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5,942 results for “binding”
An Integrated Structural Model of the DNA Damage Responsive H3K4me3 Binding WDR76:SPIN1 Complex with the Nucleosome
<p>Serial Capture Affinity Purification (SCAP) is a powerful method to isolate a specific protein complex. When combined with cross linking mass spectrometry (XL-MS) and computational approaches one can build an integrated structural model of the isolated complex. Here, we applied SCAP to dissect a subpopulation of WDR76 in complex with SPIN1, a histone marker reader that specifically recognizes trimethylated histone H3 lysine4 (H3K4me3). In contrast to a previous SCAP analysis of the SPIN1:SPINDOC complex, histones and the H3K4me3 mark were copurified with the WDR76:SPIN1 complex. Next, interaction network analysis of copurifying proteins and microscopy analysis revealed a potential role of the WDR76:SPIN1 complex in the DNA damage response. Since we detected an extensive number of cross-linked sites were found between WDR76, SPIN1, and histones, we first built an integrated structural model of the complex which revealed that SPIN1 recognized the H3K4me3 epigenetic mark while interacting with WDR76. Finally, we then used the powerful Integrative Modeling Platform to build a structural model of WDR76 and SPIN1 bound to the nucleosome.</p>
Data for UV Plasmon-Enhanced Chiroptical Spectroscopy of Membrane-Binding Proteins, June 2024
<p>Extinction spectra of arrays of aluminum nanoparticles with diameters between 40 - 100 nm.</p> <p>Circular dichroism spectra of Tol-BINAP films on Al nanoparticle arrays before and after annealing of the films.</p> <p>Electromagnetic simulations of phase, electric (Eenh) field and magnetic (Henh) field enhancements as well as optical chirality density (Cenh) enhancement around flat aluminum hexagonal pyramid at specified wavelength. The simulations were performed with FDTD using Ansys Lumerical.</p>
Binding Affinity Prediction Workflow - Simulation Input Files and Absolute Binding Free Energies
<p>The Binding Affinity Prediction (BAP) workflow calculates absolute binding free energies for protein-ligand complexes by taking their crystal structures, converting them into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks, and analysing the resulting trajectories with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the free-energy estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the BAP workflow was run on the PDBbind 2020 (http://www.pdbbind.org.cn/index.php) refined set. This entry contains the MD simulation input files (BAPSimulationInputFiles.tar.gz) and the ABFE estimates (BAPBindingFreeEnergyEstimates.csv) obtained from four 250 ns trajectories for each complex. The MD simulations for more than 4000 complexes were run on the Leonardo supercomputer while the implicit-solvent calculations were carried out on Galileo, both operated by Cineca (Italy). The MD trajectories will be stored at Cineca for approx. 1 year after publication of this entry; contact Cineca's user support if you are interested in the trajectories.</p> <p>The README file describes how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Binding-Affinity-Prediction-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
P2PXML Dataset: Deep Geometric Framework to Predict Antibody-Antigen Binding Affinity
<p>In drug development, the efficacy of an antibody depends on how the antibody interacts with the target antigen. The strength of these interactions indicates how successful an antibody is in neutralizing an antigen. Therefore, the strength, measured by “binding affinity”, is a critical aspect of antibody engineering. In theory, the higher the binding affinity, the higher the chances are that the antibody is successful against the target antigen. Currently, techniques such as molecular docking and molecular dynamics are utilized in quantifying the binding affinity. However, owing to the computational complexity of the aforementioned techniques, running simulations for large antibodies/antigens remains a daunting task. Despite the commendable improvements in deep learning-based binding affinity prediction, such approaches are highly dependent on the quality of the antibody-antigen structures and they tend to overlook the importance of capturing the evolutionary details of proteins upon mutation. Further, most of the existing datasets for the task only include antibody-antigen pairs related to one antigen variant and, thus, are not suitable for developing comprehensive data-driven approaches. To circumvent the said complexities, we first curate the largest and most generalized datasets for antibody-antigen binding affinity prediction, consisting of both protein sequences and structures. Subsequently, we propose a deep geometric neural network comprising a structure-based model and a sequence-based model that considers both atomistic and evolutionary details when predicting the binding affinity. The proposed framework exhibited a 10% improvement in mean absolute error compared to the state-of-the-art models while showing a strong correlation between the predictions and target values. We release the datasets and code publicly https://drug-discovery-entc.github.io/p2pxml/ to support the development of antibody-antigen binding affinity prediction frameworks for the benefit of science and society. </p>
