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641 results for “Protein interaction”
Data of Protein Adsorption Driven by a Combination of Protein-Protein and Protein-Surface Interactions
<p><span>It is a study of protein adsorption at solid-liquid interface by molecular dynamic simulations and experimental techniques. Data here reported regard results obtained.</span></p>
A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction - buil and Analysis
<p><strong>Title publication: </strong><em>A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction</em></p> <p><strong><strong>Year publication:</strong></strong><strong> </strong>2018</p> <p><strong>DOI publication: </strong>10.1021/acscentsci.7b00582</p> <p><strong>Description:</strong> All scripts, jupyter notebooks, and data files used to building and analyzing systems in the presented in the paper. Also related experimental data.</p>
Interaction of hypertensive rat serum immunoglobulin with recombinant human proteins
<p><strong>Microsoft Excel file: “HuProt supplemental data.xlsx”</strong></p> <p><em>Description of supplementary material : HuProt array v3.1 IgM and IgG signals from rat serum pools.</em></p> <p><strong>Sheet 1</strong>: “Array Z scores” Z scores for all array features for all sample pools used in the study detected with anti-IgM and anti-IgG reporter antibodies</p> <p><strong>Sheet 2</strong>: “IgM Z scores top 35” Z scores for the 35 array features that had highest scores detected with anti-IgM reporter in 28 week old salt loaded SHR-A3 and the corresponding scores for 16 week old SHR-A3 and 28 week old SHR-A3(<em>IgH</em>-B2) congenics.</p> <p><strong>Sheet 3</strong>: “IgM Z scores higher at 28 wks” Refinement of Sheet 2 to eliminate array features in which IgM Z scores were greater in 16 week old SHR-A3 prior to salt loading compared with 28 week old salt-loaded SHR-A3.</p> <p><strong>Sheet 4</strong>: “IgM Z scores congenics down” Refinement of Sheet 3 to include only array features in which IgM Z scores were reduced in 28 week old salt-loaded SHR-A3(<em>IgH</em>-B2) compared with 28 week old salt-loaded SHR-A3.</p> <p><strong>Sheet 5</strong>: “IgG Z scores top 35” Z scores for the 35 array features that had highest scores detected with anti-IgG reporter in 28 week old salt loaded SHR-A3 and the corresponding scores for 16 week old SHR-A3 and 28 week old SHR-A3(<em>IgH</em>-B2) congenics.</p> <p><strong>Sheet 6</strong>: “IgG Z scores higher at 28 wks” Refinement of Sheet 2 to eliminate array features in which IgG Z scores were greater in 16 week old SHR-A3 prior to salt loading compared with 28 week old salt-loaded SHR-A3.</p> <p><strong>Sheet 7</strong>: “IgG Z scores congenics down” Refinement of Sheet 3 to include only array features in which IgG Z scores were reduced in 28 week old salt-loaded SHR-A3(<em>IgH</em>-B2) compared with 28 week old salt-loaded SHR-A3.</p> <p><strong>Sheet 8: </strong>“Table 1 IgM IgG concordance” This data is presented as Table 1 in the manuscript and identifies those array features that are common to both Sheet 4 and Sheet 7, representing array features for which both IgM and IgG reactivity were reduced in 28 week old salt-loaded SHR-A3(<em>IgH</em>-B2) compared with 28 week old salt-loaded SHR-A3.</p>
Protein interaction networks are substantially rewired across evolution
<p>Complete data and source code deposit for Protein interaction networks are substantially rewired across evolution.</p>
ROS-Specific Huntingtin Interactions: Summary of PAR binding for different huntingtin protein preps
<p>PAR overlay assay with different preparations of huntingtin has yielded variable results. These results are summarized.</p>
Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"
<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p> PDB files of all structures used for the test calculations. <br> The PDB files correspond to the protonated structures obtained <br> as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p> Jupyter Notebook for generating all plots included in the manuscript, <br> including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p> - min_congrad.mdp: input file for partial optimization of protonated <br> protein--ligand complexes with Gromacs</p> <p> PyADF input scripts:</p> <p> - sp_single.pyadf: single-point calculations of separate protein and ligand<br> - sp_complex.pyadf: single-point calculation of protein-ligand complex<br> - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p> These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</p>
