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873 results for “ligands”
Output files from tauRAMD simulations for the dissociation of ligands from T4 lysozyme mutants
<p>The zip files contain the output files generated by tauRAMD to simulate the dissociation of ligands from T4 lysozyme (T4L) mutants.</p> <p>Directories:</p> <p>bnz-l99a-ph5: benzene bound to T4L L99A; simulations at 20 °C<br> bnz-l99a-ph5-t10: benzene bound to T4L L99A; simulations at 10 °C<br> bnz-l99a-ph5-t30: benzene bound to T4L L99A; simulations at 30 °C<br> bnz-m102a-ph5: benzene bound to T4L M102A<br> bnz-f104a-ph5: benzene bound to T4L F104A<br> ind-l99a-ph5: indole bound to T4L L99A</p> <p>Further details about the method can be found in the publication "Ligand unbinding mechanisms and kinetics for T4 lysozyme mutants from τRAMD simulations" (doi: https://doi.org/10.1016/j.crstbi.2021.04.001).</p> <p> </p>
Visualization results of Kinase-Ligand complex based on deep learning prediction score
<p>It contains all the validation results used in the manuscript "Global analysis of deep learning prediction using large-scale in-house kinome-wide profiling data". "ligand_list.smi" shows the compound structure information for each ligand, and the ligand atom weighting results for each target are stored in the "Targets" folder. PyMol can be used to visualize the results by using "load.py" (see readme for how to run it).</p>
Mapping out the aqueous surface chemistry of metal oxide nanocrystals; carboxylate, phosphonate and catecholate ligands
<p>Data underlying the figures in the publication “Mapping out the aqueous surface chemistry of metal oxide nanocrystals; carboxylate, phosphonate and catecholate ligands”, published in JACS Au.</p> <p> </p> <p>Table of contents:</p> <p>The <em>.pxp</em> documents contain the experimental data of the figures in the manuscript and it can be opened/edited with the software IGOR Pro 8.0 or higher.</p> <p><strong>1. Figure 1.pxp</strong>: Experimental data for <em>Figure 1</em>. (A) Solvothermal synthesis of HfO<sub>2</sub> nanocrystals starting from 1 equivalent Hf(O-tBu)<sub>4</sub> and 80 equivalents benzyl alcohol. (B) <sup>1</sup>H NMR spectra (normal or diffusion filtered) of MEEAA functionalized HfO<sub>2</sub> NCs in different solvents. The α and β resonances belong to the residual hydroxyl and methyl groups of methanol, respectively. (C) Transmission Electron Microscopy (TEM) image of the synthesized HfO<sub>2</sub> NCs. The NC diameter of the quasi-spherical NCs was calculated after measuring the surface area of at least 150 NCs and calculated the diameter as if it was a circle. A size distribution histogram and a zoomed-in image of a singular NC can be seen respectively in the bottom left and the top right corner.</p> <p><strong>2. Figure 2.pxp</strong>: Experimental data for <em>Figure 2</em>. (A) Ligand exchange performed between MEEAA functionalized NCs and PA-PEG. (B) <sup>1</sup>H NMR reference spectra in MeOD of the free ligands as reference and the stepwise titration of MEEAA functionalized NCs with PA-PEG, equivalents are with respect to the total amount of MEEAA present. (C) <sup>31</sup>P NMR spectra (4096 scans) in MeOD for the stepwise titration of MEEAA functionalized NCs with PA-PEG, broadened signals are indicative of NC binding. (D) Diffusion filtered <sup>1</sup>H NMR spectra of MEEAA functionalized NCs in MeOD after addition of 1.3 equivalents of PA-PEG. Signals arising from bound MEEAA are denoted in red, signals arising from PA-PEG are denoted in striped blue. CNC = 1210 µmol.L<sup>-1</sup>, corresponding to 34 mg NCs of this size in 0.5 ml MeOD. Resonances denoted as * are unidentified impurities.</p> <p><strong>3. Figure 3.pxp</strong>: Experimental data for <em>Figure 3. </em>Diffusion filtered <sup>1</sup>H NMR spectrum of the NC suspension in MeOD at 1.3 equivalents PA-hex-PEG added.</p> <p><strong>4. Figure 4.pxp</strong>: Experimental data for <em>Figure 4.