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715 results for “folding”
Folding images
Folding images, around 1900, chromolithography. Musée d'Art et d'Histoire (Musée du Cinquantenaire, Brussels, Belgium). Made with CapturingReality. For more updates, please consider to follow me on Twitter at @GeoffreyMarchal. Source: Objaverse 1.0 / Sketchfab
FIGURE 6 in A new Cinygmula McDunnough, 1933 species with distinct imaginal frontal fold from eastern Chinese Himalaya (Ephemeroptera: Heptageniidae)
FIGURE 6. Male genitalia of Cinygmula longissima sp. nov. A: genitalia (dorsal view); B: genitalia (ventral view); C: penes (dorsal view); D: penes (ventral view); E: titillators of penes; F: lateral spine
FIGURE 4 in A new Cinygmula McDunnough, 1933 species with distinct imaginal frontal fold from eastern Chinese Himalaya (Ephemeroptera: Heptageniidae)
FIGURE 4. Male imago of Cinygmula longissima sp. nov. A: habitus; B: legs (fore–, mid– and hindlegs from left to right and foreclaws enlarged); C: head (dorsal view); D: head (lateral view)
FIGURE 3 in A new Cinygmula McDunnough, 1933 species with distinct imaginal frontal fold from eastern Chinese Himalaya (Ephemeroptera: Heptageniidae)
FIGURE 3. Nymphal mouthparts of Cinygmula longissima sp. nov. A: labrum (dorsal view); B: left mandible (dorsal view); C: right mandible (dorsal view); D. hypopharynx (dorsal view); E: left maxilla (ventral view); F: labium (ventral view)
FIGURE 2 in A new Cinygmula McDunnough, 1933 species with distinct imaginal frontal fold from eastern Chinese Himalaya (Ephemeroptera: Heptageniidae)
FIGURE 2. Nymphal structures of Cinygmula longissima sp. nov. A: head of a mature nymph (showing the frontal fold in the shell, dorsal view); B: head capsule (showing the shape, dorsal view); C: fore–, mid– and hindleg (from above to bottom); D. Gills I–VII (from left to right)
Validation of de novo designed water-soluble and transmembrane proteins by in silico folding and melting
<p>Here are all of the datasets generated and analysed during this study. </p> <p>Here is a breakdown of their content:</p> <ul> <li><strong>8_stranded_transmembrane_barrels.zip</strong> - raw data from Alphafold (3 and 48 recycles), ESMFold and raptor predictions of the 8 stranded TMBs. A file with all the sequences is also given</li> <li><strong>12_stranded_transmembrane_barrels.zip - </strong>raw data from the Alphafold and ESMfold predictions of the 12 stranded TMBs. A file with all the sequences is also given</li> <li><strong>water_soluble_barrels.zip</strong> - raw data from the Alphafold and ESMfold predictions of the water soluble beta barrels (designable and non-designable). A file with all the sequences is also given</li> <li><strong>all design models.zip</strong> - original design models for water-soluble (designable and non-designable), 8-stranded and 12-stranded TMBs</li> </ul> <p> </p> <ul> <li><strong>ESMfold_masking_exp.tar - </strong>this tar file contains all the ESMfold masking experiments performed to the water-soluble, 8 and 12-stranded transmembrane barrels. Inside there are zipped datasets for each masking experiment<br> </li> <li> <p><strong>ziped_raw_csv_files.zip - </strong>raw csv files with all the data necessary to analyse the figures </p> </li> <li> <p><strong>analysis_notebooks.zip </strong>- Jupyter notebooks used to analyse the output prediction data for all figures</p> </li> </ul> <p> </p> <p> </p>
MD simulations associated to the paper "Unveiling An Unexpected Redox Regulation of the Folding, Function and Inhibition in the PTB Domain of FRS2"
<p>This dataset contains the molecular dynamics input files and trajectories performed for the paper "Unveiling An Unexpected Redox Regulation of the Folding, Function and Inhibition in the PTB Domain of FRS2"<br><br>The data are organized in three folders:</p> <ol> <li>`input_files`, which contains the gromacs .mdp files used for all the minimization, equilibration, and run phases.</li> <li>`topologies`, which contains a gromacs topology file (.top) and initial configuration (.gro) for both the oxidized and reduced form of PTB.</li> <li>`MD`, which contains the three 1-µs long trajectories in gromacs compressed format (.xtc) and a reference run file (.tpr) for both the oxidized and reduced form of PTB.</li> </ol>
Unraveling the Unfolding Mechanism of Pseudoazurin: Insights into Stabilizing Cupredoxin Fold as a Common Domain of Cu-Containing Proteins
