Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
261
datasets available to search
ShareScore release 0.9.0
Dataset results
261 results for “protein prediction”
Input Data for "Protein Function Prediction for newly sequenced organisms"
<p>The input sequence files in FASTA format and the detailed list of all organisms excluded when testing each specific bacterium.</p>
MetalNet: Prediction of metal binding sites in protein
<p>We have developed a machine learning method named “MetalNet” to predict metal binding sites in proteins.</p> <p>This dataset contains the result of prediction with high confidence to bind metal ions. The table includes the Uniprot id, gene name and a short description of specific predicted protein and potential metal-binding residues predicted by MetalNet. </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>
Genomically predicted theoretical protein mass database for mass spectrometry (GPMsDB) evaluation datasets
<p>These are datasets obtained for the evaluation of GPMsDB (genomically predicted protein mass database) and its toolkits (GPMsDB-tk/GPMsDB-dbtk). The following datasets are deposited.</p> <ul> <li>The genome sequences of the strains newly sequenced and added using GPMsDB-dbtk (genomes_added.zip)</li> <li>MALDI-TOF-MS peak lists obtained from reference bacterial and archaeal strains (MALDI_peaklists.zip)</li> <li>16S rRNA gene sequences of the faecal isolates (mice_isolates_16S_nanopore.zip)</li> <li>Metagenome-assembled genomes from mouse faeces (mice_MAGs.zip)</li> </ul>
Protein biomarkers for early predictions of hypoxic ischemic encephalopathy
<p>Perinatal asphyxia is a temporary interruption of oxygen availability that results in neonatal morbidity and mortality. The exact burden of perinatal asphyxia is unknown due to the lack of valid and accepted diagnostic criteria applicable to resource-limited settings. After sepsis, it is the second most important cause of neonatal death. Though there are tests available that can indicate whether an infant is suffering from birth asphyxia or not, they are not the gold standard. There is a dire need for early detection biomarkers for birth asphyxia so that the outcome of birth asphyxia can be avoided. Urinary and serum proteomics has rapidly developed and standard collection and experiment protocols are now available. With the help of the ‘omics’ approach, we can speed up biomarker discovery and widely use it to facilitate diagnostic and therapeutic developments for many diseases. Some of the promising biomarkers found in urine and serum are APOC3, ORM1, SOD1, FABP1, and AZGP1.</p>
Trypanosoma brucei predicted protein structures, part 2 of 2
<p>AlphaFold2-predicted protein structures for the <em>Trypanosoma brucei</em> (TREU927) proteome, predicted using input multiple sequence alignments optimised for the Discoba lineage in which <em>T. brucei </em>sits. The structure prediction methodology was exactly as described in <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p> <p>This deposition contains part 2 of 2. To get the full dataset, also download TbruceiTREU927_part1.zip from <a href="https://zenodo.org/record/7940748">doi:10.5281/zenodo.7940748</a>.</p> <p>Data are organised with one directory per <em>T. brucei </em>TREU927 gene ID (eg. Tb927.1.3600). Within each directory you will find:</p> <p><strong><gene id>_predmap.png</strong> A left to right representation of the linear protein sequence with one pixel per amino acid. Each horizontal bar represents one structure prediction, colour coded by pLDDT. For small proteins, there will likely be one prediction of the full-length protein. For large proteins, there may be many overlapping predictions.</p> <p><strong><gene id>_<start aa>-<end_aa> </strong>A directory containing structure prediction of that gene ID between the start and end amino acid. Within this directory you will find:</p> <p><strong><gene id>_<start aa>-<end_aa>.json</strong> The full data in a JSON format, including linear protein sequence and metadata, along with 5 predicted protein structures ranked from best to worst overall pAE. For each predicted protein structure, the structure (PDB format), its pLDDT per residue and pairwise pAE.</p> <p><strong><gene id>_<start aa>-<end_aa>_1.pdb</strong> The PDB file of the highest ranked structure.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-pae.png</strong> A plot of pAE, for the highest ranked structure, at one pixel per amino acid. Shade of green represents pAE for that amino acid pair, see below.