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Dataset results
219 results for “protein dynamics”
Movie of the molecular dynamics simulation of a crucial step in the activation of the STING protein
<p>STING (stimulator of interferon genes) is a homodimer protein involved in regulation of the innate immune system and plays a role in antitumor immunity by inducing the production of cytokines. Activation of STING stems from binding of endogenous cyclic dinucleotides (CDNs), which induce a conformational change to initiate signaling. The MP4 movie shows a molecular dynamics simulation of a crucial step of the activation process.</p>
Movie of the molecular dynamics simulation of the opening and closing of the Cullin-RING protein complex
<p>Heterobifunctional degraders, which consist of two separate protein binding moieties (the warhead and the E3-ligand) joined by a linker, are a class of molecules that "induce proximity" between a target protein of interest (POI) and a E3 ubiquitin ligase. This induced proximity can lead to ubiquitination of the POI and its subsequent proteosomal degradation through a complex machinery of proteins. TPD present a novel approach to drug protein targets, since a single degrader molecule can induce catalytic degradation of the POI and potentially offer an avenue to eliminate targets traditionally labeled as undruggable by classical therapeutic strategies. Here we probe the conformational landscape of the full CRL macromolecular assembly and present a MP4 trajectory movie showing the molecular dynamics simulation of the large-scale conformational change during the opening and closing of the Cullin-RING protein complex.</p>
Molecular dynamics-generated ensemble dataset of ubiquitin; for "PROTHON: A Local Order Parameter-Based Method for Efficient Comparison of Protein Ensembles"
<p>The molecular dynamics-generated ensemble dataset (229Mb zip file) for ubiquitin, used in the manuscript "PROTHON: A Local Order Parameter-Based Method for Efficient Comparison of Protein Ensembles", submitted to the Journal of Chemical Information and Modeling (JCIM). The dataset consists of 6 .dcd files, and one .pdb file. </p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Flavivirus Helicases -- Output Files
<p>These are the output files generated using the input files and Galaxy workflows for flavivirus helicase simulations, from: </p> <pre>https://doi.org/10.5281/zenodo.7493015</pre>
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>
Source Data for the publication: "How Orange Carotenoid Protein controls the excited state dynamics of Canthaxanthin"
<p>This dataset contains the raw data for the results shown in the paper, in plain text format. The first line of each file describes the content of the columns.</p> <p>Contents:</p> <ul> <li>ocp_adiabatic_energies.txt - Energies of the adiabatic states (A.U.) for all SH trajectories of CAN in OCP</li> <li>ocp_bla.txt - Values of the BLA coordinate (Angstrom) for all SH trajectories of CAN in OCP</li> <li>ocp_dihedrals.txt - Values of the CAN dihedrals ) degrees for all SH trajectories of CAN in OCP</li> <li>ocp_populations.txt - Calculated adiabatic populations of CAN in OCP</li> <li>vac_adiabatic_energies.txt - Energies of the adiabatic states (A.U.) for all SH trajectories of CAN in gas phase</li> <li>vac_bla.txt - Values of the BLA coordinate (Angstrom) for all SH trajectories of CAN in gas phase</li> <li>vac_dihedrals.txt - Values of the CAN dihedrals ) degrees for all SH trajectories of CAN in gas phase</li> <li>vac_populations.txt - Calculated adiabatic populations of CAN in gas phase</li> </ul> <p><br> </p> <p> </p> <p> </p>
Data from: Comparative genomics reveals the dynamic evolutionary history of cement protein genes of barnacles from intertidal to deep-sea hydrothermal vents
