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641 results for “Protein interaction”
A method for Boolean analysis of protein interactions at a molecular level.
<p>CellProfiler output files for the manuscript ' A method for Boolean analysis of protein interactions at a molecular level'. The present dataset includes all raw, numerical data generated by CellProfiler analyses performed for the aforementioned manuscript and are presented in it. It also contains the descriptive data and statistical analyses data for all the plots present in the manuscript. </p>
Spectroscopy study of albumin interaction with negatively charged liposome membranes: Mutual structural effects of the protein and the bilayers
<p>Deconvolution of the signals of carbonyl groups in the ATR-FTIR spectra of lipids in the region 1700–1760 cm<sup>−1</sup> obtained during liposome incubation with albumin.</p>
Expression and purification of Huntingtin interaction protein High mobility group protein B1 (HMGB1) – 2017/10/04
<p>Huntingtin structure function open lab notebook project. </p>
Data for: Intersectin1 promotes clathrin-mediated endocytosis by organizing and stabilizing endocytic protein interaction networks
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Potential Interactions of the 20 Arabidopsis thaliana Mitogen-Activated Protein Kinases (MPKs)
<p>A comprehensive dataset detailing potential interactions of the 20 <em>Arabidopsis thaliana</em> MPKs.</p>
STRING protein-protein interaction network (v11.5)
<p>STRING protein-protein interaction network was downloaded from the STRING website (https://string-db.org/cgi/download?sessionId=bIAz6gR8tk72).</p> <p>Version: 11.5</p>
ROS-Specific Huntingtin Interactions: Reproducible high and medium confidence huntingtin interacting proteins
<p>Mass spec identification of DNA damage-specific huntingtin protein-protein interactions.</p>
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>
Analysis of Protein--Protein Interactions in Crystallo
<p>This upload contains data and analysis scripts used in the manuscript "Towards computational design of improved protein crystal resolution". From this dataset, Figure 1 can be generated using the compare_crystal_scores.R script.</p>
Preliminary experiments to assess the suitability of DLS for protein interaction studies with huntingtin-HAP40 2019/08/12
<p><strong>Project: </strong>Biophysical investigation of the HTT interactome</p> <p><strong>Experiment: </strong>Preliminary experiments to assess the suitability of DLS for protein interaction studies with huntingtin-HAP40</p> <p><strong>Date completed:­ </strong>2019/08/12</p> <p><strong>Rationale: </strong>HTT is a multi-valent protein interactor and scaffold. It is likely that many of the proteins HTT interacts with form weak or transient interactions. One method which may be fruitful to detect this type of interaction is dynamic light scattering – a method which allows in solution calculation of protein particle radius. HTT-HAP40 Q23 (<a href="https://zenodo.org/record/3383265">https://zenodo.org/record/3383265</a>), PACSIN1 (<a href="https://zenodo.org/record/3256589">https://zenodo.org/record/3256589</a>) and PFN1A (kindly donated by Opher Gileadi and Tracy Keates) were analysed in a dilution series by DLS to assess signal to noise and concentration dependency of protein radius and polydispersity. </p>
Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks
<p>The datasets used to evaluate Enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) against predicting the pro-longevity or anti-longevity effect of model organism gene. This repo also includes the pretrained encoders that obtained the best predictive performance for each organism (see Table 2).</p>
Data table 3 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
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Predicted protein-protein interactions of the human tyrosine kinase Lck by AF2Complex
<p>Structural models and Supplementary data described in the publication:</p> <p>Predicting protein interactions of the kinase Lck critical to T cell modulation</p> <p>Structure (Cell Press), 2024</p> <p>Reference Authors: Mu Gao and Jeffrey Skolnick</p> <p><br>screening_results -- Virtual PPI screening results of the protein kinase Lck (SH3-SH2 domains as the bait).<br>predicted structural models.zip -- Compressed structural models described in the publication.<br>af2c_input_features -- Input features used to predict protein complex models with AF2Complex. </p>
Raw data for Jin et. al "Intersectin1 promotes clathrin-mediated endocytosis by organizing and stabilizing endocytic protein interaction networks"
<h2><strong><span>Intersectin1 promotes clathrin-mediated endocytosis by organizing and stabilizing endocytic protein interaction networks</span></strong></h2> <p><span> </span></p> <p><span>Meiyan Jin<sup>1,2*</sup>, Yuichiro Iwamoto<sup>1</sup>, Cyna Shirazinejad<sup>1</sup>, David G. Drubin<sup>1,3**</sup></span></p> <p><sup><span>1 </span></sup><span>Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA</span></p> <p><sup><span>2</span></sup><span> Current Address: Department of Biology, University of Florida, Gainesville, Fl 32611, USA</span></p> <p><sup><span>3</span></sup><span> Lead author</span></p> <p><sup><span>* </span></sup><span>Correspondence: meiyan.jin@ufl.edu</span></p> <p><sup><span>**</span></sup><span>Correspondence: </span><span>drubin@berkeley.edu</span></p>
Dataset for protein docking and MD simulations of CARD-CARD interactions of the apoptosome
<p>Protein docking complexes and MD simulation trajectories for the Apaf1-Caspase9 CARD-CARD interactions and the cross-dockings between Apaf1 CARD and Caspase2 CARD, and RAIDD CARD and Caspase9 CARD, respectively (RAIDD and Caspase2 CARD domains interacting in the PIDDosome). Further details in the README file.</p>
DrugBLIP: Exploring the Protein-Molecule Interaction Mechanisms with a Multi-task Learning Graph Transformer
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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>
Motif-guided Identification of KRAS-interacting Proteins
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Identification of PIAS1-direct interacting proteins through DSP-based crosslinking
<p>As a conserved post-translational modification, SUMOylation has been shown to play important roles in chromatin related biological processes including transcription. However, how the SUMOylation machinery associates with chromatin is not clear. To underatand how nuclear matrix associated SUMO enzymes regulate chromatin related function, we attempted to identify the direct interacting proteins of PIAS1, a major SUMO E3 protein. We made use of the crosslinking reagent dithiobis succinimidyl propionate (DSP), which is a water-insoluble, membrane-permeable homobifunctional N-hydroxysuccimide ester that can reversibly cross-link different proteins by primary amines in cells.</p>
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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.