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85 results for “Ligand Binding”
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>
Ligand binding remodels protein side chain conformational heterogeneity
<p>While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decrease and relative hydrophobicity increases. Across a series of 13 inhibitor bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from NMR studies suggesting that residual side chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.</p>
Associated Data: RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features
<p>Additional digital data to "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features" (ChemRxiv preprint:<a href="https://doi.org/10.26434/chemrxiv.12636704.v1">https://doi.org/10.26434/chemrxiv.12636704</a>).</p> <p>Associated code can be found at: <a href="https://github.com/HITS-MCM/RASPDplus">https://github.com/HITS-MCM/RASPDplus</a></p> <p>Files:</p> <ul> <li>weights.tar.gz: contains the model weights of one random dataset split and its associated crossvalidation folds. Used for standard RASPD+ evaluation.</li> <li>additional_model_replicates.tar.gz: contains the remaining models trained on the full set of descriptors.</li> <li>external_test_sets.tar.gz: contains the descriptor tables for all external test sets used</li> <li>dude.tar.gz: contains the descriptor tables for and several identifier lists for evaluation on the Directory of Useful Decoys - Enhanced (DUD-E)</li> <li>run_outputs.tar.gz: Performance metric data and predicted values created during the model training and evaluation runs. Basis for the figures and metrics in the manuscript.</li> </ul> <p> </p>
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset
<p><b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b></p><p>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.</p>
Dataset for: All-atom simulations reveal the intricacies of signal transduction upon binding of HLA-E ligand to the transmembrane inhibitory CD94/NKG2A receptor
<p>This dataset contains relevant structures, input and other files that are associated with our article "<em>All-atom simulations reveal the intricacies of signal transduction upon binding of HLA-E ligand to the transmembrane inhibitory CD94/NKG2A receptor", available at https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c00249</em></p>
Supplementary information: How robust is the ligand binding transition state?
<p>We have used the REVO weighted ensemble approach followed by Markov state models to identify the ligand unbinding transition states for five ligands unbinding from the enzyme soluble epoxide hydrolase (sEH). This repo provides the <em><strong>counts matrices, properties and state (cluster) labels</strong></em> of the markov state models. The counts matrices can be converted to conformation space networks using CSNAnalysis software (<a href="https://github.com/ADicksonLab/CSNAnalysis">https://github.com/ADicksonLab/CSNAnalysis</a>). The <em><strong>networks</strong></em> are also provided in the gexf formatted files to be visualized in gephi (<a href="https://github.com/gephi/gephi">https://github.com/gephi/gephi</a>).</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>
SAXS-based titrations of periplasmic binding proteins against known ligands
<p>This dataset contains the small-angle X-ray scattering intensities of a series of titrations between proteins HisBP, GlnBP, and DEBP against a number of amino acids. A total of five synchrotron beamline sessions are included to test for reproducibility across setups and total exposure time per sample:</p> <ul> <li>DESY P12 (4s)</li> <li>ESRF BM29 (5s)</li> <li>Australian Synchrotron SAXS/WAXS (20s)</li> <li>Diamond B21 (40s)</li> </ul> <p>The accompanying manuscripts can be found on bioRxiv MS 715193, entitled: "Structure-based screening of binding affinities via small-angle X-ray scattering". It is currently under consideration with te Biophysical Journal.</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>
Binding constants of clinical drugs and other organic ligands with human and mammalian serum albumins
<p>The dataset contains literature values of the experimental equilibrium binding constants of drugs and some other organic ligands with human and mammalian (predominantly bovine) serum albumins. There are 1755 records gathered from 346 original literature sources describing the albumin affinity of 324 different substances. The data were extracted from both articles and existing protein binding databases applying strict data selection rules in order to exclude the values influenced by the third-party compounds. For each experiment with a particular ligand–albumin system found in literature we provide (if possible) the following details: albumin source organism, ligand chemical name, canonical SMILES, InChIKey, the binding (either association <em>K<sub>a</sub></em> or dissociation <em>K<sub>d</sub></em>) constant