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347 results for “structural proteins”
Structure analysis of a p53 fusion protein
<p><span><span><span><span><span><span><span><span><span><span><span>The tumor suppressor p53 is a key target for cancer therapy, but its low expression levels, poor conformational stability, and high degree of disorder remain major challenges to its structural investigation. Here, we address these issues by fusing the N-terminal transactivation domain of p53 to an engineered spider silk domain termed NT*. Molecular dynamics simulations show that the disordered transactivation domain of p53 wraps around the NT* domain via a series of folding events, resulting in a globular structure.</span></span></span></span></span></span></span></span></span></span></span></p>
Static Disorder in Excitation Energies of the Fenna-Matthews-Olson Protein: Structure-Based Theory Meets Experiment
<p>The files contain atomic coordinates of 5200 different conformations of the FMO protein in protein data bank (PDB) format. Each of the 16 zip file contains one directory with 325 PDB files, where each file contains the coordinates of one conformation of the FMO protein. These data were generated with the FIRST/FRODA method as described in Fokas, A.S., Cole, D. and Chin, A. W. Photosynth. Res. 2014, 122, 275-292. The data were used by the present authors to explain the inhomogeneous optical properties of the FMO protein.</p>
Birth of new protein folds and functions in the virome - Structure Database
<p>This is the database of protein structures described in the manuscript "Birth of new protein folds and functions in the virome", by authors Jason Nomburg, Nate Price, and Jennifer A. Doudna.</p>
DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.
<p>test and training data.</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>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Data from: The evolution of protein-coding gene structure in eukaryotes
<p>Introns are highly prevalent in most eukaryotic genomes. Despite the accumulating evidence for benefits conferred by the possession of introns, their specific roles and functions, as well as the processes shaping their evolution, are still only partially understood. Here we explore the evolution of the eukaryotic gene intron-exon structure by focusing on several key features such as the intron length, the number of introns, and the intron-to-exon ratio of protein-coding genes. We utilize whole genome data from 590 species covering the main eukaryotic taxonomic groups and analyze them within a statistical phylogenetic framework. We found that the basic gene structure differs markedly among the main eukaryotic phyla, with animals, and particularly chordates, displaying intron-rich genes, compared to plants and fungi. Reconstruction of gene structure evolution suggests that these differences had evolved prior to the divergence of the phyla, and have remained mostly conserved within groups. We revisit the previously reported association between the genome size and the mean intron length, and report that the correlation patterns differ considerably among phyla. Our findings suggest that the evolution of introns may be affected by different processes across the eukaryotic tree. The substantial diversity in gene structures may indicate that introns play different molecular and evolutionary roles in different organisms.</p>
predicted_protein_complex_structures_datasets
Open the record for dataset details and reuse information.
Sampled Structures from Diffusion Models for Protein SHAPES
<p>Data associated with SHAPES (Structural and Hierarchical Assessment of Proteins with Embedding Similarity)</p> <ul> <li><code>rmsd.tar.gz</code> : RMSD and TM scores of ESMFold predicted structures with original designed backbones.</li> <li><code>mpnn.tar.gz</code> : ProteinMPNN designed sequences.</li> <li><code>raster.tar.gz</code> : CATH and sampled structures annotated with the grid square label after rasterization. Also includes the specific structures which are displayed in the raster plots.</li> <li><code>dssp.tar.gz</code> : Secondary structure frequences computed by DSSP.</li> <li><code>samples.tar.gz</code> : All sampled structures.</li> </ul> <p>Due to size, the ESMFold predicted structures for eight ProteinMPNN-designed sequences per sampled backbone are not included.</p> <p> </p> <p>Code: <code>proteinshapes-main.zip</code></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>
Deep-time structural evolution of retroviral and filoviral surface envelope proteins
<p>The retroviral surface envelope protein subunit (SU) mediates receptor binding and triggers membrane fusion by the transmembrane (TM) subunit. SU evolves rapidly under strong selective conditions, resulting in seemingly unrelated SU structures in highly divergent retroviruses. Structural modeling of the SUs of several retroviruses and related endogenous retroviral elements with AlphaFold 2 identifies a TM-proximal SU β-sandwich structure that has been conserved in the orthoretroviruses for at least 110 million years. The SU of orthoretroviruses diversified by the differential expansion of the β-sandwich core to form domains involved in virus-host interactions. The β-sandwich domain is also conserved in the SU equivalent GP1 of Ebola virus although with a significantly different orientation in the trimeric envelope protein structure relative to the β-sandwich of human immunodeficiency virus type 1 gp120, with significant evidence for divergent rather than convergent evolution. The unified structural view of orthoretroviral SU and filoviral GP1 identifies an ancient, structurally conserved, and evolvable domain underlying the structural diversity of orthoretroviral SU and filoviral GP1.</p>
