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3,650 results for “antibody”
MMSEQS meets AntiRef: reference clusters of human antibody sequences
<p>This data set contains pre-computed mmseqs databases for the antiref fasta files created by <em>Briney et al.</em> </p> <p>Please cite the original work if you use any of the databases provided here.</p> <p>Sources:</p> <ul> <li><a href="https://github.com/brineylab/antiref">Antiref GitHub</a></li> <li><a href="../records/7474336">Antiref Zenodo</a></li> <li><a href="https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad109/7247530?login=true">Antiref Paper</a></li> </ul> <p> </p> <p>The mmseqs databases were created as follows:</p> <p> </p> <p>```</p> <p>aria2x -x16 -s16 --input-file antiref_links.txt<br>snakemake -s antiref_mmseqs.smk --jobs 1 --cores 1 --local-cores 250</p> <p>```</p>
Cortical slice labelled with anti GFP and VAMP2 antibodies - sample image for software testing of "Contacting synapse" protocol
<p><strong>Image 1.tif is a Brain slice</strong>. This 16 bits confocal stack of pictures ((801x711 pixels x33 z slices - pixel size 78.17 nm) of a brain slice has been taken at 93x (LeicaHC PL APO CS2 93x/1.30 GLYC) in sequential mode with two channels : one dedicated to the GFP detection, and the other one to synpatic boutons labelled with VAMP2 protein. VAMP2 protein are expressed at glutamatergic presynaptic sites and is usually found apposed to Post Synaptic Density. This is a good sample to test "contacting synapse" software. Here GFP cells were electroporated with various plasmid. The aim of the software is to identify if expression of those plasmid within the GFP labelled cell, influence the density of synapse contacting this GFP cells. Here presynaptic contact are identified through the use of antibodies to VAMP2 proteins.</p>
Antibody Domainbed
<div> <div>The Antibody Domainbed dataset has been designed to</div> <ol> <li>incorporate state-of-the-art (SOTA) approaches in domain generalization to build robust predictors for the binding properties of biological sequences</li> <li>assess domain generalization algorithms in a real-world problem inspired by therapeutic antibody discovery.</li> </ol> <div>The dataset (sequence + structure) and corresponding backbones have been seamlessly integrated within the largest out-of-distribution (OOD) benchmark - Domainbed.</div> </div>
Molecular and functional properties of human Plasmodium falciparum CSP C-terminus antibodies
<p>AIRR Community-compliant information comprising all antibodies described in EMBO Mol Med 15:e17454 [DOI:10.15252/emmm.202317454].</p>
SIRAH-CoV2 initiative: S1 Receptor Binding Domain in complex with human antibody CR3022 (PDBid: 6W41)
<p>This dataset contains the trajectory of a 12 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV-2 receptor binding domain in complex with a human antibody CR3022 (PDB id: 6W41). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycans have been removed from the structures.</p> <p>The file 6W41_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W41_SIRAHcg_12us_prot.tar contains only the protein coordinates, while 6W41_SIRAHcg_12us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W41_SIRAHcg_12us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w41_SIRAHcg_prot.prmtop 6w41_SIRAHcg_prot.ncrst 6w41_SIRAHcg_prot_12us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins
<p>This data repository contains all previously unpublished raw data files for the manuscript “Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins” by Wolfgang Esser-Skala, Marius Segl, Therese Wohlschlager, Veronika Reisinger, Johann Holzmann, and Christian G. Huber. See <em>readme.md</em> for further information.</p>
MD simulation data: An Entropic Safety Catch Controls Hepatitis C Virus Entry and Antibody Resistance
<p><strong>Background</strong></p> <p>Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. All software is available from http://ambermd.org/. </p> <p><strong>Contents</strong></p> <p>There are three tarball (<strong>.tar.gz</strong>) files containing the <strong>core simulation data</strong>: one for wild type (WT), the second for the I438V A524T mutant and the third for the S449P mutant. Each contains:</p> <p>1. a source PDB (<strong>.pdb</strong>) file</p> <p>2. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.</p> <p>There is an additional tarball containing the <strong>control files</strong> <strong>and scripts</strong> used for running the MD simulations:</p> <p>1. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model</p> <p>2. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps</p> <p>3. control file <strong>prod.ctl</strong> used for the production run </p> <p>4. Executable <strong>run_prod</strong> that was used to perform the production run</p> <p>5. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs</p> <p>6. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs.</p>
Dataset for the Endothelin-converting enzyme 1 antibody screening study