</em> (A) and (B) <sup>31</sup>P NMR spectra of PA-PEG and PA-hex-PEG functionalized NCs at different D<sub>2</sub>O volume %. (C) Free ligand fraction for PA-PEG and PA-hex-PEG at different D<sub>2</sub>O volume %, determined by peak deconvolution.</p> <p><strong>5. Figure 5.pxp</strong>: Experimental data for <em>Figure 5.</em> (A) Ligand exchange performed between MEEAA functionalized NCs and nitrodopamine-mPEG. (B) <sup>1</sup>H NMR spectra before and after the ligand exchange titration performed in D<sub>2</sub>O with nitrodopamine-mPEG. 1.5 equivalents of nitrodopamine-mPEG were added and the pH was kept above 5 at all times during addition, the purified nitrodopamine functionalized NC spectrum was measured at pH = 7.4. CNC = 128 µmol.L<sup>-1</sup>, corresponding to 14.4 mg NCs of this size in 2 ml D<sub>2</sub>O.</p> <p><strong>6. Figure 6.pxp</strong>: Experimental data for <em>Figure 6. </em>Effect of pH on ligand binding and stability in water for purified NCs functionalized with PA-PEG, PA-hex-PEG and nitrodopamine-mPEG. (A) Bound and unbound ligand fraction in D<sub>2</sub>O based on NMR peak deconvolution at different pH values. (B) Z-average value of NCs in DLS at different pH values. (C) Zeta potential of the NCs at different pH values. All measurements were performed at constant ionic strength (0.01 mol.L<sup>-1</sup> NaCl) at 25°C</p> <p><strong>7. Figure 7.pxp</strong>: Experimental data for <em>Figure 7.</em> Stability of functionalized NCs in different concentrations of phosphate buffered saline (PBS) at pH 7.4 and 25°C. (A) Colloidal stability of functionalized nanocrystals measured using DLS z-average values at different PBS concentrations. (B) Stability of functionalized NCs in 2X PBS over time at pH 7.4 and 25°C.</p> <p><strong>8. Figure 9.pxp</strong>: Experimental data for <em>Figure 9.</em> UV-VIS spectra of purified nitrodopamine-mPEG functionalized NCs at different pH values in H2O.</p> <p> </p>
Dataset for Ligand-independent oligomerization of TACI is controlled by the transmembrane domain and regulates proliferation of activated B cells.
<p>This data set provides:</p> <p>a) Details about plasmids used in this study. It is a pdf file, describing expressed sequences and other features of plasmids listed in Supplementary Table 2.</p> <p>b) An Excel file with data used to make graphs of the publication</p>
Dual Ligand Enabled Nondirected C–H Chalcogenation of Arenes and Heteroarenes
<p>This folder /final_xyz_structures/ contains the DFT-optimized geometries (in .xyz format together with the gas-phase energy, E) accompanying the paper</p> <p> </p> <p>"Dual Ligand Enabled Nondirected C–H Chalcogenation of Arenes and Heteroarenes"</p> <p> </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>
The Reactivity of CsPbBr3 Nanocrystals toward Acid/Base Ligands
<p>This is a dataset generated for the publication titled "The Reactivity of CsPbBr<sub>3</sub> Nanocrystals toward Acid/Base Ligands". This version 0.1.0 contains two *.zip files: 1. A folder containing a set of subfolder for each of the figures reported in the main document and supporting information. 2. A folder containing the rawdata for the nuclear magnetic resonance (NMR) results shown in Figure 6 and 8 of the main document and Figure S8 of the supporting information. This folder has 3 folders named with the corresponding figure number, and a README file for instruction on the 2D NMR.</p>
NicheNet-v2: final networks and ligand-target matrices
<p>NicheNet-v2 networks and ligand-target matrices (for human and mouse). Can be used for NicheNet and MultiNicheNet analyses.</p>
Fuzzy Supertertiary Interactions within PSD-95 Enable Ligand Binding
