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15 results for “ligand – protein interactions”
Project files provided as supporting information to the manuscript "Ligand-protein interactions in lysozyme investigated through a dual-resolution model"
<p><strong>README file for the project files provided as supporting information to the manuscript "Ligand-protein interactions in lysozyme investigated through a dual-resolution model"</strong></p> <p>February 12, 2020</p> <p>Authors: Raffaele Fiorentini, Kurt Kremer and Raffaello Potestio</p> <p>================================</p> <p>Overview</p> <p>The dataset is organised in three (compressed) subfolders (see the tree diagrams in each section):</p> <p>- annihilation<br> - decoupling<br> - density</p> <p>The figure deltaG_binding_ann_dec_comparison.png shows the results of binding free energy calculations comparing the values obtained both for annihilation and decoupling.</p> <p>The figure deltaG_binding_annih_gromacs_espp.png displays the results for Binding FE, comparing the values obtained in GROMACS and ESPResSo++.</p> <p>The README.pdf file contains detailed information about these folders and their content.</p> <p>================================</p> <p>The "annihilation" folder contains all results concerning the calculation of binding free energy in case of annihilation and it is divided in two parts: </p> <p>- complex<br> - ligand</p> <p>In "complex" are reported the results of Ligand-Protein FE both in ESPResSo++ and GROMACS. All simulations are fully-atomistic. </p> <p>In "ligand" are reported the results of ligand solvation free energy both in ESPResSo++ and GROMACS. All simulations are fully-atomistic. </p> <p>====</p> <p>The "decoupling" folder contains all results concerning the calculation of binding free energy in case of decoupling and it is divided in three parts: </p> <p>- complex-DualRes<br> - complex-FullyAT<br> - ligand</p> <p>In "complex-DualRes" are reported the results of Ligand-Protein FE only in ESPResSo++ (GROMACS cannot do decoupling). The system is simulated in Dual-Resolution. It is possible to find the trajectory files in the sub-directories "lambdaindex-0" and "lambdaindex-30".</p> <p>In "complex-fullyAT" are reported the results of Ligand-Protein FE only in ESPResSo++. The system simulated is fully-atomistic. It is possible to find the trajectory file in the sub-directories "lambdaindex-0" and "lambdaindex-30".</p> <p>In "ligand" are reported the results of ligand solvation free energy only in ESPResSo++. All simulations are fully-atomistic. It is possible to find the trajectory file in the sub-directories "lambdaindex-0" and "lambdaindex-20".</p> <p>====</p> <p>The "density" folder contains the data for the tuning of the c parameter of the steric repulsion among residues. This parameter is tuned so that the water density attains the value computed in all-atom simulations.</p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction
<p>The code, dataset, and model weights are described in the paper "Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction."</p> <p> </p> <p><strong>experiment_results.zip:</strong> Contains generated results that can reproduce the result from the reported paper.</p> <p><strong>benchmark.zip:</strong> Contains docking and affinity input data of the interformer. You can use the source code to make predictions and reproduce the number of the reported paper.</p> <p><strong>checkpoints.zip: </strong>Contains one weight for the Energy and four PoseScore and Affinity models.</p> <p><strong>source_code_1.0.zip:</strong> Contains the initial version of the source code.</p> <p><strong>interformer_train.tar.gz:</strong> Contains prepared training data for interformer. poses/ contains all structure need for training, poses/ligand contains the re-docking poses generated by interformer energy, poses/ligand/rcsb contains the conformation of reference ligand, poses/pocket contains all pocket extract by raw PDB from rcsb, poses/uff contains all ligand conformation minimized using UFF from reference ligand, and train/ contains the training csv.</p> <p><strong>baseline_results.tar.gz:</strong> Contains the predictions from three methods: Interformer, DiffDock, and DeepDock. The results align with the exact numbers reported in the paper. For further details, please refer to the <em>eda/ </em>directory.</p> <p> </p> <p>You can also find the newest version of the source code at <a href="https://github.com/tencent-ailab/Interformer" target="_blank" rel="noopener">https://github.com/tencent-ailab/Interformer</a></p> <p> </p>
Protein-ligand interactions between mAMCase and chitin.