Cooperative Binding and Chirogenesis in an Expanded Perylene Bisimide Cyclophane
<p>Additional data to report <a title="DOI URL" href="https://doi.org/10.1021/jacs.4c08073">https://doi.org/10.1021/jacs.4c08073</a>:</p> <p>The encapsulation of more than one guest molecule into a synthetic cavity is a highly desirable yet a highly challenging task to achieve for neutral supramolecular hosts in organic media. Herein, we report a neutral perylene bisimide cyclophane, which has a tailored chiral cavity with an interchromophoric distance of 11.2 Å, capable of binding two aromatic guests in a π-stacked fashion. Detailed host–guest binding studies with a series of aromatic guests revealed that the encapsulation of the second guest in this cyclophane is notably more favored than the first one. Accordingly, for the encapsulation of the coronene dimer, a cooperativity factor (α) as high as 485 was observed, which is remarkably high for neutral host–guest systems. Furthermore, a successful chirality transfer, from the chiral host to encapsulated coronenes, resulted in a chiral charge-transfer (CT) complex and the rare observation of circularly polarized emission originating from the CT state for a noncovalent donor–acceptor assembly in solution. The involvement of the CT state also afforded an enhancement in the luminescence dissymmetry factor (<em>g</em><sub>lum</sub>) value due to its relatively large magnetic transition dipole moment. The 1:2 binding pattern and chirality-transfer were unambiguously verified by single-crystal X-ray diffraction analysis of the host–guest superstructures.</p>
Datasets for predicting TF binding using Virtual ChIP-seq
<p>This repository contains datasets necessary for using the Virtual ChIP-seq software.</p> <p>Virtual ChIP-seq requires the following datasets to predict transcription factor binding:</p> <ul> <li> <p>chipExpDir_AtoH_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters A-H.</p> </li> <li> <p>chipExpDir_ItoZ_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters I-Z.</p> </li> <li> <p>refTables_V1.1.0.tar.gz: PhastCons genomic conservation, FIMO PWM scores for JASPAR motifs, and ChIP-seq data of ENCODE and Cistrome database.</p> </li> <li> <p>hg38_chrsize.tsv: Length of chromosomes in hg38</p> </li> <li> <p>trainedModels_V1.0.0.tar.gz: Virtual ChIP-seq scikit-learn trained models saved in joblib format</p> </li> <li> <p><CellType>.tar.gz: Pre-calculated matrices suitable for training with other algorithms or re-training with Virtual ChIP-seq.</p> </li> </ul> <p>Some predictive features of TF binding are the same in each cell type and are stored together for simplicity in refTables_V1.0.0.tar.gz. You can use datasets from other cell types (named here as <CellType>.tar.gz) for the purpose of re-training the model. The <CellType>.tar.gz files contain pre-calculated predictive features of transcription factor binding in 4 chromosomes (5, 10, 15, 20).</p> <p>These features include:</p> <ul> <li> <p>PhastCons genomic conservation</p> </li> <li> <p>FIMO score for sequence motifs of TF in the JASPAR database</p> </li> <li> <p>Chromatin accessibility</p> </li> <li> <p>TF binding in ENCODE + Cistrome DB datasets</p> </li> <li> <p>Virtual ChIP-seq expression score</p> </li> </ul> <p> </p>
A molecular dynamics study of adenylyl cyclase: the impact of ATP and G-protein binding
<p>Adenylyl cyclases (ACs) catalyze the biosynthesis of cyclic adenosine monophosphate (cAMP) from adenosine triphosphate (ATP) and play an important role in many signal transduction pathways. The enzymatic activity of ACs is carefully controlled by a variety of molecules, including G-protein subunits that can both stimulate and inhibit cAMP production. Using homology models developed from existing structural data, we have carried out all-atom, microsecond-scale molecular dynamics simulations on the AC5 isoform of adenylyl cyclase and on its complexes with ATP and with the stimulatory G-protein subunit Gsα. The results show that both ATP and Gsα binding have significant effects on the structure and flexibility of adenylyl cyclase. New data on ATP bound to AC5 in the absence of Gsα notably help to explain how Gsα binding enhances enzyme activity and could aid product release. Simulations also suggest a possible coupling between ATP binding and interactions with the inhibitory G-protein subunit Gαi.</p> <p>All-atom molecular dynamics simulations were performed with the GROMACS 5 package. The simulations were carried out in an NTP ensemble at a temperature of 310 K and a pressure of 1 bar using a Bussi velocity-rescaling thermostat (t<sub>T</sub> = 1 ps) and a Parrinello-Rahman barostat (t<sub>P</sub> = 1 ps). We provide the atomistic trajectories of the following 6 systems after 400 ns of equilibration:</p> <ul> <li>AC5</li> <li>AC5+ATP</li> <li>AC5+Gsα</li> <li>AC5+ATP+Gsα</li> <li>AC5+FOK</li> <li>AC5+ATP+FOK</li> </ul> <p>In each trajectory, the frames are saved each 20 ps.</p>
Predictive models for off-target binding profiles generation
