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873 results for “ligands”
Transcriptomic profiles of ligand-stimulated IRF-knockout THP1 cells
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Migraine monoclonal antibodies against CGRP change brain activity depending on ligand or receptor target – an fMRI study
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Data from: A functionally conserved mechanism of modulation via a vestibule site in pentameric ligand-gated ion channels
<p>Pentameric ligand-gated ion channels (pLGICs) belong to a class of ion channels involved in fast synaptic signaling in the central and peripheral nervous systems. Molecules acting as allosteric modulators target binding sites that are remote from the neurotransmitter binding site, but functionally affect coupling of ligand binding to channel opening. Here, we investigated an allosteric binding site in the ion channel vestibule, which has converged from a series of studies on prokaryote and eukaryote channel homologs. We discovered single domain antibodies, called nanobodies, which are functionally active as allosteric modulators, and solved co-crystal structures of the prokaryote channel ELIC bound either to a positive (PAM) or a negative (NAM) allosteric modulator. We extrapolate the functional importance of the vestibule binding site to eukaryote ion channels, suggesting a conserved mechanism of allosteric modulation. This work identifies key elements of allosteric binding sites and extends drug design possibilities in pLGICs using nanobodies.</p>
Mining For Ligandable Cavities in RNA [Dataset and ML-Code]
<p>Data and code to train models described in the manuscript: Mining For Ligandable Cavities in RNA (DOI: 10.1021/acsmedchemlett.1c00068)</p> <p> </p>
Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints
<p><strong>1. Data set for for validation of the Protein-Ligand Interaction Fingerprints, which includes examples of protein structures (original PDB and equilibrated) and molecular dynamics trajectories (equilibration and ligand dissociation generated using Random Acceleration MD simulations, RAMD)</strong></p> <p><strong>mdifp_validation_data.tar.gz - </strong>archive that contains benchmark dataset for evaluation of the protein-ligand IFP protocol (PDB structures of protonated complexes, ligands, and MOL2 files of ligands) published in D. B. Kokha, B. Doser, S. Richter, F. Ormersbach, X. Cheng, R. C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <p>(2020) <a href="https://arxiv.org/abs/2006.11066">arXiv:2006.11066</a> </p> <p><strong>2YKI </strong>- protein-ligand complex , PDB ID 2YKI<br> - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/)<br> - ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/)</p> <p><strong>6EI5</strong> - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5<br> - ref-min.pdb minimized structure<br> - ref.prmtop topology file<br> - moe.mol2 - ligand structure in mol2 format<br> - amber2namd2.dcd generated MD trajectory </p> <p><strong>SAD_3-RAMD-03-2020.pkl </strong>- a pkl dataset with IFPs generated from RAMD dissociation trajectory of the complex PDB ID: 5LQ9 (trajectories from the paper Front. Mol. Biosci., 2019 DOI:10.3389/fmolb.2019.00036)</p> <p><strong>HSP90_Gromacs.zip </strong>- an archive that contains three pkl data sets of protein-ligand IFPs (for three HSP90 complexes; PDB ID: 5J64, 5J86, 5LQ9) generated from RAMD dissociation trajectories simulated using new Gromacs-RAMD engine (https://github.com/HITS-MCM/gromacs-ramd)</p> <p>The rest of the files contains data obtained from simulation of the complex of <strong>GPCR muscarinic receptor M2 (PDB ID:4MQT);</strong> immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE) with a small molecule agonist iperoxo. <br> - <strong>IXO.pdb and moe.mol2 </strong>- PDBand MOL2 structure of iperoxo<br> - <strong>AMBER_eq.tar.gz</strong> - structure of the equilibrated complex generated using AMBER software<br> -<strong> NAMD_eq.tar.gz </strong>- two equilibration trajectories in dcd format generated using NAMD software <br> - <strong>RAMD_eq.tar.gz </strong>- dissociation tarjectoris of iprtoxo from the M2 protein generated from the last snapshot of two NAMD equilibration trajectories (for each case 2 RAMD dissociaiton trajectories are available) </p> <p>( *csv files were added erroneously and do not belong to the project)</p>
