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641 results for “Protein Protein Interaction”
Data of "Pen mates' interactions, potential precursors of damaging behaviours, object manipulation, straw rooting, and primary activity: A detailed data set in undocked pigs under dietary protein restriction"
<p>Damaging behaviours, such as tail biting, are common problems in pig production, compromising animal welfare and causing economic losses. Detailed studies are impeded by the difficulty of directly observing these behaviours. Tail biting is a broader phenomenon that begins long before lesions manifest, and behavioural problems caused by various stressors present themselves weeks before they escalate to damaging behaviour, resulting in serious injuries. Therefore, detailed data on behaviours, which can be considered precursors of tail biting, such as oral and nasal manipulation of conspecifics, should be collected. The present data were collected in the course of a large study on the genetic potential of protein efficiency, in which the crude protein content in the diet was reduced to 80% of the recommendations. Dietary protein reduction is a promising way to reduce nitrogen emissions in pig manure, but its implications for animal welfare are not yet clear. Pigs differ phenotypically and genetically in their ability to utilise dietary proteins; therefore, there might be individual differences in how they cope with the protein reduction. Here, we present detailed data of focal observations of 95 pigs at an experimental farm with undocked tails that were fed a protein-reduced diet. Pigs were observed directly in their home pens for 5 min each on four different days. All actions directed towards objects in the pen, interactions with and confrontations among pen mates, and straw rooting behaviour and general activity were recorded. After the behavioural observations, wounds on different parts of the body and the cleanliness of the pigs were noted. The protein efficiency of 94 pigs was obtained. The data set comprises six tables. The first table contains information on the animals, including the identities of their parents, farrowing group, sex, and protein efficiency. The other data tables contain four 5-min observations of each pig on 10 object-manipulation behaviours; 150 interaction behaviours, including reactions; 14 confrontation behaviours and their outcomes and reactions; 10 mounting behaviours, including reactions; two rooting behaviours; seven basic behaviours; and an index of general activity. The observations took place under comparatively good housing conditions. Pigs were not tail-docked and were given fresh straw daily, <em>ad libitum</em> access to feed, floor space above the legal requirements (only a partially slatted floor), and daily cleaning of pens, and they were closely monitored for signs of damaging behaviour; all of these are favourable conditions as they limit stress and the risk of damaging behaviour. These data can be used to further explore the relationships of specific behaviours and phenomena and their association with protein efficiency. The ethogram can be used as a template for further observations. Practitioners could use the data to support pigs’ need for occupation, such as by providing sufficient straw.</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>
A dataset for predicting protein-protein interactions in humans
Open the record for dataset details and reuse information.
Dataset for article: Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes
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High-density Chemical Cross-linking for Modeling Protein Interactions
<p>Supporting parameters, IMP code and data for Mintseris & Gygi "High-density Chemical Cross-linking for Modeling Protein Interactions" <em>PNAS</em></p> <p><strong>Parameter Files</strong></p> <p>These parameter files were used for running PIXL to produce the data described in the paper</p> <p><strong>Fasta Sequence Databases</strong></p> <p>These sequence databases were used for running PIXL to produce the data described in the paper</p> <p><strong>IMP</strong></p> <p>Code, intermediate data, and final localization densities for IMP proteasome modeling as described in the paper See IMP directory README</p>
Dateset of in silico investigations on protein_protein Interaction of GST isoforms with ASK1 and JNK1
<p>Please refer to the description of the files document for details on the files in the zip folder.</p> <p>The dataset contains molecular dynamics simulation files, and other in silico investigation output files used to describe the protein-protein interactions of seven GST isoforms with that of MAPK8 (JNK1) and MAP3K5 (ASK1). </p>
X-ray structure ensemble refinement of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group P21212)
<p>X-ray structure ensemble refinement of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group P21212). Raw diffraction images are available on Zenodo: 10.5281/zenodo.4086066. A single conformer model was deposited in the Protein Data Bank under accession code <a href="https://www.ebi.ac.uk/pdbe/entry/pdb/7AV8">7AV8</a>. Refinement was carried out with <a href="https://www.phenix-online.org/documentation/reference/ensemble_refinement.html">phenix.ensemble_refinement</a> and the repository contains all input and output files:</p> <p>Input:</p> <ul> <li>mx8421v63_xPHIPAx1521_free.mtz</li> <li>refine9.pdb</li> </ul> <p>Output:</p> <ul> <li>PHIPA-P21212_ensemble_refinement_ensemble.geo</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.pdb</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.mtz</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.log</li> </ul>