<p>This file details the folder structure for the data files provided corresponding to the experiments and analysis performed in <em>Fuzzy Supertertiary Interactions within PSD-95 Enable Ligand Binding</em>. Correction parameters, results, and methods are detailed in the manuscript. The file “Sample Reference.xlsx” details the naming conventions for different FRET labeling site variants. “RefitDistanceTables.xlsx” provides the distances for all global fits of subsets of samples performed for classification of DMD structures.</p> <p> </p> <p>The .zip file "PDBDev_adtl_Datasets.zip" Contains additional files corresponding to submissions of structures from the corresponding manuscript to wwPDB-Dev.</p> <table> <tbody> <tr> <td>Folder</td> <td> </td> </tr> <tr> <td><strong>1. Single Molecule Experimental Data</strong></td> <td>.ht3 and .ptu files corresponding to time-tagged photon records collected using single-photon detectors with Hydra Harp TCSPC software and hardware. Folders are provided for each sample. Selection criteria and correction parameters are detailed in the manuscript. DO data were taken from bursts with S<sub>PIE</sub><0.1 while AO data were S<sub>PIE</sub>>0.9.</td> </tr> <tr> <td>1.1 BG</td> <td>Background files from which background signals were calculated for each sample. Subfolder for each sample.</td> </tr> <tr> <td>1.2 IRF</td> <td>IRF data files used for generation of IRF curves for fluorescence decay histogram analysis and generation of fFCS filters. Subfolder for each sample.</td> </tr> <tr> <td><strong>Fluorescence Decay Histograms</strong></td> <td>Data files for photon counts in each time bin following donor excitation pulse for Donor-only (DO), FRET (DA), and Instrument Response Function (IRF) curves used in fluorescence decay histogram analysis.</td> </tr> <tr> <td>2.1 Fit Curves</td> <td>Curves fit to decay histograms, in xyxy format (time delay after pulse, fit curve counts, time, residual)</td> </tr> <tr> <td><strong>Filtered FCS Curves and Filter Files</strong></td> <td>Data corresponding to four fFCS curves per sample: low-FRET-high-FRET (lohi), high-FRET-low-FRET (hilo), low-FRET-low-FRET (lolo), and high-FRET-high-FRET (hihi), as well as filter files generated in the Kristine software package using the .ht3 and .ptu data and IRF files from <strong>Single Molecule Experimental Data</strong>.</td> </tr> <tr> <td>3.1 Fits</td> <td>Fit curves and residuals for all samples. .cor files are simple text files in xy format (third column unused). .res, .fit files follow likewise.</td> </tr> <tr> <td><strong>Simulation AV-Derived Distance Values</strong></td> <td>Structures from DMD simulations and AV-derived interdye distances used for structural classification against experimental FRET data. Also includes references files for associating distances correctly with structures and FRET pairs.</td> </tr> <tr> <td>4.1 CoM AV Contours and PCA Data</td> <td>Files contain AV-derived distance information for all simulation structures from DMD, in compact npy format loadable via numpy.load. The text files contain center-of-mass positions in xyz format for the indicated domains.</td> </tr> <tr> <td><strong>5. MFD Histograms</strong></td> <td>Contains text files for MFD contour plot data, as well as FRET line equations for each sample. Text files are in matrix representation, with the first row and column being the x/y axis values and all internal values representing a 2D bin.</td> </tr> <tr> <td><strong>6. PDA Histograms</strong></td> <td>Contains time-binned histograms of smFRET data and modeling outputs from PDA fitting.</td> </tr> </tbody> </table> <p> </p>
Data set for Ligand additivity relationships enable efficient exploration of transition metal chemical space
<p>Dataset of transition metal complexes curated in pickle files and comma delimited format, scripts for CSD curation, and computed DFT properties for associated manuscript.</p>
Ligand Field Molecular Dynamics Simulation of Pt(II)-Phenanthroline Binding to N-Terminal Fragment of Amyloid-beta Peptide
<p>DL_POLY Classic input and output files for 10 MD simulations: 5 of free peptide, 5 with Pt(phen)</p>
Parameters and data set for all baseline methods used to formulate and evaluate the ligand NTBs-based strategy
Open the record for dataset details and reuse information.