Ligand-dependent effects of methionine-8 oxidation in parathyroid hormone peptide analogs
<p>LA-PTH is a long-acting parathyroid hormone (PTH) peptide analog in pre-clinical development for hypoparathyroidism (HP). Like native PTH, LA-PTH contains a methionine at position 8 that is predicted to be critical for function. We assessed the impact of methionine oxidation on the functional properties of LA-PTH and control PTH ligands. Oxidation of PTH(1-34) resulted in marked (~20-fold) reductions in binding affinity on the PTH receptor-1 (PTHR1) in cell membranes, similarly diminished potency for cAMP signaling in osteoblastic cell lines (SaOS-2 and UMR106), and impaired efficacy for raising blood calcium in mice. Surprisingly, oxidation of LA-PTH resulted in little or no change in these functional responses. The signaling potency of oxidized-LA-PTH was, however, reduced ~40-fold compared to LA-PTH in cells expressing a PTHR1 construct that lacks the N-terminal extracellular domain (ECD). Molecular modeling revealed that while Met8 of both LA-PTH and PTH(1-34) is situated within the orthosteric ligand-binding pocket of the receptor's transmembrane domain bundle (TMD), the Met8 sidechain position is shifted for the two ligands such that upon Met8 oxidation of PTH(1-34) steric clashes occur that are not seen with oxidized LA-PTH. The findings suggest that LA-PTH and PTH(1-34) engage the receptor differently in the Met8-interaction environment of the TMD bundle, and that this interaction environment can be allosterically influenced by the ECD component of the ligand-receptor complex. The findings should be useful for the future development of novel PTH-based peptide therapeutics for diseases of bone and mineral ion metabolism.</p>
Simulation data for: "Piezochromic effects in CdS nanocrystals: the roles of size, ligands and pressure"
<p>This folder contains all the input and output files for the simulations performed in the publication "Piezochromic effects in CdS nanocrystals: the roles of size, ligands and pressure". The data is organised as follows: /pseudos contains the PAW potentials used in all calculations</p> <p>/Geom_relax contains the data for the BFGS optimization of all nanocrystals under pressure using the electronic enthalpy method and optimizing in steps of 1GPa or above from 0 to 15GPa</p> <p>/COND contains the data for the conduction space optimization performed on the structures relaxed at various pressures</p> <p>/LDOS contains the data for the projected DOS obtained from a joint valence-conduction basis where the DOS has been decomposed in terms of the chemical environment of atoms (eg core,surface, corner, Cd, S, etc...). A 0.1 eV Gaussian smearing is applied for generating plots.</p> <p>/TDDFT contains the LR-TDDFT calculations using the optimized valence-conduction basis following Geometry relaxation and conduction optimization. The absorption spectra are obtained by convolving a Gaussian of 0.05 eV on the stick spectra obtained at the end of calculations. Please refer to the user manuals on the ONETEP website (www.onetep.org) for further details regarding how to perform these calculations</p> <p>All calculations were performed with ONETEP version 3.1 or above (specified in the output of each calculation).</p>
Exploring the ligand binding and conformational dynamics of the substrate binding domain 1 of the ABC transporter GlnPQ
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GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction
<p>The preprocessed dataset for paper "GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction" with associated code at https://github.com/Mercuryhs/GAABind.</p><p>The dataset files are saved as .pkl file for the convenience of use.</p><p><strong>Paper Abstract</strong>:</p><p>Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket- ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset, the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the SARS-CoV-2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.</p>
NicheNet-v2 ligand-receptor networks: integrated information from Omnipath
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DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.