<p>This directory contains all files to analyze mouse AMCase-ligand interactions as presented in <strong>Supplemental Figure 6</strong> in the manuscript <a href="https://www.biorxiv.org/content/10.1101/2023.06.03.542675">Díaz et al.<em> </em>(2023)</a>.</p> <p>Structure models were analyzed in PyMOL. Figures were compiled using Adobe Illustrator.</p> <p> </p> <p>Contact:</p> <p>Roberto Efraín Díaz, robertoefrain.diaz@ucsf.edu</p> <p>James Fraser, jfraser@fraserlab.com</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>
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>
Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"
<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p> PDB files of all structures used for the test calculations. <br> The PDB files correspond to the protonated structures obtained <br> as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p> Jupyter Notebook for generating all plots included in the manuscript, <br> including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p> - min_congrad.mdp: input file for partial optimization of protonated <br> protein--ligand complexes with Gromacs</p> <p> PyADF input scripts:</p> <p> - sp_single.pyadf: single-point calculations of separate protein and ligand<br> - sp_complex.pyadf: single-point calculation of protein-ligand complex<br> - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p> These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</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>
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>
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>
Integrated Protein-Ligand Interaction Database
<p><strong>IPLID</strong> integrates protein-ligand interaction data from multiple well-known resources, including BindingDB, ChEMBL, DrugBank, GPCRDB, PubChem, LINCS-HMS KinomeScan, and four published kinome assay results. Our database can facilitate projects in <em>machine learning or deep learning-based drug development </em>and other applications by providing integrated data sets appropriate for many research interests. Our database can be utilized for small-scale (e.g. kinases or GPCRs only) and large-scale (e.g. proteome-wide), qualitative or quantitative projects. With its ease of use and straightforward data format, IPLID offers a great educational resource for computer science and data science trainees who lack familiarity with chemistry and biology.</p> <p> </p> <p>Data statistics</p> <p>Target (data type) Activities | Unique chemicals | Unique proteins | File name</p> <p>All (binary) 96318 | 18107 | 3107 | integrated_binary_activity.tsv</p> <p>All (numerical) 2798365 | 683009 | 5876 | integrated_continuous_activity.tsv</p> <p>CYP450 (binary) 67552 | 17273 | 47 | integrated_cyp450_binary.tsv</p> <p>CRT (binary) 4152 | 1219 | 412 | integrated_cancer_related_targets_binary.tsv</p> <p>CDT (binary) 519 | 349 | 88 | integrated_cardio_targets_binary.tsv</p> <p>DRT (binary) 4433 | 1325 | 852 | integrated_disease_related_targets_binary.tsv</p> <p>FDA (binary) 6217 | 1521 | 592 | integrated_fda_approved_targets_binary.tsv </p> <p>GPCR (binary) 1958 | 545 | 129 | integrated_gpcr_binary.tsv</p> <p>NR (binary) 1335 | 657 | 264 | integrated_nr_binary.tsv</p> <p>PDT (binary) 1469 | 674 | 404 | integrated_potential_drug_targets_binary.tsv</p> <p>TF (binary) 1966 | 998 | 304 | integrated_tf_binary.tsv</p> <p> </p> <p>*Abbreviations: CYP450 (Cytochrome P450), CRT (Cancer-Related Target), CDT (Cardiovascular Disease candidate Target), DRT (Disease-Related Target), FDA (FDA-approved target), GPCR (G-Protein Coupled Receptor), NR (Nuclear Receptor), PDT (Potential Drug Target), TF (Transcription Factor)</p> <p>*These protein classifications are from UniProt database and the Human Protein Atlas (<a href="https://www.proteinatlas.org/">https://www.proteinatlas.org/</a>)</p> <p><a href="https://github.com/XieResearchGroup/DrugTargetInteraction/blob/master/iplid/IPLID_data_stat.png">IPLID data statistics</a></p>
Integrated Protein-Ligand Interaction Database