<p>This dataset includes molecular dynamics (MD) simulation trajectories and experimental data used in the title named study. The MD trajectories cover simulations of pseudoazurin under various conditions: apo (pH 2, pH 3, pH 7), holo (pH 2, pH 3, pH 7), and explicit water simulations of holo at pH 7. Additionally, the dataset contains raw experimental data, including small-angle neutron scattering (SANS) curves, visible (Vis) absorption spectra, and circular dichroism (CD) spectra. This comprehensive dataset supports the investigation of unfolding mechanism of Pseudoazurin.</p>
GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank (5-fold cross-validation)
<p>This dataset contains GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank.</p> <p>The dataset is designed to enable systematic PRS analyses with 5-fold cross validation. For each phenotype and fold, we provide GWAS summary statistics for the training, validation, and test sets. The validation summary statistics can be used for model selection/tuning. The test summary statistics can be used to evaluate PRS models via pseudo-validation metrics. Association testing for all phenotypes and samples was done with <strong>plink2</strong>.</p> <p> </p> <p>The <strong>phenotypes</strong> included in this dataset are:</p> <ul> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=50">HEIGHT</a>: Standing height (Data-Field: 50)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=21001">BMI</a>: Body mass index (Data-Field: 21001)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=48">WC</a>: Waist circumference (Data-Field: 48)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=49">HC</a>: Hip circumference (Data-Field: 49)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=20022">BW</a>: Birth weight (Data-Field: 20022)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=3062">FVC</a>: Forced vital capacity (Data-Field: 3062)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=3063">FEV1</a>: Forced expiratory volume in 1-second (Data-Field: 3063)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30760">HDL</a>: HDL cholesterol (Data-Field: 30760)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30780">LDL</a>: LDL cholesterol (Data-Field: 30780)</li> </ul> <p> </p> <p>To allow users to assess PRS performance as a function of sample size, we also provide <strong>subsampled training GWAS summary statistics</strong>. This is done by taking the training samples and randomly selecting (without replacement) a subset of them for conducting association testing. The training sample sizes are:</p> <ul> <li>N = 5000</li> <li>N = 10000</li> <li>N = 20000</li> <li>N = 40000</li> <li>N = 80000</li> <li>N = 160000</li> <li>Full training set (sample size varies by phenotype).</li> </ul> <p><strong>NOTE</strong>: Due to the smaller overall sample size for the Birth weight phenotype, we do not include training data for the `N=160000` setting.<br><br></p> <p>The <strong>folder structure</strong> of the GWAS data for each phenotype is as follows:</p> <ul> <li><code>train</code> <ul> <li><code>N_5000</code> <ul> <li><code> fold_1</code> <ul> <li><code>chr_1.PHENO1.glm.linear</code></li> <li><code>chr_2.PHENO1.glm.linear</code></li> <li><code>...</code></li> </ul> </li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> <li><code>N_10000</code></li> <li><code>N_20000</code></li> <li><code>N_40000</code></li> <li><code>N_80000</code></li> <li><code>N_160000</code></li> <li><code>full</code></li> </ul> </li> <li><code>validation</code> <ul> <li><code>fold_1</code> <ul> <li><code>chr_1.PHENO1.glm.linear</code></li> <li><code>chr_2.PHENO1.glm.linear</code></li> <li><code>...</code></li> </ul> </li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> <li><code>test</code> <ul> <li><code>fold_1</code></li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> </ul> <p>For more details about the GWAS study, Quality Control (QC) criteria, or other information, please consult our publication:</p> <p>Zabad, S., Gravel, S., & Li, Y. (2023). <strong>Fast and accurate Bayesian polygenic risk modeling with variational inference.</strong> The American Journal of Human Genetics, 110(5), 741–761. <a href="https://doi.org/10.1016/j.ajhg.2023.03.009" rel="nofollow">https://doi.org/10.1016/j.ajhg.2023.03.009</a></p> <p>If you use this data in your work, please cite the publication above.</p> <p> </p>
