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261 results for “protein prediction”
Host-pathogen protein interactions predicted using structure
<p>This dataset accompanies a manuscript describing a method to predict host-pathogen protein interactions using structure:</p> <p>Host-pathogen protein interactions predicted by comparative modeling.<br /> Davis FP, Barkan DT, Eswar N, McKerrow JH, Sali A. Protein Sci (2007) 16:2585-2596.<br /> http://www.proteinscience.org/cgi/doi/10.1110/ps.073228407</p> <p>The files contain predictions made for 10 human pathogens including species of Mycobacterium, Apicomplexa, and Kinetoplastida. The species.all.zip files contain all interactions predictions for each species along with the filter criteria that each interaction passed. The species.filter.zip files contains the same information, but only for the subset of interactions that passed the biological and network-level filters. These files can be viewed in any spreadsheet program, such as Excel.</p> <p> </p> <p>Predictions were made for interactions between human and </p> <ol> <li>Mycobacterium tuberculosis: mtuber</li> <li>Mycobacterium leprae: mleprae</li> <li>Leishmania major: lmajor</li> <li>Trypanosoma brucei: tbrucei</li> <li>Trypanosoma cruzi: tcruzi</li> <li>Cryptosporidium hominis: chominis</li> <li>Cryptosporidium parvum: cparvum</li> <li>Plasmodium falciparum: pfalciparum</li> <li>Plasmodium vivax: pvivax</li> <li>Toxoplasma gondii: tgondi</li> </ol>
pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
<p>pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.</p>
Predicted structures of SLC-protein complexes and controls
<p>Solute carrier (SLC) transporters form a major superfamily which transport a wide range of substrates across cellular and organellar membranes. To study their regulation on protein level and position SLCs in the human interactome, we conducted a large-scale interrogation of the protein-protein interactions (PPIs) of SLCs employing affinity purification combined with mass spectrometry (AP-MS). This study resulted in thousands of novel protein interactions of SLCs. For a subset of SLC protein complexes, we performed structural predictions using AlphaFold (v2.2, v2.3 and v3). The dataset attached contains the structures in PDB/CIF format. The structures were further discussed in the associated manuscript. In addition, an annotation table is provided, which summarizes the scores for each modelled complex. </p>
PRESCOTT/ESCOTT/iGEMME mutational effect predictions of all single point mutations for ~3000 proteins
<p>This dataset contains all necessary data to reproduce our analyses on ~3000 proteins:</p> <p>It is made up of 4 compressed datasets. </p> <ol> <li>All colabfold MSAs and structures for ~3000 proteins: colabfold-sequences-structures-3000-proteins.tar.bz2</li> <li>All escott prediction: escott-v-1-6-0-max-two-components-colabfold-msas-entire-single-point-mutations-cvRC7.tgz</li> <li>All igemme predictions: escott-v-1-6-0-tjet-only-colabfold-msas-entire-single-point-mutations.tgz</li> <li>All gnomad v4.0.0 csv files used for prescott predictions: gnomadv4-0-0-csv-files.tgz</li> </ol> <p>The dataset contains list of 500 proteins used in determining PRESCOTT coefficients in TRAINING-all_gene_names_v4_set1_no_acmg.txt. </p> <p>Furthermore, the list of 1883 proteins used to measure for testing purpose is given in TESTING-all_gene_names_v4_set1_with_acmg.txt. </p>
Input features and benchmark data sets for protein complex prediction and E. coli proteome application by AF2Complex
<p>Benchmark data sets of AF2Complex, input features for application to E. coli proteome, and predicted structural models of E. coli Ccm I as described in</p> <p><strong>Predicting direct physical interactions in multimeric proteins with deep learning</strong></p> <p><em>Mu Gao, Davi Nakajima An, Jerry M. Parks, Jeffrey Skolnick</em></p> <ol> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/af2complex_bench.tar.gz">af2complex_bench.tar.gz</a>: Benchmark data sets CP17, Dimer1193 and Oligomer562, including input features for AF2Complex/AF-Multimer, both paired and unpaired MSAs, as well as sequences, experimental structures, and results presented in the AF2Complex work (~90GB de-compressed size)</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_Ccm_I.tar.gz">ecoli_Ccm_I.tar.gz</a>: Computational models of the<em> E. coli</em> Ccm I system</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_set.tar.gz">ecoli_sets.tar.gz</a>: Lists of benchmark sets of positive and negative PPIs from <em>E. coli</em></li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_af_fea.tar.gz">ecoli_af_fea.tar.gz: </a>Pre-generated input features of <em>E. coli</em> proteome for protein complex prediction and modeling by AF2Complex (4,429 proteins, ~800 GB de-compressed size). This data set can be used with AF2Complex to probe the interactions of any combinations among the 4,429 proteins of E. coli.</li> </ol> <p> </p>