<p><span>Thoracican barnacles are a diverse group of marine organisms for which the availability of genome assemblies is currently limited. In this study, we sequenced the genomes of two neolepadoid species </span><span>(<em>Ashinkailepas kermadecensis</em>,<em> Imbricaverruca yamaguchii</em>) </span><span>from hydrothermal vents, in addition to two intertidal species. Genome sizes ranged from 481.5 to 1054.6 Mb, with repetitive sequence contents of 21.2 to 50.7%. Concordance rates of orthologs and heterozygosity ratios were between 82.4 and 91.7% and between 1.1 and 2.6, respectively, indicating high genetic diversity and heterozygosity. Based on phylogenomic analyses, we revised the nomenclature of cement genes encoding cement proteins that are not homologous to any known proteins. The major cement gene, <em>CP100A</em>, was found in all thoracican species, including vent-associated neolepadoids, and was hypothesized to be essential for thoracican settlement. Duplicated genes, <em>CP100B</em> and <em>CP100C</em>, were found only in balanids, suggesting potential functional redundancy or acquisition of new functions associated with the calcareous base. An ancestor of <em>CP52 </em>genes was duplicated dynamically among lepadids, pollicipedids with multiple copies on a single scaffold, and balanids with multiple sequential repeats of the conserved regions, but no <em>CP52</em> genes were found in neolepadoids, providing insights into cement gene evolution among thoracican lineages. This study enhances our understanding of the adhesion mechanisms of thoracicans in underwater environments. The newly sequenced genomes provide opportunities for studying their evolution and ecology, shedding light on their adaptation to diverse marine environments, and contributing to our knowledge of barnacle biology with valuable genomic resources for further studies in this field.</span></p>
Molecular Dynamics Analysis of Fibroblast Activation Protein (FAP) and Dipeptidyl Peptidase IV (DPP-IV) Complexes with Known Inhibitors
<p>MD Input and Trajectories associated with the Manuscript entitled “Solving the Fibroblast Activation Protein (FAP) Conundrum: An in Depth In Silico Analysis Suggests Key Roles for Ala657 and Tyr541 in Endopeptidase Activity and Ligand Selectivity” </p>
Glassy dynamics and memory effects in an intrinsically disordered protein construct
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MD simulations data for: Role of the αC-β4 loop in protein kinase structure and dynamics
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Data from: Comparative genomics reveals the dynamic evolutionary history of cement protein genes of barnacles from intertidal to deep-sea hydrothermal vents
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Deep learning guided design of dynamic proteins
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Molecular dynamics trajectory videos for high-affinity short peptides targeting BamA protein of Candidatus liberibacter asiaticus
<p>Molecular dynamics trajectory videos and trajectory analysis reports for high-affinity short peptides targeting BamA protein of <em>Candidatus</em> liberibacter asiaticus</p>
The dynamics of protein-RNA interfaces using all-atom molecular dynamics simulations
<p>We investigated to characterize the dynamics of protein-RNA complexes and their interfaces at molecular level by performing a more systematic analysis. To get insights on the dynamics of protein-RNA complexes, all-atom MD simulations were generated for the manuscript "The dynamics of protein-RNA interfaces using all-atom molecular dynamics simulations". Nine protein-RNA complexes are studied in this work: 1ASY (an aspartyl-tRNA synthase/tRNA), 1JBS (a ribotoxin restrictocin/SRD RNA), 1MMS (a ribosomal protein L11/23S), 1OOA (a nuclear factor NF-kappaB p105 subunit/RNA aptamer), 1RKJ (a nucleolin/pre-rRNA), 2R8S (a FAB/P4-P6 RNA ribozyme domain), 2VPL (a 50S ribosomal protein/mRNA), 2ZM5 (a tRNA delta(2)-isopentenylpyrophosphate transferase/tRNA), 3IEV (a GTP-binding protein era/3' end of 16S rRNA). </p><p>Each folder for a complex is organised as followed:</p><ul><li>in <strong>complex</strong> there are the dry MD simulations for the complex protein-RNA with the starting structure</li><li>in <strong>protein</strong> there are the dry MD simulations for the unbound protein with the starting structure</li><li>in <strong>rna</strong> there are the dry MD simulations for the unbound RNA with the starting structure</li></ul><p>In each folder, all the trajectory files are named : <strong>md_(times of simulations).xtc</strong> and the starting structure called : <strong>start.gro</strong>.</p>
Single-Molecule FRET-Resolved Protein Dynamics - from Plasmid to Data in Six Steps
<p>SmFRET trace files of 58 yeast Hsp90 molecules (in presence of AMP-PNP) containing raw intensity values through time of acceptor emission after acceptor excitation (r_r_*), donor emission after donor excitation (o_g_*), and acceptor emission after donor excitation (r_g_*).</p>
