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261
datasets available to search
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Dataset results
261 results for “protein prediction”
Monocyte Chemotactic Protein-1 (MCP-1) Expressing Monocytes to Predict Preterm Delivery: PhenoMAP Study
ClinicalTrials.gov study NCT01340222. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Precision Crohn's Disease Management Utilizing Predictive Protein Panels (ENvISION)
ClinicalTrials.gov study NCT04131504. IPD Sharing: NO. Countries: 1. Publications: 1.
Can Albumin/C-reactive Protein Ratio be Utilized for Predicting Gestational Diabetes Diagnosis or the Adverse Outcomes
ClinicalTrials.gov study NCT06761053. IPD Sharing: NO. Countries: 1. Publications: 11.
Evaluation of a Plasma Protein Profile as a Predictive Biomarker for Metastatic Relapse in Triple Negative Breast Cancer Patients
ClinicalTrials.gov study NCT04438681. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Protein Electrophoresis as a Tool for Complications Prediction in COVID-19 Hospitalised Patients
ClinicalTrials.gov study NCT04414059. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Random Urine Protein-creatinin Ratio to Predict Magnitude of Proteinuria
ClinicalTrials.gov study NCT01623791. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Predictive Value of Maternal Blood Protein Signatures in Preterm Birth
ClinicalTrials.gov study NCT06664554. IPD Sharing: NO. Countries: 1. Publications: 2.
The Value of Pancreatic Stone Protein in Predicting Acute Appendicitis
ClinicalTrials.gov study NCT01610193. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Data from: Contemporary evolution of sea urchin gamete-recognition proteins: experimental evidence of density-dependent gamete performance predicts shifts in allele frequencies over time
Open the record for dataset details and reuse information.
Data from: Antibody responses to Plasmodium vivax Duffy binding and erythrocyte binding proteins predict risk of infection and are associated with protection from clinical malaria
Open the record for dataset details and reuse information.
PDNET for protein distance prediction
<p>Deep learning protein contacts and real-valued distances using PDNET</p>
Genomes and predicted proteins of Naegleria fowleri strains CDC:V212, 986, and ATCC30863
<p>Genomes and predicted proteins for three strains of <em>Naegleria fowleri</em>: strain CDC:V212, 986, and ATCC30863.</p> <p> </p> <p>These data have been made available for the publication of the manuscript <strong>"A comparative ‘omics approach to candidate pathogenicity factor discovery in the brain-eating amoeba <em>Naegleria fowleri</em>". </strong>See https://doi.org/10.1101/2020.01.16.908186 for more information.</p>
Supplementary Structural Models (SARS-CoV-2 Spike-RBD:ACE2 complex and TMPRSS2) - SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals
<p>Structural Models (PDB) of SARS-CoV-2 Spike RBD bound to ACE2 receptors of 215 animals.</p> <p>Structural model of Human TMPRSS2.</p> <p>Modelled using the FunMod pipeline and referenced in the preprint</p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.05.01.072371v5">SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals</a></p> <p> </p>
TECPR2 protein models predicted by three algorithms
<p>This ZIP-file contains the files used for TECPR2 protein modeling and resulting PDB (Protein Data Bank) files from three algorithms/pipelines (GalaxyWEB, trRosetta, SWISS-MODEL) used for clustering analysis and visualization in Neuser et al. ("Clinical, neuroimaging and molecular spectrum of <em>TECPR2-</em>associated hereditary sensory and autonomic neuropathy with intellectual disability").<br> We always used standard parameters and the respective top model ("model_1 | model01 | model1") for each algorithm/pipeline and/or downstream steps.</p>
Dataset for: "BACPHLIP: Predicting bacteriophage lifestyle from conserved protein domains"
<p>This is the dataset used in the manuscript titled "BACPHLIP: Predicting bacteriophage lifestyle from conserved protein domains". The dataset is necessary to run the code that can be found at <a href="https://github.com/adamhockenberry/dca-weighting">https://github.com/adamhockenberry/bacphlip-model-dev</a> and outside of the context of the code this data will likely not be super well-annotated or helpful. The code within that repository, however, should provide sufficient information about the structure and usage of this dataset. </p>
Data from: Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information
Do the amino acid sequence identities of residues that make contact across protein interfaces covary during evolution? If so, such covariance could be used to predict contacts across interfaces and assemble models of biological complexes. We find that residue pairs identified using a pseudo-likelihood based method to covary across protein-protein interfaces in the 50S ribosomal unit and 28 additional bacterial protein complexes with known structure are almost always in contact in the complex provided that the number of aligned sequences is greater than the average of the lengths of the two proteins. We use this method to make subunit contact predictions for an additional 36 protein complexes with unknown structures, and present models based on these predictions for the tripartite ATP-independent periplasmic (TRAP) transporter, the tripartite efflux system, the pyruvate formate lyase-activating enzyme complex, and the methionine ABC transporter.
Data from: Using viromes to predict novel immune proteins in non-model organisms
Immunity is mostly studied in a few model organisms, leaving the majority of immune systems on the planet unexplored. To characterize the immune systems of non-model organisms alternative approaches are required. Viruses manipulate host cell biology through the expression of proteins that modulate the immune response. We hypothesized that metagenomic sequencing of viral communities would be useful to identify both known and unknown host immune proteins. To test this hypothesis, a mock human virome was generated and compared to the human proteome using tBLASTn, resulting in 36 proteins known to be involved in immunity. This same pipeline was then applied to reef-building coral, a non-model organism that currently lacks traditional molecular tools like transgenic animals, gene-editing capabilities, and in vitro cell cultures. Viromes isolated from corals and compared with the predicted coral proteome resulted in 2503 coral proteins, including many proteins involved with pathogen sensing and apoptosis. There were also 159 coral proteins predicted to be involved with coral immunity but currently lacking any functional annotation. The pipeline described here provides a novel method to rapidly predict host immune components that can be applied to virtually any system with the potential to discover novel immune proteins.
Data from: Knowledge-based prediction of protein backbone conformation using a structural alphabet
Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlights the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.
SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions
Open the record for dataset details and reuse information.
DeepGO-SE protein function prediction model data
<p>Data for training and running DeepGO-SE protein function prediction model</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.