Protein vibrational frequencies dataset: Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks
<p>Dataset for machine learning model, based on graph neural network, to predict protein natural frequencies using Graph Neural Networks. </p> <p><strong>Code</strong>: https://github.com/lamm-mit/ProteinMechanicsGNN</p> <p><strong>Paper</strong>: </p> <p>Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks</p> <p>Kai Guo and Markus J. Buehler</p> <p><em>Digital Discovery</em>, 2022, DOI: 10.1039/D1DD00007A</p>
Raw data and metadata of SiO2 NP physicochemical characterisation, in vitro investigations and in silico predictions on protein corona formation
<p>Raw data and metadata of SiO<sub>2</sub> NP physicochemical characterisation, <em>in vitro</em> investigations and<em> in silico</em> predictions on protein corona formation. Data repository for Hasenkopf I. et al., 2022. Please note that “pristine” NPs in metadata are referred to as “bare” NPs in the paper.</p> <p>1. NP phys-chem raw data (xlsx-formatted) + corresponding metadata (xlsx-formatted)</p> <p>2. NP-protein binding raw data (xlsx-formatted) + corresponding metadata (xlsx-/pdf-formatted, ImageLab-formatted analyses, pdf-formatted ImageLab reports)</p> <p>3. huRBL mediator release raw data (xlsx-formatted) + corresponding metadata (pdf-formatted)</p> <p>4. <em>in silico</em> modelling raw data (xlsx-formatted) + corresponding metadata (config-/map-formatted)</p> <p> </p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Benchmark data sets of CDPred as described in</p> <p><strong>Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks</strong></p> <p>Zhiye Guo<sup>1</sup>, Jian Liu<sup>1</sup>, Jeffrey Skolnick<sup>2</sup>, Jianlin Cheng<sup>1*</sup></p> <p><sup>1 </sup>Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211</p> <p><sup>2 </sup>School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000</p> <p>*Corresponding author (chengji@missouri.edu)</p> <p>There is four test dataset in this package, each test dataset contains four different folders and one list file. The <strong>afpred_pdb</strong> includes all the corresponding monomer structures predicted by alphafold. The <strong>cdpred_output </strong>includes the prediction results of our tool CDPred for each dataset. The <strong>pre_gen_a3m </strong>includes the multiple sequence alignments file used by CDPred to generate prediction results. And the <strong>true_pdb </strong>includes the fasta file for the test dataset and its heavy atom distance map (h_dist) and carbon alpha distance map (real_dist) that extract from the native structure.</p> <p>HomoTest1: The homodimer test dataset contains 28 targets collect from CASP_CAPRI 10-13</p> <p>HomoTest2: The homodimer test dataset contains 23 targets collect from CASP_CAPRI 13-14</p> <p>HeteroTest1: The heterodimer test dataset contains 9 targets collect from CASP_CAPRI13-14</p> <p>HeteroTest2: The heterodimer test dataset contains 55 targets collect from PDB bank 09-2021 to 11-2021</p>
Predicting compound-protein interaction using hierarchical graph convolutional networks
<p>This repository contains the datasets which are used in the article "Predicting Compound-Protein Interaction using Hierarchical Graph Convolutional Networks".</p>
Input files for "Predicting the orientation of adsorbed proteins steered with electric fields using a simple electrostatic model"
<p>In files to run PyGBe (https://github.com/pygbe/pygbe), and reproduce the results from the J. Phys. Chem. B article "Predicting the orientation of adsorbed proteins steered with electric fields using a simple electrostatic model". </p>
A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2
<p>Data produced and analyzed in the manuscript "A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2" by Pasquadibisceglie et al.</p> <p><br> If you include these data in your manuscript, please cite: Pasquadibisceglie A, Leccese A and Polticelli F (2022) A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2. <em>Front. Chem.</em> 10:1004815. doi: 10.3389/fchem.2022.1004815</p>
Multimodal dataset: Protein Function Prediction using STRING data & COVID19 Mortality Model by EI
<p>The PFP.zip file contains 1. 5 well-formated GO terms dataset for EI, 2. STRING data 3. GO term annotation. The last two could be merged by the 'generate_data.py' script in https://github.com/GauravPandeyLab/ensemble_integration</p> <p>The covid19_model_built.zip contained the EI model built based on the COVID-19 Mortality dataset, the detail of usage are here:.</p>