Virtual Screening and Testing of GSK-3 Kinase Inhibitors Using human SH-SY5Y Neuronal cells Expressing Tau Folding Reporter and Mouse Hippocampal Primary Neuron Culture Under Tau Cytotoxicity
<p>Supplementary Figure S1. for IJMS</p>
"Robust Folding of Elastic Origami" Data Supplement
<p>Data sets for "Robust Folding of Elastic Origami", meant to be used with Mathematica notebooks provided at <a href="https://github.com/meleetrimble/robust-folding-paper-support">https://github.com/meleetrimble/robust-folding-paper-support</a>.</p>
The GPS velocity of Kepingtagh fold-and-thrust belt
<p>Our GPS dataset for the Kepingtagh FTB started in 2008 and ended in 2019, using data from a variety of GPS instrument types. For every GPS campaign survey, we set 30s sampling rates and observed for 48-96 hours every GPS site to ensure that at least one full UTC session (and often two or more) was recorded. Our GPS observation network consists of 73 GPS stations across the entire Kepingtagh FTB (Table S1). We estimated GPS velocities for 32 campaign sites of the enhanced CMONOC project from 2008 to 2019, and 41 sites of our GPS campaigns measured in 2017, 2018, and 2019.</p>
Simulation Data for Design of Peptides that Fold and Self-Assemble on Graphite
<p>This data set for the manuscript entitled "Design of Peptides that Fold and Self-Assemble on Graphite" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software NAMD, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text and supporting information. They include molecular model structure files (NAMD psf or Amber prmtop format), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and trajectories in dcd format (downsampled to 10 ns per frame). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>Version: 2.0</p> <p>Changes versus version 1.0 are the addition of the free energy of folding, adsorption, and pairing calculations (Sim_Figure-7) and shifting of the figure numbers to accommodate this addition.</p> <p><br> Conventions Used in These Files<br> ===============================</p> <p>Structure Files<br> ----------------<br> - graph_*.psf or sol_*.psf (original NAMD (XPLOR?) format psf file including atom details (type, charge, mass), as well as definitions of bonds, angles, dihedrals, and impropers for each dipeptide.)</p> <p>- graph_*.pdb or sol_*.pdb (initial coordinates before equilibration)<br> - repart_*.psf (same as the above psf files, but the masses of non-water hydrogen atoms have been repartitioned by VMD script repartitionMass.tcl)<br> - freeTop_*.pdb (same as the above pdb files, but the carbons of the lower graphene layer have been placed at a single z value and marked for restraints in NAMD)<br> - amber_*.prmtop (combined topology and parameter files for Amber force field simulations)<br> - repart_amber_*.prmtop (same as the above prmtop files, but the masses of non-water hydrogen atoms have been repartitioned by ParmEd)</p> <p>Force Field Parameters<br> ----------------------<br> CHARMM format parameter files:<br> - par_all36m_prot.prm (CHARMM36m FF for proteins)<br> - par_all36_cgenff_no_nbfix.prm (CGenFF v4.4 for graphene) The NBFIX parameters are commented out since they are only needed for aromatic halogens and we use only the CG2R61 type for graphene.<br> - toppar_water_ions_prot_cgenff.str (CHARMM water and ions with NBFIX parameters needed for protein and CGenFF included and others commented out)</p> <p>Template NAMD Configuration Files<br> ---------------------------------<br> These contain the most commonly used simulation parameters. They are called by the other NAMD configuration files (which are in the namd/ subdirectory):<br> - template_min.namd (minimization)<br> - template_eq.namd (NPT equilibration with lower graphene fixed)<br> - template_abf.namd (for adaptive biasing force)</p> <p>Minimization<br> -------------<br> - namd/min_*.0.namd</p> <p>Equilibration<br> -------------<br> - namd/eq_*.0.namd</p> <p>Adaptive biasing force calculations<br> -----------------------------------<br> - namd/eabfZRest7_graph_chp1404.0.namd<br> - namd/eabfZRest7_graph_chp1404.1.namd (continuation of eabfZRest7_graph_chp1404.0.namd)</p> <p>Log Files<br> ---------<br> For each NAMD configuration file given in the last two sections, there is a log file with the same prefix, which gives the text output of NAMD. For instance, the output of namd/eabfZRest7_graph_chp1404.0.namd is eabfZRest7_graph_chp1404.0.log.