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ShareScore release 0.9.0
Dataset results
295 results for “Structure prediction”
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>
The AF2 predicted structures and MD simulation results for the paper "In-situ structural insights into activity regulation of mammalian pyruvate dehydrogenase complex"
<p>Thank you for your interest in our work. You can discover content that interests you within the respective compressed packages, accompanied by “readme” files.</p>
Protein structure data for "AI-predicted protein deformation encodes energy landscape perturbation"
<p>AF2-predicted protein structures of WT and mutant proteins that have corresponding ddG measurements in the ThermoMutDB database of protein mutant stability measurements. PDB structures are compressed using <a href="https://github.com/steineggerlab/foldcomp/">FoldComp</a>, and saved in "structures.zip".</p> <p>Summary of the final dataset and results can be found in "results_summary.pkl".</p> <p>Code used to plot figures can be found in "code4figs.zip".</p>
Structure Databases: Predicted Influence of Organic Structure Directing Agents on Al Distributions in CHA Zeolites
<p>Structures and energies associated with the paper: Predicted Influence of Organic Structure Directing Agents on Al Distributions in CHA Zeolites</p>
FIGURE 4. Predicted secondary structure for D1–D1 in Aliterella shaanxiensis (Aliterellaceae), a new coccoid cyanobacterial species from China
FIGURE 4. Predicted secondary structure for D1–D1′ helix of 16S–23S rRNA intergenic spacer of three Aliterella strains. (a) A. antarctica CENA408T; (b) A. atlantica CENA595T; (c) A. shaanxiensis FACHB–2293.
FIGURE 5. Predicted secondary structure for Box-B in Aliterella shaanxiensis (Aliterellaceae), a new coccoid cyanobacterial species from China
FIGURE 5. Predicted secondary structure for Box-B helix of 16S–23S rRNA intergenic spacer of three Aliterella strains. (a) A. antarctica CENA408T; (b) A. atlantica CENA595T; (c) A. shaanxiensis FACHB–2293.
Exploring zero-shot structure-based protein fitness prediction
<p>This repository contains data used in Exploring zero-shot structure-based protein fitness<br>prediction.</p> <p>Directions to use this data can be found on <a href="https://github.com/gitter-lab/benchmarking-structure-based-models">our GitHub repository</a>.</p> <ol> <li><code>experimental_struct_artifacts</code> contains the experimentally determined structures for ProteinGym assays used in our analysis along with the reference file needed to generate ESM inverse folding predictions for these structures in ProteinGym.</li> <li><code>results</code> contains the prediction results obtained by running SSEmb on the 216 ProteinGym assays being considered in this study.</li> <li><code>test.tar.gz</code> contains all the structures from ProteinGym as well as MSAs generated using mmseqs2. To use this directory: <ul> <li>Setup SSEmb as directed in its <a href="https://github.com/KULL-Centre/_2023_Blaabjerg_SSEmb">repository</a></li> <li>Download this file and extract it in the data folder.</li> </ul> </li> </ol> <p> </p> <p> </p>
Predicted structures of the periplasmic adaptor protein, CmeA
<p>Predicted structures for CmeA, sequence alignments, and a fully assembled model for CmeABC (Chapter 6 of Kahlan Newman's Doctoral Thesis). </p>
Supplementary Data for "Exploring structure-function relationships in engineered receptor performance using computational structure prediction"
<p>These data are supplementary data for the manuscript "<strong>Exploring structure-function relationships in engineered receptor performance using computational structure prediction</strong>", which has been submitted for consideration for publication. These data include protein structure predictions used in this study.</p>
Data for "Supervised machine learning methods for crystal structure prediction of the binary Cs-Te system"
<p>Crystal structures, high-throughput calculations and trained machine learning models presented in the paper "Supervised machine learning methods for crystal structure prediction of the binary Cs-Te system".</p> <ul> <li><em>crystal_datasets </em>contains the input/output data sets of crystal structures for high-throughput calculations and ML models.</li> <li><em>aiida_ht_calculations </em>contains the data regarding the high-throughput DFT calculations.</li> <li><em>ml_models</em> contains the trained ML models.</li> </ul> <p>Eeach zip-archive contains a jupyter-notebook examplifying how the data can be accessed and reused.</p>
fMRI free-viewing data, resting-state and structural in macaques associated with publication 'Social prediction modulates activity of macaque superior temporal cortex'
<p>Using a free-viewing and functional magnetic resonance imaging, we show that activity in a region of the macaque middle superior temporal (midSTS) cortex was specifically modulated by the predictability of social interactions. This region could be distinguished from other temporal regions involved in face processing. Using resting-state fMRI in anesthetized macaques, we showed that the connectivity between the face-responsive areas and the social prediction area was more integrated in macaques than in humans. We reproduce the social prediction results in a replication study and provide a control to rule out oculomotor implication through the FEF in the social prediction activity of the midSTS, using Transcranial Ultrasound Stimulation. Using standard geometric shape movement stimuli, we also show that macaques do not attribute mental state to shapes.</p>
