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10 results for “ligand binding site”
LOBSTER (Ligand Overlays from Binding SiTe Ensemble Representatives)
<p>LOBSTER ("Ligand Overlays from Binding SiTe Ensemble Representatives") is a dataset of ligand overlays designed to evaluate small molecule superposition tools.</p> <p><br>Based on all structures from the RCSB PDB, the dataset generation and filtering protocols are fully automated to avoid subjectivity in the selection of protein-ligand complexes and to gain the largest possible set of refined compounds. Affinity and activity data have been processed to select ligands with a high ligand efficiency.<br>Ligands were superimposed in their crystal pose by aligning the corresponding binding pockets to so-called ensembles. For poses generated in benchmark experiments, this offers an objective comparison to the superimposed ligand crystal poses. A clustering of ensembles created with the same protein-ligand complexes ensures the diversity of the LOBSTER set.<br>The 671 ligand ensembles comprise a total of 3212 unique ligands from 3521 different protein-ligand complexes. A total of 72 734 ligand pairs have been derived from the ensembles. Ten subsets were generated from the pairs according to the shape overlap of the pairs, quantified by the Shape Tversky Index.</p>
PSnpBind: A database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow
<p>A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is freely available at <a href="https://psnpbind.org">https://psnpbind.org</a>.<strong> </strong>The source code of the tools used in constructing PSnpBind is available on <a href="https://github.com/ammar257ammar/PSnpBind-Build">GitHub</a>.</p>
Data for manuscript: The Conformational Space of the SARS-CoV-2 Main Protease Active Site Loops is Determined by Ligand Binding and Interprotomer Allostery
<div>The data is provided as a part of the manuscript "<strong>The Conformational Space of the SARS-CoV-2 Main Protease Active Site Loops is Determined by Ligand Binding and Interprotomer Allostery</strong>". This repository includes an archive with folders:</div> <div> </div> <div><strong>md_data </strong></div> <div> <ul> <li>a directory with MD data for all simulation systems considered in the manuscript. Initial and final conformations are provided.</li> </ul> </div> <div> </div> <div><strong>fig_data</strong></div> <div> <ul> <li>a directory with the data underlying all the main text in the manuscript. </li> </ul> </div> <div> </div> <div>Videos S1-S3 are also included.</div>
scPDB BO1 subset (protein ligand-binding sites)
<p>The BO1 subset of the scPDB database.</p> <p>BO1 consists in 766 pairs of non-redundant binding-sites<br> (383 similar pairs, 383 dissimilar pairs).</p> <p>BO1 was describbed in:<br> ---<br> Eguida, M., & Rognan, D. (2020).<br> A computer vision approach to align and compare protein cavities:<br> application to fragment-based drug design.<br> Journal of Medicinal Chemistry, 63(13), 7127-7142.<br> https://doi.org/10.1021/acs.jmedchem.0c00422<br> ---</p> <p>The scPDB was recently describbed in:<br> ---<br> Desaphy, J., Bret, G., Rognan, D., & Kellenberger, E. (2015).<br> sc-PDB: a 3D-database of ligandable binding sites—10 years on.<br> Nucleic acids research, 43(D1), D399-D404.<br> https://doi.org/10.1093/nar/gku928<br> ---</p> <p>The scPDB is available at:<br> http://bioinfo-pharma.u-strasbg.fr/scPDB/<br> </p>
Vertex balanced dataset (protein ligand-binding sites)
<p>Vertex balanced dataset.</p> <p>This dataset consists in 676 pairs of binding-sites<br> (338 similar pairs, 338 dissimilar pairs).</p> <p>The original Vertex dataset was describbed in (but is not downloadable from):<br> ---<br> Chen, Y. C., Tolbert, R., Aronov, A. M., McGaughey, G., Walters, W. P., & Meireles, L. (2016).<br> Prediction of protein pairs sharing common active ligands using protein sequence,<br> structure, and ligand similarity.<br> Journal of chemical information and modeling, 56(9), 1734-1745.<br> https://doi.org/10.1021/acs.jcim.6b00118<br> ---</p> <p>Its balanced version was recently describbed in (but is not downloadable from):<br> ---<br> Eguida, M., & Rognan, D. (2020).<br> A computer vision approach to align and compare protein cavities:<br> application to fragment-based drug design.<br> Journal of Medicinal Chemistry, 63(13), 7127-7142.<br> https://doi.org/10.1021/acs.jmedchem.0c00422<br> ---<br> </p>
LABind: Identifying Protein Binding Ligand-Aware Sites via Learning Interactions Between Ligand and Protein
<p>This dataset contains the three datasets used in LABind. For each dataset, we have saved the corresponding FASTA sequence files, the associated labels (0 for non-binding and 1 for binding), and the corresponding PDB files.</p>
The dataset used in the article "A point cloud graph neural network for protein-ligand binding site prediction"
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Fragment screening using biolayer interferometry reveals ligands targeting the SHP-motif binding site of the AAA+ ATPase p97
<p>Biosensor techniques have become increasingly important for fragment-based drug discovery during the last years. The AAA+ ATPase p97 is an essential protein with key roles in protein homeostasis and a possible target for cancer chemotherapy. Currently available p97 inhibitors address its ATPase activity and globally impair p97-mediated processes. In contrast, inhibition of cofactor binding to the N-domain by a protein-protein-interaction inhibitor would enable the selective targeting of specific p97 functions. Here, we describe a biolayer interferometry-based fragment screen targeting the N-domain of p97 and demonstrate that a region known as SHP-motif binding site can be targeted with small molecules. Guided by molecular dynamics simulations, the binding sites of selected screening hits were postulated and experimentally validated using protein- and ligand-based NMR techniques, as well as X-ray crystallography, ultimately resulting in the first structure of a small molecule in complex with the N-domain of p97. The identified fragments provide insights into how this region could be targeted and present first chemical starting points for the development of a protein-protein interaction inhibitor preventing the binding of selected cofactors to p97.</p> <p><br> Here we publish the primary data of the biolayer-interferometry (BLI), STD-NMR, HSQC-NMR and mixed-solvent MD simulation results.<br> For BLI, the sensorgrams of 10 binding fragments identified in the screening are given. Measurements were conducted with the N-domain and the ND1-construct of p97. Additionaly, the measurements with ADP as positive control (n=6) are provided.<br> Four of the 10 fragments were selected as most promising hits for further investigation: TROLL2, TROLL7, TROLL8 and TROLL12. For these fragemnts, the STD-NMR build-up data, the HSQC-NMR spectra and the representative poses from the mixed solvent MD simulations used for CORCEMA predictions are provided. For TROLL2 the anomalous map of the bromine atom of the crystal structure (PDB entry: 7PUX) is given.</p>
Data from: Common internal allosteric network links anesthetic binding sites in a pentameric ligand-gated ion channel
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Genome-wide mapping of ligand-dependent ERα binding sites in the ERα AF-2 mutant mouse uterus
GEO Series GSE137196. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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