</p> <p>Simulation Output<br> -----------------<br> The simulation output files (which match the names of the NAMD configuration files) are in the output/ directory. Files with the extensions .coor, .vel, and .xsc are coordinates in NAMD binary format, velocities in NAMD binary format, and extended system information (including cell size) in text format. Files with the extension .dcd give the trajectory of the atomic coorinates over time (and also include system cell information). Due to storage limitations, large DCD files have been omitted or replaced with new DCD files having the prefix stride50_ including only every 50 frames. The time between frames in these files is 50 * 50000 steps/frame * 4 fs/step = 10 ns. The system cell trajectory is also included for the NPT runs are output/eq_*.xst.</p> <p>Scripts<br> -------<br> Files with the .sh extension can be found throughout. These usually provide the highest level control for submission of simulations and analysis. Look to these as a guide to what is happening. If there are scripts with step1_*.sh and step2_*.sh, they are intended to be run in order, with step1_*.sh first.</p> <p><br> CONTENTS<br> ========</p> <p>The directory contents are as follows. The directories Sim_Figure-1 and Sim_Figure-8 include README.txt files that describe the files and naming conventions used throughout this data set.</p> <p>Sim_Figure-1: Simulations of N-acetylated C-amidated amino acids (Ac-X-NHMe) at the graphite–water interface.</p> <p>Sim_Figure-2: Simulations of different peptide designs (including acyclic, disulfide cyclized, and N-to-C cyclized) at the graphite–water interface.</p> <p>Sim_Figure-3: MM-GBSA calculations of different peptide sequences for a folded conformation and 5 misfolded/unfolded conformations.</p> <p>Sim_Figure-4: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-5: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 295 K.</p> <p>Sim_Figure-5_replica: Temperature replica exchange molecular dynamics simulations for the peptide cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) with 20 replicas for temperatures from 295 to 454 K.</p> <p>Sim_Figure-6: Simulation of the peptide molecule cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) in free solution (no graphite).</p> <p>Sim_Figure-7: Free energy calculations for folding, adsorption, and pairing for the peptide CHP1404 (sequence: cyc(GTGSGTG-GPGG-GCGTGTG-SGPG)). For folding, we calculate the PMF as function of RMSD by replica-exchange umbrella sampling (in the subdirectory Folding_CHP1404_Graphene/). We make the same calculation in solution, which required 3 seperate replica-exchange umbrella sampling calculations (in the subdirectory Folding_CHP1404_Solution/). Both PMF of RMSD calculations for the scrambled peptide are in Folding_scram1404/. For adsorption, calculation of the PMF for the orientational restraints and the calculation of the PMF along z (the distance between the graphene sheet and the center of mass of the peptide) are in Adsorption_CHP1404/ and Adsorption_scram1404/. The actual calculation of the free energy is done by a shell script ("doRestraintEnergyError.sh") in the 1_free_energy/ subsubdirectory. Processing of the PMFs must be done first in the 0_pmf/ subsubdirectory. Finally, files for free energy calculations of pair formation for CHP1404 are found in the Pair/ subdirectory.</p> <p>Sim_Figure-8: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) where the peptides are far above the graphene–water interface in the initial configuration.</p> <p>Sim_Figure-9: Two replicates of a simulation of nine peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-9_scrambled: Two replicates of a simulation of nine peptide molecules with the control sequence cyc(GGTPTTGGGGGGSGGPSGTGGC) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-10: Adaptive biasing for calculation of the free energy of the folded peptide as a function of the angle between its long axis and the zigzag directions of the underlying graphene sheet.</p> <p> </p>
Distribution. Disjunct in South Africa in Cape Fold Belt Mts in Western Cape and isolated populations in S Free State and Eastern Cape N of 33° S. in Muridae
Distribution. Disjunct in South Africa in Cape Fold Belt Mts in Western Cape and isolated populations in S Free State and Eastern Cape N of 33° S.
