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873 results for “ligands”
Molecular Dynamics Trajectories for GPR6 with Ligand OLA
<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>
Molecular Dynamics Trajectories for GPR6 without Ligand (IagNoLig)
<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>
Simulation and analysis data set for apo-conformational kinetics and gated ligand binding to HIV-1 protease
<p>The data set provided here accompanies a study described in the manuscript:</p> <p>S. Kashif Sadiq, Abraham Muñiz Chicharro, Patrick Friedrich, Rebecca Wade, A multiscale approach for computing gated ligand binding from molecular dynamics and Brownian dynamics simulations. (2021) Preprint available: https://doi.org/10.1101/2021.06.22.449380</p> <p>This study combines molecular dynamics MD simulations and associated conformational analyses and Markov state models (MSMs) with Brownian dynamics (BD) simulations to compute conformation gated ligand association kinetics to HIV-1 protease.</p> <p>To download the data, go to a directory where you would like to download the files. Then for each of the provided tar files enter the following command:</p> <p>tar xvf $X.tar</p> <p>where $X is the name prefix of the corresponding tar file.</p> <p>The unpacked data set creates a ./data sub-directory which itself contains two further sub-directories: MD and BD. Please see README.txt files within these sub directories for further instructions on the software tools and scripts that have been provided therein for using and reproducing the data set. The MD README.txt is found within: data_MD_MSM_analysis.tar, the BD README.txt is found within: data_BD_examples.tar.</p> <p>Please note, the python Jupyter notebook and associated module for further analysis of the MSM from the pre-defined feature set calculated in the study as well as other analyses can also be found at:</p> <p>https://github.com/kashifsadiq/hiv1pr-msm/</p> <p>MD trajectory files are provided for further analysis but are not required to reproduce the MSM and conformational analyses reported in the study. To facilitate overview, MSM analysis has been stored in several object files. To exactly reproduce the reported MD/MSM analyses, untar only the 1) data_MD_MSM_analysis.tar and 2) data_MD_MSMobj.tar files and work through the python Jupyter notebook.</p> <p> </p>
Dataset - Exploring Ligand Binding to Calcitonin Gene-Related Peptide Receptors
<p>Spervised molecular dynamics simulations (SuMD) of:</p> <p>- CGRP binding to CGRPR TMD</p> <p>- telcagepant binding to CGRPR ECD</p> <p>- telcagepant unbinding from CGRPR ECD</p> <p>(Water molecules, POPC and ions removed)</p>
Consensus machine-learning models for protein-ligand binding affinity estimation
<p><strong>Motivation:</strong> In structure-based virtual screening, machine learning based scoring function gained popularity in the last few years as they outperformed classical scoring function. The protein-ligand system can be encoded by a set of orthogonal descriptor spaces, which are then mined by machine learning algorithms to find a relationship with the binding affinity experimental value.</p> <p><strong> </strong></p> <p><strong>Results:</strong> In this work we propose our modelling approach to derive a new scoring function, derived from a combination of multiple descriptor spaces coupled with machine learning algorithms ensembled in consensus. The SF has been trained on the PDBbind v.2019 data and has been extensively internally and externally validated on a large set of complexes. When benchmarked on the PDBbind core set, it achieved better performance than state-of-the-art counterparts, scoring: R<sub>Pearson </sub>= 0.85-0.86 r<sup>2</sup> = 0.70-0.72 and RMSE = 1.15-1.21. As highlights: (i) an applicability domain definition has been implemented to delimit the SF’s application boundaries, and (ii) a mechanistic interpretation is proposed by investigating the contribution of each protein-ligand atom pairs in the prediction of the binding affinity, which could provide a support in the lead-optimization process.</p> <p><strong> </strong></p> <p><strong>Availability and implementation:</strong> Our scoring function is freely available through the webportal: <a href="https://predictor.exscalate.eu/">https://predictor.exscalate.eu/</a></p>
Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state.