Host-pathogen protein interactions predicted using structure
<p>This dataset accompanies a manuscript describing a method to predict host-pathogen protein interactions using structure:</p> <p>Host-pathogen protein interactions predicted by comparative modeling.<br /> Davis FP, Barkan DT, Eswar N, McKerrow JH, Sali A. Protein Sci (2007) 16:2585-2596.<br /> http://www.proteinscience.org/cgi/doi/10.1110/ps.073228407</p> <p>The files contain predictions made for 10 human pathogens including species of Mycobacterium, Apicomplexa, and Kinetoplastida. The species.all.zip files contain all interactions predictions for each species along with the filter criteria that each interaction passed. The species.filter.zip files contains the same information, but only for the subset of interactions that passed the biological and network-level filters. These files can be viewed in any spreadsheet program, such as Excel.</p> <p> </p> <p>Predictions were made for interactions between human and </p> <ol> <li>Mycobacterium tuberculosis: mtuber</li> <li>Mycobacterium leprae: mleprae</li> <li>Leishmania major: lmajor</li> <li>Trypanosoma brucei: tbrucei</li> <li>Trypanosoma cruzi: tcruzi</li> <li>Cryptosporidium hominis: chominis</li> <li>Cryptosporidium parvum: cparvum</li> <li>Plasmodium falciparum: pfalciparum</li> <li>Plasmodium vivax: pvivax</li> <li>Toxoplasma gondii: tgondi</li> </ol>
Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells
<p>Single cell gene expression data for the paper "Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells" to appear in PLOS Pathogens.</p> <p>Package contains two single cell data sets, one consisting of single cells from PBMCs in three animals, and the other three tissues from one animal.</p> <p>The single-cells are grouped based on the SIV viral genes that they express.</p> <p> </p>
pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
<p>pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.</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>
HDAC6 protein-protein interaction network in CNS
<p>HDAC6 stands out as a distinctive member within the histone deacetylase family due to its predominant presence in the cytosol, facilitating its interaction with a wide array of non-histone proteins. Its dysregulation has been linked to various outcomes, encompassing diverse cancer types, immune-related disorders, and neurological conditions, including Alzheimer's, Parkinson's, ALS, Huntington's, Charcot-Marie-Tooth disease, and Rett syndrome.</p><p>The current network represents the known HDAC6 interactions in the central nervous system (CNS).</p><p>The latest version of the current network is available in WikiPathways under accession number WP5426.</p>
Serine-129 phosphorylation of a-synuclein is an activity-dependent trigger for physiologic protein-protein interactions and synaptic function
<p><strong>Phosphorylation of a-synuclein at the Serine-129 site (a-syn Ser129P) is an established pathologic hallmark of synucleinopathies and a therapeutic target. In physiologic states, only a small fraction of a-syn is phosphorylated at this site, and most studies have focused on the pathologic roles of this post-translational modification. We found that unlike wild-type (WT) a-syn which is widely expressed throughout the brain, the overall pattern of a-syn Ser129P is restricted, suggesting intrinsic regulation. Surprisingly, preventing Ser129P blocked activity-dependent synaptic attenuation by a-syn – thought to reflect its normal function. Exploring mechanisms, we found that neuronal activity augments Ser129P, which is a trigger for protein-protein interactions that are necessary for mediating a-syn function at the synapse. AlphaFold2-driven modeling and membrane-binding simulations suggest a scenario where Ser129P induces conformational changes that facilitate interactions with binding partners. Our experiments offer a new conceptual platform for investigating the role of Ser129 in synucleinopathies, with implications for drug-development. </strong></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>
Water will find its way: transport through narrow tunnels in hydrolases (water-protein interaction analysis)