On the role of native contact cooperativity in protein folding
<p>This repository provides supporting information related to the paper "On the role of contact cooperativity in protein folding" by Wang, Frechette and Best ( <a href="https://doi.org/10.1073/pnas.2319249121">https://doi.org/10.1073/pnas.2319249121</a> ) and contains: </p> <ol> <li>C++ code using Boltzmann machines to learn the parameters of an Ising model that best describes contact formation observed in an MD simulation. </li> <li>The fitted parameters and python scripts used for plotting several of the figures in the paper.</li> </ol>
MD simulations associated to the paper "A PDZ tandem repeat folds and unfolds via different pathways"
<p>These are the MD simulation data generated for this study. The files are:</p> <p>x11_plain_simulation: training data, charmm22*+tip3p simulation of X11 PD1-PDZ2</p> <p>x11_random_coil: reference random coil data for X11 PDZ1-PDZ2</p> <p>x11_e0.21_0.335_folding_t310: 200 folding simulations</p> <p>x11_e0.21_0.335_melting_t380: 200 unfolding simulations</p> <p>x11d2_e0.21_0.33_folding_t310: 200 folding simulations for the isolated PDZ2</p> <p>x11_e0.21_0.33_folding_t310_d2open: 200 folding simulations for X11 PDZ1-PDZ2 starting with PDZ1 folded and the C-ter tail bound</p>
Role of net charges and charge clustering in a dynamic disordered complex between an IDP and a folded domain.
<p>Simulation input files and trajectories for the paper "Role of net charges and charge clustering in a dynamic disordered complex between an IDP and a folded domain". </p> <p>Files here:</p> <p>cg_prota_multigd.tgz -- coarse-grained simulations of prothymosin alpha with multiple globular domains</p> <p>gd_prota_cg_umbrella.tgz -- coarse grained umbrella sampling simulations of prothymosin alpha associating with a single globular domain</p> <p>prota_aa.tgz -- all-atom simulations in explicit water of prothymosin alpha alone, with several force fields</p> <p>gd_amber03ws.tgz -- all-atom simulations in explicit water of globular domain alone, with amber ff03ws force field</p> <p>gd_des-amber.tgz-- all-atom simulations in explicit water of globular domain alone, with des-amber force field</p> <p>prota+gd_des-amber-SF1.0.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, des-amber-SF1.0 force field</p> <p>prota+gd_des-amber.tgz-- all-atom simulations in explicit water of prothymosin alpha + single globular domain, des-amber force field</p> <p>prota+gd_amber_ff99sbws.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff99sbws force field</p> <p>prota+gd_amber_ff99sb-disp.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff99sb-disp force field</p> <p>prota+gd_amber_ff03ws.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff03ws force field</p> <p> </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>
FIG. 4. Character states for the left and right dorsolateral folds for R in Morphological Change during Rapid Population Expansion Confounds Leopard Frog Identifications in the Southwestern United States
FIG. 4. Character states for the left and right dorsolateral folds for R. berlandieri, uncertain individuals, and R. yavapaiensis. Points along the dark line indicate individuals with the same character state for the left and right dorsolateral folds. See text for description of character states. We have applied a small amount of random noise to the points to enhance visibility.
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.