<p>test and training data.</p>
Ligand efficacy modulates conformational dynamics of the µ-opioid receptor
<p>This dataset includes DEER data from:</p> <p>Zhao and Elgeti et al. (2024)</p> <p>doi: 10.1038/s41586-024-07295-2</p>
Molecular dynamics simulation of a pentameric ligand-gated ion channel DeCLIC
<p>Molecular dynamics simulation trajectories, parameter files for a bacterial pentameric ligand-gated ion channel DeCLIC, in the system with 150mM CaCl2 or NaCl2.</p>
Datasets for "Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data"
<div> <p>Datasets used for implementing the <a href="https://github.com/huankoh/PSICHIC">PSICHIC</a> experiments shown in the <a href="https://doi.org/10.1101/2023.09.17.558145">manuscript</a>.</p> <p> </p> </div>
Dataset : Micromolar concentration affinity study on a benchtop NMR spectrometer with secondary 13C labeled hyperpolarized ligands
<p><strong>Data of the dDNP decays for 13C drug screening</strong></p> <p><strong>Drug discovery Data recap</strong></p> <p> </p> <p><strong>Samples</strong></p> <p>Sample 2 : 44mM of Ac-L30 (N-Acetyl [1-<sup>13</sup>C]-6 amino-2-naphthoic acid) in 60/30/10 DMSO/D2O/H2O with 25 mM Tempol</p> <p>Sample 4 (in fact sample 3 in Topspin and according to OC figures) : 44mM of Ac-L08 (N-Acetyl [1-<sup>13</sup>C]-glycine) in 60/30/10 DMSO/D2O/H2O with 25 mM Tempol</p> <p> </p> <p><strong>Dissolution Ac-L30 </strong></p> <p><em>Sample : 600 µM Sample 2 without and with 2 µM HSA</em></p> <p><em>Topspin Folder : 20210414-DrugScreening</em></p> <ul> <li>Dissolution fragment : 1</li> <ul> <li>D1 5 sec</li> <li>Int between 167.2 - 168.3 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 11.88 (10.84, 12.91)</li> <li> a = 1.017 (0.967, 1.066)</li> <li> d = 0.0005482 (-0.006744, 0.007841)</li> </ul> <li>TE : 2</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <li>Dissolution fragment + HSA : 3</li> <ul> <li>D1 5 sec</li> <li>Integration between 167.9 - 168.1 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 6.339 (5.57, 7.109)</li> <li> a = 1.027 (0.9665, 1.088)</li> <li> d = -0.007015 (-0.01521, 0.001185)</li> </ul> <li>TE : 4</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <ul> <li>Code used to plot : Ac-L30.m</li> </ul> </ul> <p> </p> <p><strong>Dissolution Ac-L30 </strong></p> <p><em>Sample : 600 µM Sample 4 without and with 2 µM HSA</em></p> <p><em>Topspin Folder : 20210414-DrugScreening</em></p> <ul> <li>Dissolution fragment : 5</li> <ul> <li>D1 2,5 sec</li> <li>Int between 168.6 - 169 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 29.25 (29.05, 29.46)</li> <li> a = 1.019 (1.015, 1.023)</li> <li> d = -0.001389 (-0.002239, -0.0005388)</li> </ul> <li>TE : 6</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <li>Dissolution fragment + HSA : 11</li> <ul> <li>D1 2,5 sec</li> <li>Integration between 168.6 - 169 ppm</li> <li>General model:</li> <li> val(t) = a*(exp(-(t)/T)+d)</li> <li> Coefficients (with 95% confidence bounds):</li> <li> T = 29.73 (29.57, 29.88)</li> <li> a = 0.9938 (0.9905, 0.9971)</li> <li> d = -0.0003466 (-0.0007762, 8.314e-05)</li> </ul> <li>TE : 12</li> <ul> <li>polarization to be computed</li> </ul> </ul> <ul> <ul> <li>Code used to plot : Ac-L08.m</li> </ul> </ul>
Unravelling the Mechanism and Influence of Auxiliary Ligands on the Isomerization of Neutral [P,O]-Chelated Nickel Complexes for Olefin Polymerization