<p>This project contains the following underlying data included in a study aimed at characterizing six antibodies agaisnt Endothelin-converting enzyme 1 (ECE1). The study is available on Zenodo (DOI: 10.5281/zenodo.7459248).</p>
Dataset for the Angiogenin antibody screening study
<p>This project contains the following underlying data included in a study aimed at characterizing three commercial antibodies against Angiogenin (ANG) protein. The study is available on Zenodo (DOI: 10.5281/zenodo.7671286).</p>
Dataset for the Sphingosine 1-phosphate receptor 1 (S1PR1) antibody screening study
<p><strong><span>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</span></strong></p> <p><em>This project contains the following underlying data included in a study aimed at characterizing nine commercial antibodies against Sphingosine 1-phosphate receptor 1 (S1PR1) protein, encoded by S1PR1 gene. The study is available on Zenodo (<a href="https://doi.org/10.5281/zenodo.10819189">https://doi.org/10.5281/zenodo.10819189</a>).</em></p> <p><em>The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).</em></p>
Dataset for the Prolow-density lipoprotein receptor-related protein1 (LRP-1) antibody screening study
<p>This project contains the following underlying data included in a study aimed at characterizing ten commercial antibodies against Prolow-density lipoprotein receptor-related protein 1 (LRP-1) protein, encoded by <em>LRP1 </em>gene. The study is available on Zenodo (DOI:10.5281/zenodo.7971951).</p>
Dataset for the QPRTase (Nicotinate-nucleotide pyrophosphorylase [carboxylating]) antibody screening study
<p> This project contains the following underlying data included in a study aimed at characterizing four commercial antibodies against Nicotinate-nucleotide pyrophosphorylase [carboxylating] (QPRTase) protein, encoded by the <em>QPRT</em> gene. The study is available on Zenodo (DOI: 10.5281/zenodo.7459387).</p>
Dataset for the Calponin-3 antibody screening study
<p>This dataset contains the following underlying data included in a study aiming at characterizing eight commercial antibodies for the Calponin-3 (CNN3) protein. The study is available on Zenodo (DOI: 10.5281/zenodo.8356134).</p>
Dataset for the TGM2 (Protein-glutamine gamma-glutamyltransferase 2) antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aimed at characterizing seventeen commercial antibodies against Protein-glutamine gamma-glutamyltransferase 2 (TGM2) protein, encoded by TGM2 gene. The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.10819348">https://doi.org/10.5281/zenodo.10819348</a>).</em></p>
Dataset for the Casein kinase II subunit alpha antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing ten antibodies for Casein kinase II subunit alpha protein. The study is available on Zenodo (<a href="https://doi.org/10.5281/zenodo.10818214">https://doi.org/10.5281/zenodo.10818214</a>). </em></p> <p><em>The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).</em></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>
Raw Data for the Protocol: Antibody-Assisted Selective Isolation of Purkinje Cell Nuclei
<p>Sun1/sfGFP+, Pcp2-Cre+ and Sun1/sfGFP+, Pcp2-Cre- cryosectioned cerebella immunostained for the Myc tag (files 3037, 3046), which is fused to the GFP protein, Calbindin (files 3038, 3047) and Hoechst (files 3036, 3045). </p> <p> </p> <p>Original uncropped images from western blot analysis of TOM20, Histone H3, and GAPDH.</p>
Dataset for the Rab3A antibody screening study
<p>This dataset contains the following underlying data included in a study aimed at characterizing sixteen commercial antibodies for Rab3A (UniProt ID P20336) protein. </p>
Dataset for the Huntingtin antibody screening study
<p><strong><span>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</span></strong></p> <p><em>This dataset contains underlying data from a study that evaluated twenty commercial antibodies agaisnt Huntingtin in western blot, immunoprecipitation and immunofluorescence. The study is accessible on our Zenodo community (<a href="https://doi.org/10.5281/zenodo.11582780">https://doi.org/10.5281/zenodo.11582780</a>) and serves as a research tool to facilitate reproducible and reliable Huntingtin resarch.</em></p> <p><em>The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF). </em></p>
Supplementary data - Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective
<p>Analysis files and scripts for <a href="https://doi.org/10.1101/2024.06.21.600107" target="_blank" rel="noopener">associated manuscript</a>. </p> <ul> <li>CR3022.zip: script (in Rust) and necessary data to run said script for CR3022 analysis with the results from running the script.</li> <li>MA-analysis-script.zip: script (in Rust) and necessary data to run said script for automated analysis of MA benchmark results.</li> <li>MA-analysis-data.zip: data from running the MA-analysis-script, containing all MA and Stitch output files.</li> <li>MA-analysis-data-EMPEM.zip: data from running MA and Stitch on the EMPEM benchmark.</li> </ul>
ScienceDex guides
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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.