Prepared protein-ligand complex from MOAD
<p>This is a protein-ligand data set. </p> <p>Each compressed (tar.gz) represents a collection of PDB IDs starting with the same initial ID, for example, 1.tar.gz conatins all PDB IDs that start with 1.</p> <p>With each subfolder, protein.pdb is a prepared protein structure file. This file has been properly added with correct hydrogen atoms, protonation information, and completion of missing heavy atoms and residues. The JSON and SDF files with the same name record a ligand, named according to the format: residue name_chain id_residue number. The JSON file contains information about the center coordinates of the ligand's heavy atoms and a box of dimensions +/- 10 angstroms around the ligand, which can be directly used as grid parameters in molecular docking. The SDF file provides the ligand atom coordinates with correct bond order information.</p>
MDD-Molecular Dynamics Dataset: Collection of protein-ligand complex simulations
<p>Dataset is part of the paper: https://chemrxiv.org/engage/chemrxiv/article-details/664c73f6418a5379b0de8152.</p> <p>This dataset consists of molecular dynamics (MD) simulations of 862 unique protein-ligand complexes, covering a wide range of protein families and diverse chemical classes of ligands. It is derived from publicly available repositories and represents the largest single source of MD simulations to date.</p> <p>All protein-ligand complexes included in the dataset were prepared following a standardized protocol. Missing atoms in the protein structures were added using the PDBFixer tool. The protein targets were parameterized using the AMBER99SB-ILDN force field, while ligands were parameterized with the ANTECHAMBER module within the ACPYPE tool. Ligand partial charges were determined to match the quantum-mechanically generated electrostatic potential via the Restrained Electrostatic Potential (RESP) method, and the remaining parameters were set using the GAFF2 force field. The molecular dynamics simulations were performed using GROMACS. The simulations were configured in a cubic simulation box with periodic boundary conditions and employed a TIP3P water model within an electrostatically neutral environment. The simulation protocol included an initial minimization cycle, followed by temperature equilibration in the NVT ensemble and pressure equilibration in the NPT ensemble. Production simulations were conducted over a period of 200 ns, with a timestep of 100 ps.</p> <p>Constructing a large, representative set of MD simulations poses challenges due to the high computational costs and complexities associated with preparing molecular systems. Moreover, given the limited number of suitable training examples (complexes) and the large volume of MD data from each simulation, careful filtering and feature selection are crucial. This dataset is valuable for exploring how molecular dynamics simulation data can be integrated with protein-ligand binding affinity prediction tasks, an essential component of in silico drug discovery pipelines. MD simulations, in particular, offer a dynamic view by illustrating the temporal interactions within protein-ligand complexes, potentially providing additional insights for affinity and specificity estimates.</p>
Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction
<p>This Zenodo repository provides comprehensive resources for the paper titled "Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction" published on <a href="https://academic.oup.com/bioinformatics/article/41/8/btaf429/8238154">Bioinformatics</a>. We created a dataset of 63,000 molecular dynamics simulations by performing 10 simulations of 10 ns on 6,300 complexes. Neural networks were developed to learn from this data in order to predict the binding affinities of protein-ligand complexes. The implementation of these neural networks are available on <a href="https://github.com/ICOA-SBC/MD_DL_BA" target="_blank" rel="noopener">github</a>. Our collection includes training/benchmark datasets, trained statistical models, and results on test sets (CSV & PDF files).</p> <p> </p> <p><strong>Training/benchmark datasets:</strong></p> <p>Training, validation and test sets are provided to train and evaluate the following neural networks:</p> <ul> <li>Pafnucy, Proli and Densenucy without MD data augmentation (dataset file names contain "initial")</li> <li>Pafnucy, Proli and Densenucy with MD data augmentation (dataset file names contain "MDDA")</li> <li>Pafnucy with/without MD data augmentation and Proli and Densenucy with MD data augmentation were also evaluated on the fep test set (test set file name contain "fep")</li> <li>Timenucy and Videonucy using spatiotemporal learning methods (dataset file names contain "4D")</li> <li>Pafnucy without MD data augmentation and on a reduced training set (dataset file names contain "reduced")</li> </ul> <p>For each training methodology (MD data augmentation and spatiotemporal learning), we provide the data for the whole complex, only the ligand or only the protein. Additionally for spatiotemporal learning, we provide the data with only the ligand using the tracking mode.