<p>Initial coordinates, molecular dynamics trajectories and representative snapshots resulting from the study "Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state." by Pasquadibisceglie and Polticelli.</p> <p>The MD folders contain the parameter/topology (parm7) and initial coordinates (rst7) for the molecular dynamics simulations. Moreover, a NetCDF trajectory "prod.nc" of the production phase is also included.<br> In detail:<br> - MD0 -> SLC25A29 in absence of ligands;<br> - MD1, MD3, MD4 -> SLC25A29-ARG complex;<br> - MD1-LYS, MD3-LYS, MD4-LYS -> SLC25A29-LYS complex.</p> <p>The folder PDB_figures contains the PDB files used to produce the figures presented in the manuscript.</p>
Research data supporting "Surface dynamics and ligand-core interactions of quantum size photoluminescent gold nanoclusters"
<p>Experimental research raw data supporting the publication by Lin, Y. et al, 2018, Surface dynamics and ligand-core interactions of quantum sized photoluminescent gold nanoclusters, Journal of the American Chemical Society. DOI: 10.1021/jacs.8b04436</p> <p>Molecular simulation data is available upon reasonable request from irene.yarovsky@rmit.edu.au.</p>
PDB files of "Characterization of ligand-induced thermal stability of the human organic cation transporter 2 (OCT2)"
<p>The predicted alphafold structure of OCT2 (Uniprot O15244) was embedded in a DPPC model membrane, solvated with TIP3P water and subjected to 250 ns molecular dynamics simulation using the Desmond module of Schroedingers Drug Discovery Suite. The last 150 ns were used for clustering and the cluster with most of the members (cluster0) was selected as a model for OCT2. Based on that (still predicted, but optimised) model we performed docking with various substrates for which we also provide the obtained docking pose. See Publication in Frontiers in Pharmacology (DOI: 10.3389/fphar.2023.1154213) for details and results.</p>
Original data for publication "Intracluster ligand rearrangement: an NMR-based thermodynamic study"
<p>Original data for publication "Intracluster ligand rearrangement: an NMR-based thermodynamic study" published in Nanoscale, 2023.</p> <p>Original data used for the Figures are provided.</p>
Mechanism of rotenone binding to respiratory complex I depends on ligand flexibility
<p>Snapshots from MD simulations (with umbrella sampling and metadynamics) of<br> respiratory complex I. Only subunits ND1, NDUFS2 and NDUFS7 (with the membrane spanning N-terminus<br> truncated) and rotenone are shown in binding mode (see Fig. 2 of main paper): </p> <p>1) ROT1: configurations/rot1.pdb<br> 2) pre-redox: configurations/rot1.5.pdb<br> 3) ROT2: configurations/rot2.pdb</p> <p>Force-field and topology files in GROMACS format:</p> <p>4) Bonded parameters: parameters/rotenoids_ffBonded.itp<br> 5) Rotenone: parameters/rot.itp<br> 6) Dehydrated derivative: parameters/dehyd_rot.itp</p>
Computational Data for "Synthesis, Structures and Photophysical Properties of Tetra- and Hexanuclear Zinc Complexes Supported by Tridentate Schiff Base Ligands"
<p>Computational data used for the article "Synthesis, Structures and Photophysical Properties of Tetra- and Hexanuclear Zinc Complexes Supported by Tridentate Schiff Base Ligands". This Includes the geometries, optimizations, and the TD-DFT calculations of all mentioned structures and simulated UV-Vis spectra.</p>
Triplicate MD simulations performed on the ligand Abscisic acid and the 7CKA protein for a total time of 100 ns.
<p><strong>Molecular Dynamic Simulation study</strong></p> <p>Triplicate MD simulations were performed on the ligand Abscisic acid (<strong>PubChem ID: Abscisic acid</strong>) and the 7CKA protein for a total time of 100 ns. This was done to investigate the quality and stability of the complex until the point at which it converged.</p>
Open Data for publication: Engineering ligand chemistry on Au25 nanocluster: From unique ligand addition to precisely controllable ligand exchange
<p>Open Data for publication: <strong>Engineering ligand chemistry on Au<sub>25</sub> nanocluster: From unique ligand addition to precisely controllable ligand exchange</strong><br> Published in Chemical Science, 2023</p> <p>Authors: Jiangtao Zhao, Abolfazl Ziarati, Arnulf Rosspeintner, Yanan Wang and Thomas Bürgi</p>
PIGNet2: A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening
<p>Training and test datasets of the paper "Improving the versatility of deep learning-based protein-ligand interaction prediction for accurate binding affinity scoring and virtual screening".</p>
Is the neuropeptide PEN a ligand of GPR83?
<p>Numerical dataset for the manuscript by Giesecke et al., Is the neuropeptide PEN a ligand of GPR83?</p>
"Inverted" cyclic(alkyl)(amino)carbene ligands allow olefin metathesis with ethylene at parts-per-billion catalyst loading
<p>Data confirming the structure of the new compounds obtained within the project, published in <em>Chem Catalysis </em><strong>2023</strong><em>, 3, 100713.</em></p> <p><a href="https://doi.org/10.1016/j.checat.2023.100713">https://doi.org/10.1016/j.checat.2023.100713</a></p> <p>The research was supported by the National Science Centre, Poland (OPUS grant DEC-2017/27/B/ST5/02563).</p>
Dual structure of a vanadyl-based molecular qubit containing a bis(β-diketonato) ligand. Open dataset
<p>Data supporting the original figures 2, 4, 6, and 7 of the related publication</p>
A Study of Atezolizumab (an Engineered Anti-Programmed Death-Ligand 1 [PD-L1] Antibody) as Monotherapy or in Combination With Bevacizumab (Avastin®) Compared to Sunitinib (Sutent®) in Participants Wit
ClinicalTrials.gov study NCT01984242. IPD Sharing: Not stated. Countries: 9. Publications: 2.
Effects of PPAR Ligands on Ectopic Fat Accumulation and Inflammation
ClinicalTrials.gov study NCT00470262. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Atezolizumab in Participants With Programmed Death - Ligand 1 (PD-L1) Positive Locally Advanced or Metastatic Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT02031458. IPD Sharing: Not stated. Countries: 19. Publications: 2.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.