Data for paper: Transfer learning for cross-context prediction of protein expression from 5'UTR sequence
<p>This depsit contains data for the paper entitled: "<strong>Transfer learning for cross-context prediction of protein expression from 5'UTR sequence</strong>".</p> <p>The <strong>rebeca.zip</strong> file contains a snapshot of the rebeca package which can be used to train, fine tune and test the CONV-LSTM model used in this study.</p> <p>The <strong>datasets.zip</strong> file contains the compiled sequence to expression datasets from across all Flow-seq expressions considered in this study. </p> <p>The <strong>analysis.zip</strong> file contains all data files and jupyter notebooks necessary to reproduce our analysis. Each Flow-seq study has a dedicated folder (e.g., `fepB') with two sub-folders: 1. The `data\_split' folder, which contains the steps necessary to split the Flow-seq data for our ML experiments (a `readme.txt' file describes the input and output files and a jupyter notebook is available to reproduce the data split); 2. The `data\_analysis' folder, which contains a jupyter notebook and the necessary input files to reproduce the analysis of our experiments.</p>
Gene and protein sequence features augment HLA class I ligand predictions
<p>Dataset and analyses supporting the manuscript "Gene and protein sequence features augment HLA class I ligand predictions".</p> <p>The "peptides" files contain the mass-spec detected peptides obtained from HLA ligandomics performed on the indicated tumor lines. </p> <p>The "protein data" files contain the RNAseq data (TPM) and Ribosome profiling data (ribosome occupancy) per protein, for each tumor line. </p> <p>The "source data" zip archive contains the source data underlying the figures of the manuscript.</p> <p>The "HLA ligandome analyses" zip archive contains the R scripts used for all data analysis in the manuscript, including all data and output files. These analyses can also be found at https://github.com/kasbress/HLA_Ligandome_Analyses/</p> <p> </p> <p> </p>
How can we biochemically validate protein function predictions with the Ras GTPase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-74ad-345f">How can we biochemically validate ProteinCartography with the Ras GTPase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> using human HRas and KRas as our inputs for a single run (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P01112/entry">P01112</a> and <a href="https://www.uniprot.org/uniprotkb/P01116/entry">P01116</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
How can we biochemically validate protein function predictions with the deoxycytidine kinase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-1e5d-e272">How can we biochemically validate ProteinCartography with the deoxycytydine kinase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> on the deoxycytidine kinase (dCK) using human dCK as our input (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P27707/entry">P27707</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
Amino acid sequences of the proteins predicted from the whole genome of hilsa shad (Tenualosa ilisha) of the Bay of Bengal
<p>Gene prediction was performed by AUGUSTUS (Stanke et al., 2006) from the whole genome sequence of <em>T. ilisha</em> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/400122">PRJNA400122</a>). The data contain amino acid sequences of 37,450 predicted protein coding genes.</p>
Simulated and experimental data distributed to the CASP13 participants in protein structure prediction assisted with sparse NMR data
<p>All simulated and experimental data distributed to the CASP participants in protein structure prediction assisted with sparse NMR data in CASP13.</p> <p>Also available at http://predictioncenter.org/casp13/results.cgi?view=targets&model=first&tr_type=others&sub_type=N&groups_id=</p> <p> </p>
The dataset used in the article "A point cloud graph neural network for protein-ligand binding site prediction"
Open the record for dataset details and reuse information.
Supplementary Data associated with the article "LukProt: A database of eukaryotic predicted proteins designed for investigations of animal origins"
<p>Supplementary Data associated with the article "LukProt: A database of eukaryotic predicted proteins designed for investigations of animal origins" - BUSCO example.</p> <p> </p> <p>National Science Centre of Poland is acknowledged for funding of the project 2020/36/C/NZ8/00081, "The role of glycosylation in the emergence of animal multicellularity", which enabled the creation of this dataset.</p> <p> </p> <p> </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)
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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.