A hybrid structure determination approach to investigate the druggability of the nucleocapsid protein of SARS-CoV-2
<p><span>The ongoing pandemic caused by SARS-CoV-2 has called for concerted efforts to generate new insights into the biology of betacoronaviruses to inform drug screening and development. Here, we establish a workflow to determine the RNA recognition and druggability of the nucleocapsid N-protein of SARS-CoV-2, a highly abundant protein crucial for the viral life cycle. We use a synergistic method that combines NMR spectroscopy and protein-RNA cross-linking coupled to mass spectrometry to quickly determine the RNA binding of two RNA recognition domains of the N-protein. Finally, we explore the druggability of these domains by performing an NMR fragment screening. This workflow identified small molecule chemotypes that bind to RNA binding interfaces and that have promising properties for further drug development.</span></p> <p><span>This deposition contains the NMR data acquired to determine the structural features of RBDs- RNA recognition as well as selected relevant data regarding the characterization of promising molecular fragments to disrupt protein-RNA interaction.</span></p> <p><span>Furthermore, we included the molecular docking files used to obtain the reported structural model.</span></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>
Structure-guided discovery of potent antifungals that prevent Ras signaling by inhibiting protein farnesyltransferase
<p>Infections by fungal pathogens are difficult to treat due to a paucity of antifungals and emerging resistances. Next-generation antifungals therefore are needed urgently. We have developed compounds that prevent farnesylation of <em>Cryptoccoccus</em> <em>neoformans</em> Ras protein by inhibiting protein farnesyltransferase with 3–4 nanomolar affinities. Farnesylation directs Ras to the cell membrane and is required for infectivity of this lethal pathogenic fungus. Our high-affinity compounds inhibit fungal growth with 3–6 micromolar minimum inhibitory concentrations, 4- to 8-fold better than Fluconazole, an antifungal commonly used in the clinic. Compounds bound with distinct inhibition mechanisms at two alternative, partially overlapping binding sites, accessed via different inhibitor conformations. We showed that antifungal potency depends critically on the selected inhibition mechanism, because this determines the efficacy of an inhibitor at low <em>in</em> <em>vivo</em> levels of enzyme and farnesyl substrate. We elucidated how chemical modifications of the antifungals encode the desired inhibitor conformation and concomitant inhibitory mechanism.</p>
AlphaFold2 predicted structures of ThsA and ThsB proteins
<p>This Zenodo record contains the AlphaFold2 models described in the manuscript: Structural characterization of macro domain-containing Thoeris antiphage defense systems</p>
The conformational plasticity of structurally unrelated lipid transport proteins correlates with their mode of action
<p>Supporting DATA files for "Conformational plasticity of structurally unrelated lipid transport proteins correlates with their mode of action".</p> <ol> <li><strong>zenodo_simulation_starting_files_and_result_pdbs.zip</strong> contains input files for atomistic and coarse-grained MD simulations, membrane binding interface results from coarse-grained simulations, and apo-like and holo-like conformations of lipid transfer domains arising from clustering of atomistic simulations</li> <li><strong>zenodo_data_and_codes.zip </strong>contains the raw data and/or scripts used for the analysis of the figures of the paper as well as a README file explaining the contents. The figure caption in the manuscript indicates the folder name that contains data relevant to that particular figure. <p>The trajectories have not been uploaded here, in case you are interested, please ask the corresponding author (stefano.vanni@unifr.ch).</p> </li> </ol> <p> </p>
Synchrotron diffraction images for the 0.86-Å crystal structure of hydrogenated human myelin protein P2
<p>1000 X-ray diffraction images collected from a crystal of hydrogenated human myelin protein P2. Key processing files for the XDS package are included. The data were collected on the EMBL/DESY synchrotron beamline P13. </p>
Fueling ab initio folding with oceanic metagenomics enables structure and function predictions of new protein families
<p>Code and protein sequence database to construct multiple sequence alignment from Tara Ocean data.</p>
Supplemental Material for 'Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space' and DeepUrfold
<p>Data provided for the paper Draizen, EJ, Veretnik, S, Mura, C, and Bourne, PE. "Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space." <em>Nature Communications</em>, Aug. 2024.</p> <div> </div> <p> </p>
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.