<p>Computational prediction of genome-wide protein-ligand interactions plays a key role in drug discovery, toxicology, and in many other applications. Despite recent advances in <em>deep learning</em>, the large quantity of high-quality data required for training and evaluating models has impeded its applications in computational drug development. To obviate this problem, we have developed an <em>integrated Protein-Ligand Interaction Database </em>(<strong>IPLID</strong>). <strong>IPLID</strong> integrates protein-ligand interaction data from multiple well-known resources, including BindingDB, ChEMBL, DrugBank, GPCRDB, PubChem, LINCS-HMS KinomeScan, and four published kinome assay results. <strong>IPLID</strong> is enabled with search functionalities specifically designed for machine learning, particularly deep learning projects. Users can retrieve numerically or binary labeled (e.g. pki, pkd, or binary) protein-ligand interaction data for different classes of proteins (e.g. GPCRs, kinases, FDA-approved targets, protein products of cancer-related genes, etc.). To facilitate the development of benchmarks for training and testing of machine learning algorithms, it also provides chemical-chemical structure similarity scores calculated by a well-established method, Tanimoto coefficient (Jaccard similarity) of two Extended Connectivity Fingerprint (ECFP4) molecular representations. Protein sequence similarities by BLAST score comparison and position-specific scoring matrices against UniRef50 sequence database are also available for more complicated protein-ligand interaction modeling projects. We believe our database can facilitate projects in <em>machine learning or deep learning-based drug development</em> and other applications by providing integrated data sets appropriate for many research interests. Our database can be utilized for small-scale (e.g. kinases or GPCRs only) and large-scale (e.g. proteome-wide), qualitative or quantitative projects. With its ease of use and straightforward data format, <strong>IPLID</strong> offers a great educational resource for computer science and data science trainees who lack familiarity with chemistry and biology.</p> <p> </p> <ul> <li>Activities are in <em>tab-delimited</em> text file formats (.tsv).</li> <li>Binary activities are under '<em>binary_activity</em>' directory, and numerical activities are under '<em>numerical_activity</em>' directory.</li> <li>File names are in "(<strong>targets</strong>)_(<strong>activity_type</strong>).tsv"</li> <li>Long target names are abbreviated; abbreviations listed below.</li> <li>Ligand-ligand similarity scores are under '<em>ligand_info</em>' directory.</li> <li>Protein-protein similarity scores and position-specific scoring matrices are under '<em>protein_info</em>' directory.</li> <li>Primary ligand-id and protein-id are <em>InChIKey</em> and <em>UniProt ID</em>, respectively.</li> </ul> <p>*<strong>Abbreviations</strong>: CYP450 (Cytochrome P450), CRT (Cancer-Related Target), CDT (Cardiovascular Disease candidate Target), DRT (Disease-Related Target), FDA (FDA-approved target), GPCR (G-Protein Coupled Receptor), NR (Nuclear Receptor), PDT (Potential Drug Target), TF (Transcription Factor)</p> <p>*These protein classifications are from UniProt database and the Human Protein Atlas (<a href="https://www.proteinatlas.org/">https://www.proteinatlas.org/</a>)</p>
Assessing interaction recovery of predicted protein-ligand poses
<p>We provide the following data:</p> <ol> <li>PDB files containing protein structures from the PoseBusters dataset prepared with OpenEye's SPRUCE protein preparation software</li> <li>SDF files containing the corresponding PoseBusters ligand in its crystal pose</li> </ol> <p>This data is provided for the 256 PoseBuster targets used in our paper "Assessing interaction recovery of predicted protein-ligand poses" [1] with associated code at https://github.com/Exscientia/plif_validity.</p> <p> </p> <h3>References</h3> <p>[1] Errington D, Schneider C, Bouysset C, Dreyer FA, Assessing interaction recovery of predicted protein-ligand poses, arXiv; 2024. Available from: https://arxiv.org/abs/2409.20227 </p>
Inferred stacking interactions for a dataset of proteins, nucleic acids and ligands
Open the record for dataset details and reuse information.
PDBscreen with multiple data augmentation strategies suitable for training protein-ligand interaction prediction methods
<p>PDBscreen with multiple data augmentation strategies suitable for training protein-ligand interaction prediction methods.</p> <p>PDBscreen is the training dataset for EquiScore.</p>
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