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-plddt.png</strong> A plot of pLDDT, for the highest ranked structure, at one horizontal pixel per amino acid. Bar height and colour both represent pLDDT for that amino acid, see below.</p> <p>PDB structure and pAE/pLDDT of lower ranked models are embedded in the JSON file.</p> <p>All pLDDT and pAE plots use the colour scales used by https://alphafold.ebi.ac.uk/: For pLDDT: > 90 (dark blue), 90 > pLDDT > 70 (light blue), 70 > pLDDT > 50 (orange), < 50 (yellow) discontinuous. For pAE: 0 (white angstrom) to dark green (32 angstrom) continuous.</p> <p>If you use this resource, please cite this Zenodo deposition and <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p>
Trypanosoma brucei predicted protein structures, part 1 of 2
<p>AlphaFold2-predicted protein structures for the <em>Trypanosoma brucei</em> (TREU927) proteome, predicted using input multiple sequence alignments optimised for the Discoba lineage in which <em>T. brucei </em>sits. The structure prediction methodology was exactly as described in <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p> <p>This deposition contains part 1 of 2. To get the full dataset, also download TbruceiTREU927_part2.zip from <a href="http://zenodo.org/record/7948119">10.5281/zenodo.7948119</a>.</p> <p>Data are organised with one directory per <em>T. brucei </em>TREU927 gene ID (eg. Tb927.1.3600). Within each directory you will find:</p> <p><strong><gene id>_predmap.png</strong> A left to right representation of the linear protein sequence with one pixel per amino acid. Each horizontal bar represents one structure prediction, colour coded by pLDDT. For small proteins, there will likely be one prediction of the full-length protein. For large proteins, there may be many overlapping predictions.</p> <p><strong><gene id>_<start aa>-<end_aa> </strong>A directory containing structure prediction of that gene ID between the start and end amino acid. Within this directory you will find:</p> <p><strong><gene id>_<start aa>-<end_aa>.json</strong> The full data in a JSON format, including linear protein sequence and metadata, along with 5 predicted protein structures ranked from best to worst overall pAE. For each predicted protein structure, the structure (PDB format), its pLDDT per residue and pairwise pAE.</p> <p><strong><gene id>_<start aa>-<end_aa>_1.pdb</strong> The PDB file of the highest ranked structure.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-pae.png</strong> A plot of pAE, for the highest ranked structure, at one pixel per amino acid. Shade of green represents pAE for that amino acid pair, see below.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-plddt.png</strong> A plot of pLDDT, for the highest ranked structure, at one horizontal pixel per amino acid. Bar height and colour both represent pLDDT for that amino acid, see below.</p> <p>PDB structure and pAE/pLDDT of lower ranked models are embedded in the JSON file.</p> <p>All pLDDT and pAE plots use the colour scales used by https://alphafold.ebi.ac.uk/: For pLDDT: > 90 (dark blue), 90 > pLDDT > 70 (light blue), 70 > pLDDT > 50 (orange), < 50 (yellow) discontinuous. For pAE: 0 (white angstrom) to dark green (32 angstrom) continuous.</p> <p>If you use this resource, please cite this Zenodo deposition and <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</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>
Alphafold predicted structures of VPS13 proteins from model organisms
<p>This upload contains AlphaFold-predicted structures of VPS13 proteins from a variety of organisms. Given the large size of these proteins, only partial sequences were predicted with AlphaFold(1) and the resulting structures were aligned in PyMOL(2). A summary of the structures uploaded here is presented as a collection of domain cartoons in the "VPS13 domain organization across eukaryotic evolution.pdf" file. </p> <p>The structures were generated with AlphaFold v2.029 on the Yale High Performance Cluster. Each *.zip file contains the best ranked predictions (out of five) for each sequence (*.pdb files) and the PyMOL assembled full structure (*.pse file). In a few cases, where a good alignment was not possible due to long disordered regions in the C-terminal portions (mostly in proteins from <em>D. discoideum</em> and <em>A. thaliana</em>), the full structures were aligned manually in PyMOL based on the continuity of the lipid transfer groove. The structures in PyMOL can be colour-coded by the confidence value of AlphaFold predictions using the following prompt:</p> <p>set_color n0, [0.051, 0.341, 0.827]<br> set_color n1, [0.416, 0.796, 0.945]<br> set_color n2, [0.996, 0.851, 0.212]<br> set_color n3, [0.992, 0.490, 0.302]<br> color n0, b < 100; color n1, b < 90<br> color n2, b < 70; color n3, b < 50</p> <p>Considering that full length structures were assembled by aligning different protein fragments and in view of the presence of flexible loops with low prediction confidence scores, the relative positions of different folded domains are not necessarily correct.