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21 results for “ligand modeling”
LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models
<p>The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named <strong>LigPCDS</strong>. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.</p> <p> </p> <p>The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries.</p> <p>The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% <span lang="EN-GB">[-19.4,20.</span><span lang="EN-GB">2]</span> and 77.4% <span lang="EN-GB">[-11.7,12.1]</span> in terms of Intersection over Union (mIoU) metric and between 62.4% <span lang="EN-GB">[-18.8,19.</span><span lang="EN-GB">7]</span> and 87.0% <span lang="EN-GB">[-8.4,8.8]</span> in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record.</p> <p>The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines. </p> <p>The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand</p> <p>This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.</p>
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>
Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles
<p>Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe<sup>3+</sup> containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe<sup>2+</sup><sub>(1−3x)</sub>Fe<sup>3+</sup><sub>(2x)</sub>S<sup>2-</sup><sub>(1-y)</sub>(S<sub>n</sub><sup>2-</sup>)<sub>y</sub>. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe<sup>2+</sup>Fe<sup>3+</sup><sub>2</sub>S<sup>2-</sup><sub>4</sub>) along a metal oxidation path, and Fe<sup>2+</sup>S<sup>2-</sup> and pyrite (Fe<sup>2+</sup>S<sub>2</sub><sup>2-</sup>) along a ligand oxidation path, respectively.</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>
Ising model for nanocrystal ligand exchange
Open the record for dataset details and reuse information.
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>
Cryo-EM and X-ray crystallography ligands represented as 3D voxel grids for training deep learning models
<p>Ligand datasets used to train and evaluate the models studied in <em>"Ligand Identification using Deep Learning</em><em>"</em> by Karolczak, J. <em>et al.</em></p> <p>The blobs_full.tar.gz and cryoem_blobs.zip files contain compressed 3D numpy arrays (*.npz) of all the ligand blobs extracted from X-ray and cryo-EM PDB deposits prior to quality filtering. The npz file names correspond to the PDB ID, chain, residue number, and ligand name of the extracted blob. The cmb_data.csv file contains the tabular data used to train the CheckMyBlob model. The X-ray data were later divided into training and testing subsets according to the xray_train.csv and xray_holdout.csv files, respectively. The ligand_mapping.csv file contains the mapping from ligand IDs to ligand group names. Finally, the cryoem_qscores.csv file contains Q-scores that were used to filter cryo-EM ligands.</p>
A machine learning model reveals expansive downregulation of ligand-receptor interactions enhancing lymphocyte infiltration in melanoma with developed resistance to Immune Checkpoint Blockade
<p>Data repository containing the data to reproduce the results and findings that are published in:</p> <p>Sahni, S., Wang, B., Wu, D. <em>et al.</em> A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. <em>Nat Commun</em> <strong>15</strong>, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4</p>
Consensus machine-learning models for protein-ligand binding affinity estimation
<p><strong>Motivation:</strong> In structure-based virtual screening, machine learning based scoring function gained popularity in the last few years as they outperformed classical scoring function. The protein-ligand system can be encoded by a set of orthogonal descriptor spaces, which are then mined by machine learning algorithms to find a relationship with the binding affinity experimental value.</p> <p><strong> </strong></p> <p><strong>Results:</strong> In this work we propose our modelling approach to derive a new scoring function, derived from a combination of multiple descriptor spaces coupled with machine learning algorithms ensembled in consensus. The SF has been trained on the PDBbind v.2019 data and has been extensively internally and externally validated on a large set of complexes. When benchmarked on the PDBbind core set, it achieved better performance than state-of-the-art counterparts, scoring: R<sub>Pearson </sub>= 0.85-0.86 r<sup>2</sup> = 0.70-0.72 and RMSE = 1.15-1.21. As highlights: (i) an applicability domain definition has been implemented to delimit the SF’s application boundaries, and (ii) a mechanistic interpretation is proposed by investigating the contribution of each protein-ligand atom pairs in the prediction of the binding affinity, which could provide a support in the lead-optimization process.</p> <p><strong> </strong></p> <p><strong>Availability and implementation:</strong> Our scoring function is freely available through the webportal: <a href="https://predictor.exscalate.eu/">https://predictor.exscalate.eu/</a></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>
DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.
