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347 results for “structural proteins”
A precise and general FRET-based method for monitoring structural transitions in protein self-organization
<p>Data underlying the figures in the manuscript</p>
A joint embedding of protein sequence and structure enables robust variant effect predictions
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In vitro and in vivo characterization and protein-bound structural elucidation of three microbial choline TMA lyase inhibitors
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Amino acids to proteins – levels of structure
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Comparative structural insights and functional analysis for the distinct unbound states of Human AGO proteins - Molecular dynamics trajectories and analysis scripts
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Sequence data and structural data utilized in the study and analysis of grain protein function prediction.
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Data from: Protein structure determination using metagenome sequence data
Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families and that metagenome sequence data more than triple the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact-based structure matching, and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the Protein Data Bank. This approach provides the representative models for large protein families originally envisioned as the goal of the Protein Structure Initiative at a fraction of the cost.
Emerging variants of SARS-CoV-2 NSP10 highlight strong and functional conservation of its binding to two non-structural protein, NSP14 and NSP16
<p>The data set contains: (1) topology (prmtop), (2) coordinate files to the run the simulation (3) A plumed.dat file (4) COLVAR and (5) HILLS files; (6) Trajectory (xtc) file and (7) corresponding pdb file. </p> <p>The simulations were run as replicates.</p>
Conformation Database for Publication: Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
<p><strong>Conformation database</strong> for 2022 Publication "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction"</p> <ul> <li>DOI of Physica A publication: <a href="https://doi.org/10.1016/j.physa.2022.128395">https://doi.org/10.1016/j.physa.2022.128395</a></li> <li>GitHub source code: <a href="https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction">https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction</a></li> </ul> <p>This conformation database shows the distinct conformations of best-known and next best energies:</p> <p>├── <strong>20merA</strong><br> │ ├── <strong>20merA_E8_set</strong><br> │ ├── <strong>20merA_E9_set</strong><br> │ ├── confs_20merA_E8.txt<br> │ └── confs_20merA_E9.txt<br> ├── <strong>20merB</strong><br> │ ├── <strong>20merB_E10_set</strong><br> │ ├── <strong>20merB_E9_set</strong><br> │ ├── confs_20merB_E10.txt<br> │ └── confs_20merB_E9.txt<br> ├── <strong>24mer</strong><br> │ ├── <strong>24mer_E8_set</strong><br> │ ├── <strong>24mer_E9_set</strong><br> │ ├── confs_24mer_E8.txt<br> │ └── confs_24mer_E9.txt<br> ├── <strong>25mer</strong><br> │ ├── <strong>25mer_E7_set</strong><br> │ ├── <strong>25mer_E8_set</strong><br> │ ├── confs_25mer_E7.txt<br> │ └── confs_25mer_E8.txt<br> ├── <strong>36mer</strong><br> │ ├── <strong>36mer_E13_set</strong><br> │ ├── <strong>36mer_E14_set</strong><br> │ ├── confs_36mer_E13.txt<br> │ └── confs_36mer_E14.txt<br> ├── <strong>48mer</strong><br> │ ├── <strong>48mer_E22_set</strong><br> │ ├── <strong>48mer_E23_set</strong><br> │ ├── confs_48mer_E22.txt<br> │ └── confs_48mer_E23.txt<br> └── <strong>50mer</strong><br> ├── <strong>50mer_E20_set</strong><br> ├── <strong>50mer_E21_set</strong><br> ├── confs_50mer_E20.txt<br> └── confs_50mer_E21.txt</p>
Mapping Synthetic Binding Proteins Epitopes on Diverse Protein Targets by Protein Structure Prediction and Protein-Protein Docking
<p>The predicted 3D structures of 145 SBPs and the 96 models of SBPs in complex with protein targets.</p>
Data for CASP15 performance benchmarking of the state-of-the-art protein structure prediction methods
<p>CASP15 performance benchmarking of the state-of-the-art protein structure prediction methods</p>
Structure Data of protein
<p>structure data generated from alpha fold</p>
Automated protein-protein structure prediction of the T cell receptor-peptide major histocompatibility complex
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Data from: Exploring the universe of protein structures beyond the Protein Data Bank
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Data from: Protein structure determination using metagenome sequence data
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Data from: Knowledge-based prediction of protein backbone conformation using a structural alphabet
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Data from: Sequence co-evolution gives 3D contacts and structures of protein complexes
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Data from: Large scale determination of previously unsolved protein structures using evolutionary information
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Ubiquity and evolution of structural maintenance of chromosomes (SMC) proteins in eukaryotes
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Data from: Tissue-specific changes in apoplastic proteins and cell wall structure during cold acclimation of winter wheat crowns
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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.