<p>Models for predicting off-target binding, built with Conformal Prediction, and the <a href="http://cpsign-docs.genettasoft.com">CPSign software</a>. The dataset is part of an upcoming publication (Manuscript in preparation), which will provide more details.</p> <p>The dataset is a GZipped Tar archive, with the models as Java Archive (JAR) files. For every JAR-file, there is also a corresponding audit log, with the extension ".audit.json", produced by the workflow software (<a href="http://scipipe.org">SciPipe</a>) used to train the models. This audit file contains all the shell commands used in the workflow that produced the models.</p>
Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues
<p>This dataset contains predictions of Virtual ChIP-seq for binding of 36 transcription factors in Roadmap Epigenomics dataset tissues with matched DNase-seq and RNA-seq data.</p> <p>Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was > 0.3.</p> <p>Each subfolder contains gzipped BED files. Each file is named as <Tissue>_<Age>_<TF>_<Accession>_Predictions.bed.gz. Columns correspond to Chromosome, Start, End, <Tissue>_<Age>_<TF>_<Accession>, Posterior probability</p> <p>You can use the posterior probabilities provided in Virchip_PosteriorCutoffs_V3.0.0.tsv. These are posterior probability cutoffs which maximized MCC in H1-hESC cell type, or are set to 0.4 if there was no ChIP-seq data of that TF in H1-hESC (0.4 is the mode of all optimal posterior probability cutoffs in H1-hESC).</p>
Absolute Binding Free Energies with OneOPES
<h1>Supporting Material: Absolute Binding Free Energies with OneOPES</h1> <p>Further information about the content of the directory can be found in the README file included</p> <p>The Plumed input files can also be found on Plumed-Nest https://www.plumed-nest.org/eggs/24/017/</p> <p> </p> <h3>Aknowledgements</h3> <p>The authors acknowledge PRACE and the Swiss National Supercomputing Centre (CSCS) for large supercomputer time allocations on Piz Daint, project IDs: pr126, s1107, s1169, s1228. FLG acknowledges the Swiss National Science Foundation and Bridge for financial support (projects number: 200021_204795, CRSII5_216587 and 40B2-0_203628). The authors are grateful to Nicola Piasentin for helping in devising the error-informed stopping strategy and for carefully reading the manuscript.</p> <p> </p> <h3>Reference</h3> <p>Absolute Binding Free Energies with OneOPES<br>Maurice Karrenbrock, Alberto Borsatto, Valerio Rizzi, Dominykas Lukauskis, Simone Aureli, and Francesco Luigi Gervasio<br>The Journal of Physical Chemistry Letters 0, <em> 15<br>DOI: 10.1021/acs.jpclett.4c02352 </em></p> <p> </p> <h3>Versions :</h3> <ul> <li>1.0.0 First version</li> <li>1.0.1 Bugfix: added the missing topology files (top.top)</li> <li>1.1.0 Bugfix: added missing index files (index.ndx) and missing Slurm files (run.slr). New: added a directory with what is needed to equilibrate the systems</li> </ul>
Predictive modeling of moonlighting DNA-binding proteins
<p>This repository contains the codes used for the prediction of moonlighting proteins in the paper "Predictive modeling of moonlighting DNA binding proteins".</p> <p>The repository is organized as the following:</p> <p>1. The DNA binding protein identifiers and their features that were used to train the models for the prediction of DNA binding Moonlighting proteins.</p> <p>2. Five feature sets were used to create Catboost models that make predictions. The source code for generating predictions based on all the features and predictions based on particular features is supplied. In addition, the source code for generating maximum and average ensemble predictions has been made available. A detailed explanation is given in README file.</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>
Binding site plasticity regulation of the FimH catch-bond mechanism: Molecular Dynamics dataset
<p>Dataset of Molecular Dynamics simulations and analysis scripts used in the article "Binding site plasticity regulation of the FimH catch-bond mechanism" [<a href="https://doi.org/10.1016/j.bpj.2023.05.029">paper</a>][<a href="https://doi.org/10.1101/2022.11.15.516604">bioRxiv</a>].</p> <p>Contains:</p> <ul> <li>Replica Exchange with Solute Scaling (REST2) simulations of the FimH protein lectin domain in its two main allosteric states (Associated and Separated), in presence and absence of its synthetic ligand heptyl α-ᴅ-mannose (input files and trajectories of the unscaled replicas)</li> <li>Replica Exchange Umbrella Sampling (REUS) simulations of the liganted systems along a collective variable (CV) describing binding site opening (input files and trajectories)</li> <li>REUS simulations in presence of a pulling force on the protein-ligand complex.</li> </ul> <p>See the article for more details.</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>
Dataset for FigS2F in "DNA is the allosteric driver for the asymmetric binding of homodimer estrogen-related receptor"