<p>Repository including information on the analyses of water-protein interactions by MM/GBSA.</p> <p><strong>hal_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Hal.<br><strong>epx_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Epx.<br><strong>lip_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results<strong> </strong>for Lip<strong>.</strong></p> <p>In each archive structure is:<br> {supercluster_id}: - supercluster directory<br> {event_nr}_{rounded_radius}: - transport event directory<br> renum.pdb -pdb file containing protein, water of interest and 8 Angstrom radius sphere of surrounding waters<br> water.txt - file denoting water ID for water of interest in the renum.pdb file<br> binding_[implicit|explicit].dat - file containing MMGBSA results for full implicit and partial explicit system<br> decomposition_[implicit|explicit].dat - file containing MMGBSA decomposition results for full implicit and partial explicit system<br> info_{supercluster_id}.csv - file containing descriptions of transport events in supercluster (columns: event_name, radius, frame, simulation, water_id)</p> <p><strong>results.tar.gz </strong>- contains csv files generated by <em>script collect_data_minimal.py</em> for each protein for full implicit and partial explicit solvent. Columns in those csv file are: event_name, supercluster, radius, frame, simulation, water_id, delta_total, delta_van_der_waals, delta_electrostatic, delta_G_solv</p> <p><strong>scripts.tar.gz</strong> - contains following scripts<br> read_transport_events.py<br> prep_inputs.py<br> run_MMGBSA_single.sh<br> collect_data_minimal.py<br> plot_bin.py</p> <p>To recreate MMGBSA calculations with provided inputs one should:<br>$ python read_transport_events.py [Hal.dat|Epx.dat|Lip.dat] > selection.txt<br>Prepare transport events information in a compatible format. Binary transport events databases are available in the repository.</p> <p>$ python prep_inputs.py selection.txt - {absolute output path} {supercluster_id} 1 <br>Prepare parm and coordinate file for each supercluster id included in computation. Last argument 1 point script to resume preparations from renum.pdb files.</p> <p>$ bash run_MMGBSA_single.sh {residue_count}<br>Runs MMGBSA for both partial explicit and fully implicit systems. Needs to be run for each event in each supercluster.<br>residue_count should be set to 293 for hal, 319 for epx, and 534 for lip.</p> <p>$ python collect_data_minimal.py [hal|epx|lip]/{supercluster_id} [hal|epx|lip]/info_{supercluster_id}.csv [0|1] >> output.csv<br>Gathers data from MMGBSA calculations for a single supercluster into csv file. <br>Last argument should be 0 for fully implicit system, 1 for partial explicit system.</p> <p>$ python plot_bin.py {hal_implicit_csv} {hal_explicit_csv} {epx_implicit_csv} {epx_explicit_csv} {lip_implicit_csv} {lip_explicit_csv}<br>Results visualization. Produces "combined.png".</p>
Movies of Interactions of the parts of the MeCP2 protein (MBD and NTD domains) in solution and interacting with a surface
<p>Four movies illustrating the interactions of the methyl-CpG binding domain (MBD; 486 amino acids) and N-terminal (NTD; residues 1-77 of the full length MeCp2) domains of the Methyl CpG binding protein 2 (MeCP2; residues 78-162 of the full length MeCp2) with a solid surface. The MBD domain is the only region of the MeCP2 protein.</p> <p>These movies are supplementary material to the manuscript (submitted)<br> <br> <strong>Structure and Dynamics of the Rett Syndrome Protein, MeCP2 </strong><br> Noriyuki Kodera, Anna A. Kalashnikova, Mary E. Porter-Goff, Catherine A. Musselman, Cecilia<br> Chávez-García, Mikko Karttunen, Borries Demeler, Tatiana G. Kutateladze, Toshio Ando,<br> and Jeffrey C. Hansen</p> <p> </p> <ul> <li>MBD in water was simulated for 3 microseconds. All the rest of the systems were simulated for 1 microsecond. The movies show the last 20 nanoseconds of the full length simulations. The movies come in pairs with the same system in solution and when a surface is present (as listed below)</li> <li>Files: <ul> <li> <p>MBD+NTD2_last20ns.mpg: MBD and NTD domains in solution (reference simulation).</p> </li> <li> <p>MBD+NTD_last20ns.mpg: MBD and NTD domains in the presence of a surface.</p> </li> <li> <p>MBD+halfNTD2_last20ns.mpg: MBD domain and half of the NTD domain in solution (reference simulation).</p> </li> <li> <p>MBD+halfNTD2_last20ns.mpg: MBD domain and half of the NTD domain in the presence of a surface.</p> </li> </ul> </li> </ul>
Interactive study of human PTCD2 protein
<p>We investigated the intractome of human PTCD2 protein, a putative key player in non-canonical mitochondrial RNA processing, by Proximity-dependent Biotin Identification (BioID) followed by ESI-LC-MSMS. The dataset includes the results of the Mass spec analysis with detailed reports made by the proteomics core facility, University of Geneva. </p>
Predicting compound-protein interaction using hierarchical graph convolutional networks
<p>This repository contains the datasets which are used in the article "Predicting Compound-Protein Interaction using Hierarchical Graph Convolutional Networks".</p>
Scaling protein-water interactions in the Martini 3 coarse-grained force field to simulate transmembrane helix dimers in different lipid environments
<p>This dataset contains molecular dynamics (MD) trajectories used for preparation of the following manuscript: <br> "Scaling protein-water interactions in the Martini 3 coarse-grained force field to simulate transmembrane helix dimers in different lipid environments". </p>
HerpesFolds: All-versus-all protein interaction network
<p>For HSV-1, HCMV, and KSHV, all possible protein-protein combinations within the species were structurally predicted with AlphaFold 2.3. A user interface and network visualization can be found at: https://www.bosse-lab.org/herpesfolds/</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.
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DANDI Archive for NWB datasets
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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.