Sharing: YES. Countries: 11. Publications: 0.
Positron Emission Tomography (PET) Using Hormone Receptor Ligands in Breast Cancer
ClinicalTrials.gov study NCT00647790. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cadonilimab (AK104) Plus Chemotherapy as First-line Treatment in Non-squamous Non-Small Cell Lung Cancer (NSCLC) Patients With Programmed Cell Death Ligand 1 (PD-L1) Negative
ClinicalTrials.gov study NCT06001151. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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.