<p><strong>Abstract</strong></p> <p>The estrogen-related receptors (ERRs, NR3B), orphan members of the steroid hormone receptor (SR) subfamily (NR3), are crucial for the transcriptional control of cellular energy metabolism. As basal members of NR3 subfamily, the ERRs are key elements for the understanding of how the binding of SRs to DNA evolved from monomeric to dimeric palindromic binding sites. To unravel the initial steps of DNA selection by SRs, we combined structural, biophysical and phylogenetic studies. Our results unveil the molecular mechanisms of the ERR dimerization which are imprinted in the protein itself with DNA acting as an allosteric driver by allowing the formation of a novel extended asymmetric dimerization region (KR-box). Phylogenetic analyses suggest that the dimerization asymmetry used by ERRs is an ancestral feature necessary for establishing a strong overall dimerization interface, which was progressively lost in the course evolution by other SRs.</p> <p><strong>Methods</strong></p> <p>Ancestral character reconstruction and stochastic mapping were performed under R version 4.1.2 using the make.simmap function as implemented in the phytools package version 1.0-1. Character evolution was inferred using a model of symmetrical transition rates between the character states (SYM). 10 000 character histories were sampled to allow the incorporation of the uncertainty associated with the transition between different states. Inferred state frequencies for ancestral nodes were plotted using the describe.simmap function.</p> <p><strong>Usage Notes</strong></p> <p>This dataset contains all the files necessary to reproduce Figure S2 of the associated paper (Patel et al., in preparation). Those files are:</p> <p>- script_FigS2F.R : the R script necessary to load the data and process them as indicated in the methods section. The outputs of character mappings are also indicated in the script file, in order that the user can compare them with the results he would get on his/her own computer.</p> <p>- tree_FigS2F.nex: the backbone tree used for character mapping</p> <p>- FigS2F_KR_box.csv: the data table containing the presence-absence data regarding the KR box motif for each nuclear receptor.</p>
DATASET: Protein Binding Leads to Reduced Stability and Solvated Disorder in the Polystyrene Nanoparticle Corona
<p>This dataset contains the DLS, CD, fluorescence, ITC, TEM, and ANS raw data used for the manuscript.</p>
Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions
<p>This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions."</p> <p>The dataset is composed of the following files:</p> <p>* pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space</p> <p>* s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above</p> <p>* view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view"</p> <p>* s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue.</p> <p>* s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above.</p> <p>* viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg"</p> <p>* 0readme -- brief instructions on how to view the trajectories</p> <p>* colors.vmd -- utility script for VMD</p> <p>* covmacros.vmd -- VMD script to define coronavirus spike subdomains</p> <p>* fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles</p> <p>* diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations</p> <p>* pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes</p> <p>Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br> </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>
All Atom Molecular Dynamics Simulations of Lopinavir at the Binding Pocket of SARS-CoV2 Main Protease
<p>Data includes all of the trajectories (2000) of classical all-atom molecular dynamics (MD) simulations of lopinavir at the binding pocket of SARS-CoV2 main protease target. In order to decrease the size of the file only protein and ligand trajectories were provided. Simulation has been performed with Desmond. Protein–ligand complexes were obtained by Glide/SP docking program. Complex was placed in the cubic boxes with explicit TIP3P water models that have 10.0 Å thickness from surfaces of protein. The system is neutralized by adding counter ions, and salt solution of 0.15M NaCl was also used to adjust the concentration of the systems. The long-range electrostatic interactions were calculated by the particle mesh Ewald method. A cutoff<br> radius of 9.0 Å was used for both van der Waals and Coulombic interactions. The temperature was set as 310K initially, and Nose–Hoover thermostat was used for adjustment. Martyna–Tobias–Klein protocol was employed to control the pressure, which was set at 1.01325 bar. The time-step was assigned as 2.0 fs. The default values were used for minimization and equilibration steps, and finally 500 ns production run was performed for the simulations.</p>
Dataset for "Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites"
<p>Datasets for preprint (https://doi.org/10.1101/2020.07.10.190785) entitled:</p> <p>"Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites"</p> <p>Contains:</p> <p>- All data files for scripts quoted in the preprint and deposited at https://github.com/krasileva-group/hvNLR</p> <p>- Clade Membership Tables</p> <p>- Clade Alignment Files</p> <p>- Clade Trees</p> <p>- Excel files for Figure S1, Figure S2, and Table 1</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.