value in molarity-based scale, temperature in K, albumin and ligand concentrations, buffer pH, composition, and concentration, experimental method, model used for the binding constant calculation, DOI or link to the source paper. For the results obtained using independent binding sites model, the average number of binding sites <em>n</em> is also provided. If two different types of binding sites were suggested, we put the second site-specific binding constant <em>K<sub>a2</sub></em> or <em>K<sub>d2</sub></em> and the number of the second-type sites<em> </em> <em>n</em><sub>2</sub> into separate columns. For some systems, the enthalpies of binding have also been determined either from the temperature dependence of the binding constant or using direct calorimetric measurements. Their values are also given in the respective column. The dataset can be used as the reference one, for the development of predictive models to calculate the binding constants, and for the choice of the experimental setup in the future albumin binding studies.</p>
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>
PLBD (Protein Ligand Binding Database) table description XML file
<p>PLBD (Protein Ligand Binding Database) table description XML file<br> =================================================================</p> <p>General<br> -------</p> <p>The provided ZIP archive contains an XML file "main-database-description.xml" with the description of all tables (VIEWS) that are exposed publicly at the PLBD server (https://plbd.org/). In the XML file, all columns of the visible tables are described, specifying their SQL types, measurement units, semantics, calculation formulae, SQL statements that can be used to generate values in these columns, and publications of the formulae derivations.</p> <p>The XML file conforms to the published XSD schema created for descriptions of relational databases for specifications of scientific measurement data. The XSD schema ("relational-database_v2.0.0-rc.18.xsd") and all included sub-schemas are provided in the same archive for convenience. All XSD schemas are validated against the "XMLSchema.xsd" schema from the W3C consortium.</p> <p>The ZIP file contains the excerpt from the files hosted in the https://plbd.org/ at the moment of submission of the PLBD database in the Scientific Data journal, and is provided to conform the journal policies. The current data and schemas should be fetched from the published URIs:</p> <p> https://plbd.org/<br> https://plbd.org/doc/db/schemas<br> https://plbd.org/doc/xml/schemas</p> <p>Software that is used to generate SQL schemas, RestfulDB metadata and the RestfulDB middleware that allows to publish the databases generated from the XML description on the Web are available at public Subversion repositories:</p> <p> svn://www.crystallography.net/solsa-database-scripts<br> svn://saulius-grazulis.lt/restfuldb</p> <p>Usage<br> -----</p> <p>The unpacked ZIP file will create the "db/" directory with the tree layout given below. In addition to the database description file "main-database-description.xml", all XSD schemas necessary for validation of the XML file are provided. On a GNU/Linux operating system with a GNU Make package installed, the XML file validity can be checked by unpacking the ZIP file, entering the unpacked directory, and running 'make distclean; make'. For example, on a Linux Mint distribution, the following commands should work:</p> <p> unzip main-database-description.zip<br> cd db/release/v0.10.0/tables/<br> sh -x dependencies/Linuxmint-20.1/install.sh<br> make distclean<br> make</p> <p>If necessary, additional packages can be installed using the 'install.sh' script in the 'dependencies/' subdirectory corresponding to your operating system. As of the moment of writing, Debian-10 and Linuxmint-20.1 OSes are supported out of the box; similar OSes might work with the same 'install.sh' scripts. The installation scripts require to run package installation command under system administrator privileges, but they use *only* the standard system package manager, thus they should not put your system at risk. For validation and syntax checking, the 'rxp' and 'xmllint' programs are used.</p> <p>The log files provided in the "outputs/validation" subdirectory contain validation logs obtained on the system where the XML files were last checked and should indicate validity of the provided XML file against the references schemas.</p> <p>Layout of the archived file tree<br> --------------------------------</p> <p> db/<br> └── release<br> └── v0.10.0<br> └── tables<br> ├── Makeconfig-validate-xml<br> ├── Makefile<br> ├── Makelocal-validate-xml<br> ├── dependencies<br> ├── main-database-description.xml<br> ├── outputs<br> └── schema</p>
Computational data on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR)