</p> <p> </p> <p><strong>References</strong></p> <p>1. J. Jumper, <em>et al.</em>, Highly accurate protein structure prediction with AlphaFold. <em>Nature</em> 596, 583–589 (2021).</p> <p>2. The PyMOL Molecular Graphics System, Version 2.0. Schrödinger LLC.</p>
Mutation Effect Prediction of Proteins using RoseTTAFold
<p>Deep mutational scanning datasets used for 'Mutation Effect Prediction of Proteins using RoseTTAFold'. </p> <p>Target MSAs, RF predicted PDBs and experimental values (csvs) are provided. Results published and referenced in paper are also provided.</p>
PRESCOTT/ESCOTT/iGEMME mutational effect predictions of all single point mutations for about 3000 proteins
<p>PRESCOTT/ESCOTT/iGEMME mutational effect predictions of all single point mutations for about 3000 proteins</p> <p>This dataset contains four tgz files:</p> <p> </p>
Alignment-based protein mutational landscape prediction: doing more with less
<div class="page"> <div class="layoutArea"> <div class="column"> <p>The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.</p> </div> </div> </div>
Dataset for Peptide binder design with inverse folding and protein structure prediction
<p>Dataset for a paper on peptide design</p> <p> </p> <p><br> mutated_peptides - results for randomly intriduced mutations in protein-peptide complexes that can be predicted at 2 Å (Figure 1)<br> pdb_peptide - variation in the number of recycles (1-10) for 96 peptides (Figure 1)<br> minibinder - results for the minibinder set (Figure 2)<br> Pfam - results for the Pfam set (Figures 4+5)<br> protein_mpnn - results on protein_mpnn test set (Figure 6)</p> <p> </p> <p> </p>
Novel Blood Test to Predict Safe Foods for Infants and Toddlers With Food Protein-induced Enterocolitis Syndrome (FPIES)
ClinicalTrials.gov study NCT04644783. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study Evaluating Targeted Therapies in Participants Who Have Advanced Solid Tumors With Genomic Alterations or Protein Expression Patterns Predictive of Response
ClinicalTrials.gov study NCT04632992. IPD Sharing: YES. Countries: 1. Publications: 1.
Alignment-based protein mutational landscape prediction: doing more with less
Open the record for dataset details and reuse information.
Genomics of new ciliate lineages provides insight into the evolution of obligate anaerobiosis - single gene datasets for phylogenomic analysis of anaerobic ciliates (SAL, Ciliophora), protein datasets for mitochondrial pathways prediction, and mitochondrial genomes
Open the record for dataset details and reuse information.
The predicted and experimental peptide binding information (PEPBI) database described in: A paired database of predicted and experimental protein peptide binding information
Open the record for dataset details and reuse information.
F I G U R E 1 in Composition and divergence of coronavirus spike proteins and host ACE2 receptors predict potential intermediate hosts of SARS- CoV-2
F I G U R E 1 Structural diagrams of spike glycoproteins of SARS‐CoV, MERS‐CoV, and SARS‐CoV‐2. All spike proteins of coronaviruses contain S1 subunit and S2 subunit, which were divided by the S cleavage sites. FP, fusion peptide; HR, heptad repeat 1 and heptad repeat 2; RBD, receptor‐binding domain, contains core binding motif in the external subdomain; SP, signal peptide
F I G U R E 2 in Composition and divergence of coronavirus spike proteins and host ACE2 receptors predict potential intermediate hosts of SARS- CoV-2
F I G U R E 2 Phylogenetic analysis of sequences of coronavirus spike glycoproteins. The sequences of spike glycoproteins of SARS‐CoV‐2, bat SARS‐like CoV, pangolin SARS‐like CoV, and SARS‐CoV were analyzed. The red stars indicate pangolin SARS‐like CoV and bat SARS‐like CoV. Host flags are marked after the clusters. SARS‐CoV‐2, severe respiratory syndrome coronavirus‐2
ScienceDex guides
Understand access before you commit
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.