Data table 5 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)
<div>An Excel table contains a list of proteins identified by MS from pull-down experiment using cerebellar cortex lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 2_cortex isogenic mouse IgG control dynabeads.xlsx" </div> <div> </div>
Data table 4 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)
<div>An Excel table contains a list of proteins identified by MS from a pull-down experiment using cerebellum lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 3_cerebellum isogenic mouse IgG control dynabeads.xlsx<span><br></span></div>
Data table 2 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)
<p>An excel table containing a list of proteins identified by MS from samples after a pull-down experiment using cerebral cortex lysates with Dyna beads coated with control isogenic mouse IgG. The proteins from the list were considered false positive interactors of ataxin-3 in the cortex.</p>
Data table 1 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)
<p>An excel table contains LFQ intensity and other raw MS data for proteins identified in fractions 3,4,5 (i), 11, 12, 13, (ii) 18, 19, and 20 (iii) from ki150 and ki21 model brains. These fractions showed enrichment in ATXN3 protein. The fractions are visualized in Figure 4 (<a href="http://doi.org/10.3389/fnmol.2023.1122308" target="_blank" rel="noopener">doi.org/10.3389/fnmol.2023.1122308</a>)</p>
The PIWI-interacting protein Gtsf1 controls the selective degradation of small RNAs in Paramecium
<p><span>Ciliates undergo developmentally programmed genome elimination, in which small RNAs direct the removal of transposable elements during the development of the somatic nucleus. 25-nt scnRNAs are produced from the entire germline genome and transported to the maternal somatic nucleus, where selection of scnRNAs corresponding to germline-specific sequences is thought to take place. Selected scnRNAs then guide the elimination of transposable elements in the developing somatic nucleus. How germline-specific scnRNAs are selected remains to be determined. Here, we provide important mechanistic insights into the scnRNA selection pathway by identifying a <em>Paramecium</em> homolog of Gtsf1 as essential for the selective degradation of scnRNAs corresponding to retained somatic sequences. Consistently, we also show that Gtsf1 is localized in the maternal </span><span>somatic </span><span>nucleus where it associates with the scnRNA-binding protein Ptiwi09. Furthermore, we demonstrate that the scnRNA selection process is critical for genome elimination. We propose that Gtsf1 is required for the coordinated degradation of Ptiwi09-scnRNA complexes that pair with target RNA via the ubiquitin pathway, similarly to the mechanism suggested for microRNA target-directed degradation in metazoans.</span></p>
PhasAGE Training School 1 - Structure and protein interactions of repeated and low complexity regions - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Cytoscape files - Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation
<p>Cytoscape files of pathway enrichment analyses and PPI mapping associated with manuscript https://www.nature.com/articles/s41467-023-39241-7</p> <p><strong>Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics</strong> <strong>identify cancer-specific mechanisms of stress adaptation </strong></p> <p>Anna Rodina<sup>1,11</sup>, Chao Xu<sup>1,11</sup>, Chander S. Digwal<sup>1,11</sup>, Suhasini Joshi<sup>1,11</sup>, Anand R. Santhaseela<sup>1</sup>, Sadik Bay<sup>1</sup>, Swathi Merugu<sup>1</sup>, Aftab Alam<sup>1</sup>, Pengrong Yan<sup>1</sup>, Chenghua Yang<sup>1,12</sup>, Tanaya Roychowdhury<sup>1</sup>, Palak Panchal<sup>1</sup>, Liza Shrestha<sup>1</sup>, Yanlong Kang<sup>1</sup>, Sahil Sharma<sup>1</sup>, Yogita Patel<sup>2</sup>, Justina Almadovar<sup>1</sup>, Adriana Corben<sup>3,13</sup>, Mary Alpaugh<sup>1,14</sup>, Shanu Modi<sup>4</sup>, Monica L. Guzman<sup>5</sup>, Teng Fei<sup>6</sup>, Tony Taldone<sup>1</sup>, Stephen D. Ginsberg<sup>7,8</sup>, Hediye Erdjument-Bromage<sup>9</sup>, Thomas A. Neubert<sup>9</sup>, Katia Manova-Todorova<sup>10</sup>, Jason C. Young<sup>2</sup>,<strong> </strong>Meng-Fu Bryan Tsou<sup>10</sup><strong>, </strong>Tai Wang<sup>1,*</sup>, Gabriela Chiosis<sup>1,4,*</sup></p> <p><strong>Abstract </strong></p> <p>Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of first-in-class chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks. </p>