<p>This folder contains the DFT-optimized geometries (in .xyz format together with the gas-phase energy, E) accompanying the paper</p> <p>"Unravelling the Mechanism and Influence of Auxiliary Ligands on the Isomerization of Neutral [P,O]-Chelated Nickel Complexes for Olefin Polymerization"</p> <p>where conformers occur, they are always named from the lowest Gibbs energy to the highest in ascending order from c1 (sometimes omitted), c2, c3, ...</p> <p>Folder structure:</p> <p>--/ligands/ contains DFT optimized structures of auxiliary ligands L1-L5</p> <p>--/XIII/ contains optimized DFT structures and constrained optimized DFT structures for catalyst system XIII</p> <p>--/XIV/ contains optimized DFT structures and constrained optimized DFT structures for catalyst system XIV</p> <p>--/first_polymerization_step/ contains optimized DFT structures for the first polymerization step (ethylene and acrylate enchainment) catalyzed by system XIII</p>
Targeting protein-ligand neosurfaces using a generalizable deep learning approach [benchmark dataset]
<p>PDB files and processed surface meshes for the binder recovery benchmark. For the larger PDBbind decoy set only the PDB files are provided.</p>
Refining ligand poses in RNA/ligand complexes of pharmaceutical relevance: a perspective by QM/MM simulations and NMR measurements
<p>This directory contains files for the project:</p> <p>Title: Refining ligand poses in RNA/ligand complexes of pharmaceutical relevance: a perspective by QM/MM simulations and NMR measurements</p> <p>Authors: Gia Linh Hoang, Manuel Röck, Aldo Tancredi, Thomas Magauer, Davide Mandelli, Jörg B. Schulz, Sybille Krauss, Giulia Rossetti, Martin Tollinger, Paolo Carloni</p> <p>There are two folders containing input and parameter files of the classical MD simulations with GROMACS and QM/MM simulation with MiMiC, and an input file for NMR Chemical Shifts calculation with ORCA.</p>
Dataset: Low-Basicity 5-HT6 Receptor Ligands from the Group of Cyclic Arylguanidine Derivatives and Their Antiproliferative Activity Evaluation
<p>Biochromatographic data for publication: Low-Basicity 5-HT6 Receptor Ligands from the Group of Cyclic Arylguanidine Derivatives and Their Antiproliferative Activity Evaluation</p> <p> </p>
Quantum Chemical Data – A Heterodox Approach for Designing Iron Photosensitizers: Pentacyanoferrate Complexes with Monodentate Pyridinium based Acceptor Ligands
<p>Simulated ground and excited state properties of <strong>[</strong><strong>Fe-mpz<sup>+</sup>]</strong><strong> </strong>and <strong>[Fe-bpy<sup>+</sup>]</strong>, as obtained at the B3LYP/def2svp level of theory within water (PCM vs PCM and 10 explicit water molecules). Both complexes were investigated within Franck-Condon region (see directory: FC) as well as within two triplet equilibria (see directories: 3mc and 3mlct).</p> <p>The folder of each system contains the charge density differences (CDDs) of dipole-allowed transitions contributing to the electronic absorption within the singlet ground state (singlet-singlet transitions: SS and singlet-triplet transitions: ST).</p> <p>The spin density of the relaxed triplet ground state (see directories: 3mc and 3mlct) and CDDs of spin and dipole allowed triplet-triplet transitions are given. These transitions are correlated to the excited-state absorption signals as investigated by transient absorption spectroscopy.</p> <p>All fully relaxed equilibrium structures, i.e., S<sub>0</sub> (FC) and T<sub>1</sub> (<sup>3</sup>MC and <sup>3</sup>MLCT) as well as (approximate) transition state strutures along the <sup>3</sup>MLCT-<sup>3</sup>MC pathways are provided as xyz files within the respective folders.</p>
ScienceDex guides
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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.