Molecular Dynamics Simulation Dataset for "Hydrophobic Mismatch Drives Self-Organization of Designer Proteins into Synthetic Membranes"
<p>This repository contains molecular dynamics (MD) simulation data from the study on the self-organization of designer proteins in synthetic membranes. The data includes simulations for different single lipid compositions (DOPC, DPPC, DYPC) denoted as [lipid]-PL* where PL stands for the different TMD constructs. Multi component simulation are named accordingly. The repository provides initial (eqi.gro) and final (prod.gro) coordinates for each simulation. The 'cmd' file in each directory outlines the assembly process of each simulation, and the 'mdp' folder contains all input files for the simulations. </p>
Data from: Molecular docking and dynamics studies to identify novel active compounds targeting potential breast cancer receptor proteins from an indigenous herb Euphorbia thymifolia Linn
<p>Breast cancer has become most prevalent disease and their incidence has doubled in Indian scenario. Targeted therapy with the novel compounds derived from plants could be the promising approach for the development of drugs. <em>Euphorbia thymifolia</em> L is a widely growing tropical herb which has been reported for its various ethnopharmacological properties, including anticancer properties. The aim of the present study was to identify the active phytocompounds present in the methanolic extract using an <em>I</em><em>n-silico</em> approach. The methanolic extract of <em>E. thymifolia</em> (ME.ET) was subjected to GC-MS analysis and the identified compounds were docked with potential protein targets implicated in breast cancer such as ERK1, AKT, EGFR/HER2, ER, MELK, PLK1, PTK6. Compounds with good docking score were further subjected to dynamics study to understand the protein ligand binding stability, ligand pathway calculation, molecular mechanics energies combined with Poisson-Boltzmann (MM/PBSA) calculation using Schrodinger suite. Out of 219 unique phytocompounds subjected to docking, two compounds namely, 3,6,9,12-tetraoxatetradecane-1,14-diyl dibenzoate (TTDB) and succinic acid, 2-(dimethylamino)ethyl 4-isopropylphenyl ester (SADPE) showed good docking score. Molecular dynamics study showed high affinity and low binding energy for TTDB with HER2, ERK1 and SADPE with ER. Hence this is the first study to identify and report active compounds from <em>E.thymifolia</em> linn. Further <em>invitro</em> and <em>invivo</em> anticancer studies can be performed to confirm these results and understand the molecular mechanism by which TTDB and SADPE exhibit anticancer activity against breast cancer.</p>
Comparative Pore Structure and Dynamics for Bacterial Microcompartment Shell Protein Assemblies in Sheets or Shells
<blockquote> <p>Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis. In order to make this fit into the 50GB Zenodo limit, it was constructed with the following tar commands:<br><br>For <a href="../api/records/10800570/draft/files/bmc_shell_vs_sheet.tar.gz/content" target="_blank" rel="noopener noreferrer">bmc_shell_vs_sheet.tar.gz:</a><br><code>tar -zcvf bmc_shell_vs_sheet.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*log" --exclude="*xsc" --exclude="*coor" --exclude="*vel" --exclude="*[0-9].out" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*ppm" --exclude="*png" --exclude="*pdf" --exclude="*catchy*" --exclude="*svg" --exclude="*restart*" --exclude="*history" --exclude="core.*" --exclude="FFTW_NAMD*" --exclude="*avi" --exclude="*mp4" --exclude="*pcad*" --exclude="*pcaa*" --exclude="*ammn*" --exclude="*amnp*" --exclude="*amsa*" --exclude="*dtnt*" --exclude="*mthl*" --exclude="*ntrt*" --exclude="*.inp" --exclude="*.sph" --exclude="structures" --exclude="test-sim" --exclude="test-sim-flatten-constantarea" --exclude="*.dx"</code><br><br>For <a href="../api/records/10800570/draft/files/bmcflattenmdff.tar.gz/content" target="_blank" rel="noopener noreferrer">bmcflattenmdff.tar.gz</a>:<br><code>tar --exclude="emdensitymap" --exclude="emstructures" --exclude="forSaad" --exclude="*dcd" --exclude="*ppm" --exclude="*mp4" --exclude="*png" --exclude="*old" --exclude="*BAK" --exclude="*txt" --exclude="animations" --exclude="apbs*" -zcvf bmcflattenmdff.tar.gz</code><br><br>The bmc_shell_vs_sheet has the folder test-sim-curved for shell simulation while test-sim-flatten-noconstantratio for sheet simulation.<br>Pore analysis for shell can be found in hole2 and for sheet in hole-flatten<br>Analysis has been done with various *py *sh and *tcl files<br>For visualizing trajectories in VMD "<strong>load*.tcl</strong>" scripts were used.<br><br>The bmcflattenmdff contains all the scripts needed for converting the shell topology to sheet.</p> </blockquote>