<p>test and training data.</p>
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>
Ligand docking models of vanillic acid decarboxylase (VdcCD)
<p>Docking of substrates and intermediates to the vanillic acid decarboxylase, VdcCD, in models of domain closure.</p>
Characterizing the consensus residue specificity and surface of Bcl-2 binding to BH3 ligands using the knob-socket model
<p><span>Cancer cells bypass cell death by changing the expression of the BCL-2 family of proteins, which are apoptotic pathway regulators. Upregulation of pro-survival BCL-2 proteins or downregulation of cell death effectors BAX and BAK interferes with the initiation of the intrinsic apoptotic pathway. In normal cells, apoptosis can occur through pro-apoptotic BH3-only proteins interacting and inhibiting pro-survival BCL-2 proteins. When cancer cells over-express pro-survival BCL-2 proteins, a potential remedy is the sequestration of these pro-survival proteins through a class of anti-cancer drugs called BH3 mimetics that bind in the hydrophobic groove of pro-survival BCL-2 proteins. To improve the design of these BH3 mimetics, the packing interface between BH3 domain ligands and pro-survival BCL-2 proteins was analyzed using the Knob-Socket model to identify the amino acid residues responsible for interaction affinity and specificity. A Knob-Socket analysis organizes all the residues in a binding interface into simple 4 residue units: 3-residue sockets defining surfaces on a protein that pack a 4th residue knob from the other protein. In this way, the position and composition of the knobs packing into sockets across the BH3/BCL-2 interface can be classified. A Knob-Socket analysis of 19 BCL-2 protein and BH3 helix co-crystals reveal multiple conserved binding patterns across protein paralogs. Conserved knob residues such as a Gly, Leu, Ala and Glu most likely define binding specificity in the BH3/BCL-2 interface, whereas other residues such as Asp, Asn, and Val are important for forming surface sockets that bind these knobs. These findings can be used to inform the design of BH3 mimetics that are specific to pro-survival BCL-2 proteins for cancer therapeutics.</span></p>
Characterizing the consensus residue specificity and surface of Bcl-2 binding to BH3 ligands using the knob-socket model
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Ligand-mediated PAI-1 inhibition in a mouse model of peritoneal carcinomatosis
GEO Series GSE189169. Homo sapiens. 19 samples. Type: Expression profiling by array; Protein profiling by protein array.
Low-intensity pulsed ultrasound-mediated blood-brain barrier opening increases anti programmed death-ligand 1 delivery and efficacy in Gl261 mouse model
GEO Series GSE220909. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
CD137 (4-1BB) ligand mediates CD4 T-cell immuno-surveillance of pre- lymphoma B cells in a lymphoma-prone mouse model
GEO Series GSE196100. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Dynamic Ligand Impacts on Dissolved Iron Distributions in a Global Ocean Biogeochemical Model
<p>Annual mean CESM ocean output files on the coarse resolution gx3v7 grid.<br> Archived here in support of the paper submitted to JAMES by<br> Sherman et al. "Dynamic Ligand Impacts on Dissolved Iron Distributions in a<br> Global Ocean Biogeochemical Model".</p> <p> </p> <p>gdev.450 = dynamic ligand<br> gdev.457 = constant ligand = 1 nM<br> gdev.460 = implicit ligand</p> <p>gdev.458 = dynamic ligand + Sol Fe Depo X3<br> gdev.459 = constant ligand = 1 nM + Sol Fe Depo X3<br> gdev.461 = implicit ligand + Sol Fe Depo X3</p>
Proneurogenic ligands defined by modeling developing cortex growth factor communication networks
GEO Series GSE84482. Mus musculus. 9 samples. Type: Expression profiling by array.
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