A Deep Learning Approach to the Forward Prediction and Inverse Design of Plasmonic Metasurface Structural Color - Raw Data
<p>Reflection spectra of PDMS - Al nanorod metamaterials were collected using LUMERICAL FDTD simulations. PDMS material properties were defined using a refractive index of 1.41 and Al material properties were defined using frequency selective permittivities from the handbook of Palik. A total of 4620 structures were simulated, sweeping the following dimension parameters:</p> <ul> <li>Aluminium thickness (t)</li> <li>Pillar height (h)</li> <li>Pillar diameter (d)</li> </ul> <p>Reflectance spectra were converted into CIE 1931 chromaticity values (x,y). This dataset is comprehensive and allows for the development of deep learning models for the forward and inverse design of the given metamaterial structure as detailed in the associated manuscript. The associated manuscript and supporting documentation provide extensive details of data collection and processing methods.</p> <p> </p>
Structure prediction from SARS-CoV-2 accessory proteins ORF-6
<p>Structure prediction made with Collabfold for SARS-CoV-2 accessory protein ORF-6.</p> <p>The archive contains both the structure and</p>
Structure prediction from SARS-CoV-2 accessory proteins ORF-7B
<p>Structure prediction made with Collabfold for SARS-CoV-2 accessory protein ORF-7B.</p> <p>The archive contains both the structure and the logs from the prediction.</p>
Data for Structural Features of Interfacial Water Predict the Hydrophobicity of Chemically Heterogeneous Surfaces
<p>Scripts, raw and processed data, Jupyter notebooks, and force field files for the simulations performed in:</p> <p>B. C. Dallin, A. S. Kelkar, and R. C. Van Lehn. “Structural Features of Interfacial Water Predict the Hydrophobicity of Chemically Heterogeneous Surfaces.” <em>Chemical Science </em><strong>2023</strong>.</p>
ActTemp+sMSA dataset of "Biasing AlphaFold2 to predict GPCRs and Kinases with user-defined functional or structural properties"
<p>PDB models generated with the protocol described in "Biasing AlphaFold2 to predict GPCRs and Kinases with user-defined functional or structural properties"</p>
Predicted structures for comparison with IgFold
<p>Predicted antibody structures used to compare the accuracy of IgFold to alternative methods, including RepertoireBuilder, DeepAb, ABlooper, NanoNet, and AlphaFold.</p>
Data and Code from "Structure-based prediction of Ras-effector binding affinities and design of 'branchegetic' interface mutations"
<p>Data, data generation and data analysis for manuscript "Structure-based prediction of Ras-effector binding affinities and design of ‘branchegetic’ interface mutations", currently available as a preprint <a href="https://doi.org/10.1101/2022.09.04.506480">here</a>.</p> <p>Contains the following directories:</p> <ul> <li>01_models: Contains all scripts for model generation and selection, as well as some of the generated and selected models. <ul> <li>01_inputs: The different inputs for the homology modelling pipeline. This includes AlphaFold single and complex templates, PDB templates and sequence alignments.</li> <li>02_validation: Model generation and initial selection for validation models, based on AF2 single models and PDB complex models.</li> <li>03_production1: Model generation and initial selection for Ras effector complexes, based on AF2 single models and PDB complex models.</li> <li>04_production2: Model generation and initial selection for Ras effector complexes, based on AF2 single models and AF2 complex models.</li> <li>05_selection_optics: Code and analysis for selection by unsupervised learning using OPTICS.</li> </ul> </li> <li>02_selected_models: The three representative models selected for each complex.</li> <li>03_affinity_prediction: Contains code and data for the prediction of binding affinities for Ras effector complexes.</li> <li>04_branch_pruning: Contains code and data for branch pruning analysis.</li> <li>05_systems_analysis: Contains code and data for the analysis of Ras effector systems based on affinities derived from affinity prediction and branch pruning analysis.</li> <li>06_visualization: Information on where in the raw data the panels for the figures in the manuscript can be found.</li> </ul>
Source data belonged to "Establishing structure-property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach"
<p>This record contains all the necessary data to obtain the results of the study "Establishing structure-property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach"</p>
Datasets associated with the publication: "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".
<p>ECMWF HRES and ERA5 datasets for the paper "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".</p> <p>Content: </p> <p>========================================================================================</p> <p>Storm Friederike:</p> <p>ECMWF HRES forecast, 18.01.2018 00:00 UTC - 23:00 UTC, hourly, GRIB-format.</p> <p>ECMWF ERA5 reanalysis, 16.01.2018 12:00 UTC - 19.01.2018 00:00 UTC, twelve-hourly data, GRIB-format.</p> <p>========================================================================================</p> <p>Storm Vladiana:</p> <p>ECMWF HRES analysis, 23.09.2016 00:00 UTC - 23.09.2016 00:00 UTC, six-hourly data, rotated North Pole (latitude: 51˚, longitude: 160˚), NetCDF-format.</p> <p>========================================================================================</p> <p>Storm Egon:</p> <p>ECMWF ERA5 reanalysis, 12.01.2017 00:00 UTC - 13.01.2017 06:00 UTC, six-hourly data, GRIB-format.</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.