Data for 'Ranking Single Fluorescent Protein Based Calcium Biosensor Performance by Molecular Dynamics Simulations'
<h2>Melike Berksoz, Canan Atilgan* </h2> <h3>Faculty of Engineering and Natural Sciences, Sabanci University </h3> <p><strong>*Correspondance:</strong> Canan Atilgan, Faculty of Natural Sciences and Engineering, Sabancı University, Tuzla 34956 Istanbul, Türkiye, E-mail: canan@sabanciuniv.edu</p> <p>Genetically Encoded Fluorescent Biosensors (GEFBs) have become indispensable tools for visualizing biological processes <em>in</em> <em>vivo.</em> A typical GEFB is composed of a sensory domain (SD) which undergoes a conformational change upon ligand binding and a genetically fused fluorescent protein (FP). Ligand binding in the SD allosterically modulates the chromophore environment and changes its spectral properties. Single fluorescent (FP)-based biosensors, a subclass of GEFBs, offer a simple experimental setup; they are easy to produce in living cells, structurally stable and simple due to their single-wavelength operation. However, they pose a significant challenge for structure optimization, especially concerning the length and residue content of linkers between the FP and SD which effect how well the chromophore responds to conformational change in the SD. In this work, we use classical all-atom molecular dynamics simulations to analyze the dynamic properties of a series of calmodulin-based calcium biosensors, all with different FP-SD interaction interfaces and varying degrees of calcium binding dependent fluorescence change. Our results indicate that biosensor performance can be predicted based on distribution of water molecules around the chromophore and shifts in hydrogen bond occupancies between the ligand-bound and ligand-free sensor structures.</p> <p>Hydrogen bond occupancies were calculated with merging_bonds.py script. Double counted hydrogen bonds where a residue acts both as acceptor and donor are merged into a single entry with merge_files.py. To run sasa.tcl, you need VMD software. Trajectories were created with NAMD2 with a dcdfrequency of 5000 timesteps (every 10 ps) and strided in a 1:100 ratio (every 1 ns=1 frame in dcd). </p>
Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding
<p>This dataset contains files of the molecular dynamics simulations performed in "Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding" study. Further information in presented in README.md file and the abstract of the study is presented below:</p> <p>"The nucleocapsid (N) protein of the SARS-CoV-2 virus, the causal agent of COVID-19, is a multifunction phosphoprotein that plays critical roles in the virus life cycle, including transcription and packaging of the viral RNA. To play such diverse roles, the N protein has two globular RNA-binding modules, the N- (NTD) and C-terminal (CTD) domains, which are connected by an intrinsically disordered region. Despite the wealth of structural data available for the isolated NTD and CTD, how these domains are arranged in the full-length protein and how the oligomerization of N influences its RNA-binding activity remains largely unclear. Herein, using experimental data from electron microscopy and biochemical/biophysical techniques combined with molecular modeling and molecular dynamics simulations, we showed that, in the absence of RNA, the N protein formed structurally dynamic dimers, with the NTD and CTD arranged in extended conformations. However, in the presence of RNA, the N protein assumed a more compact conformation where the NTD and CTD are packed together. We also provided an octameric model for the full-length N bound to RNA that was consistent with electron microscopy images of the N protein in the presence of RNA. Together, our results shed new light on the dynamics and higher-order oligomeric structure of this versatile protein."</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.