Predicting and modeling protein-protein interactions in E. coli envelopome
<p>Structural models and Supplementary data described in the reference:</p> <p>Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.</p> <p>Mu Gao, Davi Nakajima An, and Jeffrey Skolnick<em>. eLife</em>, 2022. <strong>11</strong>: p. e82885.</p> <p>List of files:</p> <ul> <li>af2c_fea_220331.tar -- A tarball of input features of full E coli proteome to AF2Complex version 1.3.0. Feature files are pickled and gzipped, which AF2Complex v1.3 takes as input directly.</li> </ul> <p>Results of an application to E coli envelopome on four query proteins from the outer membrane biogenesis pathway.</p> <ul> <li>Supplementary Data.xlsx -- Virtual PPI screening results of PpiD, YfgM, SurA, and BamA.</li> <li>screening_top1_models.zip -- Compressed top 1 dimeric models of top hits from the PPI screening. Note that these models are unrelaxed.</li> <li>predicted structural models.zip -- Compressed structural models of supercomplexes formed in the OMP biogenesis pathway described in the reference.</li> </ul>
Interactive computational and experimental approaches improve the sensitivity of periplasmic binding protein-based nicotine biosensors for measurements in biofluids
<p>Here are the raw simulation trajectories (water removed).</p>
Temporal iCLIP captures co-transcriptional RNA-protein interactions
<p>Minimum datasets required to perform analysis using code from github. </p>
Surfactant Proteins SP-B and SP-C in Pulmonary Surfactant Monolayers: Physical Properties Controlled by Specific Protein–Lipid Interactions - Simulation dataset
<p>Simulation data related to the article:</p> <p>Liekkinen, J., Olzynska, A., Cwiklik, L., Bernardino de la Serna, J., Vattulainen, I., & Javanainen, M. Surfactant Proteins SP-B and SP-C in Pulmonary Surfactant Monolayers: Physical Properties Controlled by Specific Protein–Lipid Interactions. <em>bioRxiv</em>, pp.2022-12. (Pre-print available at https://doi.org/10.1101/2022.12.12.520108)</p> <p> </p> <p>DPPC/POPC/POPG/CHL1 (60/20/10/10 mol-%) monolayers simulated in non-equilibrium conditions. The monolayer is compressed or expanded at a constant rate at different temperatures. Surfactant proteins SP-B and SP-C are included in the monolayers for comparison. The Charmm36 lipid model [1] is used together with the 4-point OPC water model [2]. Each system consist of two monolayers with 169 lipids each. The monolayers are separated by a water slab, and surrounded by vacuum.</p> <p>All trajectories are simulated for 5000 ns with Gromacs 5.1.x [3] using the default Charmm36 monolayer simulations parameters given in the mdp file. Topology (.top) and index (.ndx) files are also included. The topologies (.itp) for the lipids can be obtained from Charmm-GUI and for the OPC water model from https://bioinformatics.cs.vt.edu/~izadi/</p> <p>[1] DOI: 10.1021/jp101759q</p> <p>[2] DOI: 10.1021/jz501780a</p> <p>[3] DOI: 10.1016/j.softx.2015.06.001</p>
Molecular dynamics trajectories of MTDH-SND1 protein-protein interaction inhibitors
<p>This includes the prmtop and MD trajectories (production stage) and Amber outfiles of L1-L12, C26-A6/2, and MTDH peptide, GaMD trajectories of the protein, and the umbrella sampling trajectories of L5 and C26-A6/2. To reduce the file size, the trajectory files had been compressed using “xz” algorithm, and frames were saved by one frame per 1ns. However, for reference, one additional larger traj file with higher saving frequency for L5 was also uploaded. The input files and input coordinate files were uploaded as a separate attachment at http://pubs.acs.org.</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>
NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions
<p>Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a synthetic platform for protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes<span class="Apple-converted-space"> </span>drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pre-trained protein language models to expand, <em>in silico</em>, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of generating protein complexes with diverse molecular recognition properties as tools for biotechnology and synthetic biology.</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.