</p> <p> </p> <p><strong>Statistical models:</strong></p> <p>We provide the models trained with Pafnucy, Proli, Densenucy, Timenucy and Videonucy. Each models were trained in 10 replicates. </p> <p>For Pafnucy, Proli, Densenucy, we provide the models trained with random and systematic rotations, as well as with or without MD data augmentation.</p> <p>For Proli, Densenucy, Timenucy and Videonucy, we provide the models trained on the whole complex, only the ligand or only the protein.</p> <p>For Pafnucy we also provide the models trained on the reduced set (5932 complexes).</p> <p> </p> <p><strong>Results on test sets (CSV & PDF files):</strong></p> <p>We provide the predictions on the PDBbind v.2016 core set.</p> <ul> <li>For spatiotemporal learning methods (Timenucy and Videonucy), there are predictions for only 83 complexes, as we did not perform simulations on the whole test set.</li> <li>For models trained with MD DA, predictions were carried on the crystallographic structures as well as on the frames extracted from the simulations performed on the test set (augmented test).</li> </ul> <p>Results on the FEP dataset are also provided for Pafnucy, Proli and Densenucy.</p> <p> </p> <p>The Raw MD data (~4.5 To) are stored, and can be visualized/downloaded, on the <a href="https://mdposit.mddbr.eu/#/browse?search=MDBind">MDDB</a>.</p> <p>This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-A0100712496 & 2022-AD011013521) and CRIANN (Grant 2021002).</p>
IR and GC-MS data for catalytic studies in "Perchlorate reduction catalyzed by dioxidomolybdenum(VI) complexes: Effect of ligand substituents"
Open the record for dataset details and reuse information.
IR data of the compounds published in "The Effect of Pyridine-2-thiolate Ligands on the Reactivity of Tungsten Complexes toward Oxidation and Acetylene Insertion"
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Dataset S4-6: Leveraging co-evolutionary insights and AI-based structural modeling to unravel receptor-peptide ligand-binding mechanisms
<h3>Significance statement:</h3> <p>This study presents proof-of-concept for a rapid and inexpensive alternative to classical structure-based approaches for resolving ligand-receptor binding mechanisms. It relies on a multilayered bioinformatic approach that leverages genomic data across diverse species in combination with AI-based structural modeling to identify true ligand and receptor homologues, and subsequently predict their binding mechanisms. <em>In silico </em>findings were validated by multiple experimental approaches, which investigated the effect of amino acid changes in the proposed binding pockets on ligand-binding, complex formation with a co-receptor essential for downstream signaling, and activation of downstream signaling. Our analysis combining evolutionary insights, <em>in silico</em> modeling and functional validation provides a framework for structure-function analysis of other peptide-receptor pairs, which could be easily implemented by most laboratories.</p> <h3><span>Zip file contains:</span></h3> <p><span>Dataset S4:</span><span> </span><strong><span>Plasmid maps of constructs used in this study.</span></strong></p> <p><span>Dataset S5:</span><span> </span><strong><span>AFM and AF3 predicted structures (.pdb) and AFM confidence metrics (.pae)</span></strong></p> <p><span>Dataset S6:</span><span> </span><strong><span>Unedited files (.tiff) of co-IP and western blotting.</span></strong></p>
Enthalpic classification of water molecules in target-ligand binding
<p>This repository contains supporting data for the manuscript entitled: Enthalpic classification of water molecules in target-ligand binding.</p>
qFit-ligand reveals widespread conformational heterogeneity of drug-like molecules in X-ray electron density maps
<p>Benchmark dataset and prospective cases tested for the development of <em>qFit-ligand</em>. Files included: refined single conformer models, un-refined qFit-ligand multiconformer models, and refined qFit-ligand multiconformer models. </p>
Metal Binding to Amyloid Beta 1-42: A Ligand Field Molecular Dynamics Study
<p>DL_POLY Classic inputs and trajectories (PDB format) for 3 x free, Cu and Pt simulations</p>
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