<p>This repository contains the data and files for the computational study on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR). <br> The repository is organized into different folders as described below:</p> <p>====================================================================================================<br> 1_Starting_structure<br> This folder contains the starting structures of the four binding modes obtained from the crystallographic study. These structures serve as the initial configurations for the Molecular Dynamics (MD) simulations.</p> <p>====================================================================================================<br> 2_mdp_files<br> This folder contains two subfolders:</p> <p>1_MD<br> The "1_MD" subfolder includes the MD simulation files in the mdp format. These files define the parameters and settings for running the MD simulations with Gromacs version 2019.3.</p> <p>2_US<br> The "2_US" subfolder includes the files required for performing Umbrella Sampling (US) simulations for each of the binding modes using Gromacs version 2021.3. Within each mode folder, you will find the following files:</p> <p>constraint: Position restraints files used in the Umbrella Sampling simulations.<br> pull: mdp files containing the settings for pulling in the US simulations.<br> us: mdp files used for the US simulations.</p> <p>====================================================================================================<br> 3_MD_results<br> This folder contains the results of the MD simulations. It includes the structure files (.gro) and trajectory files (.xtc) for each simulation. Due to the large size of the files, the solvent has been excluded, and the results are provided at every 1 nanosecond (ns) interval.</p> <p>====================================================================================================<br> 4_US_results<br> The "4_US_results" folder includes the trajectories of the US simulations for each of the binding modes. For each mode, two trajectories are provided.</p> <p>====================================================================================================<br> 5_pdb<br> This folder includes the pdb files of the simulated structures of the two binding modes after the equilibration step.</p> <p>====================================================================================================</p> <p>We acknowledge funding by the German Research Foundation (DFG) through the Emmy Noether Young Group Leader Programme (CK, project KO 5423/1-1), the Swedish e-Science Research Centre (SeRC, ML, PN), the Swedish Research Council (PN, Grant No. 2018-4343). Computing resources were provided by the Swedish National Infrastructure for Computing (SNIC).</p>
FABind: Fast and Accurate Protein-Ligand Binding
<p>The preprocessed PDBbind2020 dataset for paper "FABind: Fast and Accurate Protein-Ligand Binding" with associated code at <a href="https://github.com/QizhiPei/FABind">https://github.com/QizhiPei/FABind</a>.</p><p>The dataset files are saved as .pt and lmdb file for the convenience of use.</p><p>We follow the same preprocessing as TankBind.<br><br><strong>Paper Abstract:</strong></p><p>Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at <a href="https://github.com/QizhiPei/FABind">https://github.com/QizhiPei/FABind</a>.</p>
Proper modelling of ligand binding requires an ensemble of bound and unbound states
<p>Crystallographic data for structures described in the manuscript "Proper modelling of ligand binding requires an ensemble of bound and unbound states".</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>
Target-ligand binding affinity from single point enthalpy calculation and elemental composition
<p>This repository contains supporting files for the manuscript entitled: Target-ligand binding affinity from single point enthalpy calculation and elemental composition.</p>
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
<p>This repository contains materials for the paper, "Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties", published in <a href="https://onlinelibrary.wiley.com/doi/10.1002/minf.202300249">Molecular Informatics.</a></p> <p>`data.zip` contains vocabulary and dataset files for identifying chemical vocabularies and key chemical words associated with protein ligand binding. </p> <p>`results.zip` comprises outputs specific to vocabularies and datasets, as well as various related statistics.</p> <p> </p> <p> </p>
A Comprehensive Dataset of protein-protein interactions and Ligand Binding Pockets for Advancing Drug Discovery
<p>This dataset presents a comprehensive collection of structural data related to protein-protein interactions (PPIs) and ligand binding pockets. The dataset includes high-quality structural information that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes and associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for repurposing protein partners.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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