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295 results for “Structure prediction”
Structure prediction of linear and cyclic peptides using CABS-flex
<p>PDB models from: Structure prediction of linear and cyclic peptides using CABS-flex.</p>
Structure based prediction of selective MraY inhibitors
<p>Molecular Docking and Molecular Dynamic Simulation data </p>
Predicting pore structure and oil-bearing characteristics in saline-alkaline lacustrine shale strata via transfer learning
<p>玛湖凹陷凤城组样品的原始数据和吉木萨凹陷芦草沟组样品的原始数据</p>
Predicting pore structure and oil-bearing characteristics in saline-alkaline lacustrine shale strata via transfer learning
<p>玛湖凹陷凤城组和吉木萨尔凹陷芦草沟组样本的原始数据,以及核心 Python 代码。</p>
Data to accompany Structural prediction of chimeric immunogens to elicit targeted antibodies against betacoronaviruses
<p>This dataset contains predicted chimera structures, NanoDSF data on thermal stability of expressed chimeras on the surface of pseudoviruses, and immunofluorescence data on expressed chimeras on the surface of pseudoviruses as described in the manuscript.</p>
Data from: Ribosomal DNA sequence heterogeneity reflects intra-species phylogenies and predicts genome structure in two contrasting yeast species
The ribosomal RNA encapsulates a wealth of evolutionary information, including genetic variation that can be used to discriminate between organisms at a wide range of taxonomic levels. For example, the prokaryotic 16S rDNA sequence is very widely used both in phylogenetic studies and as a marker in metagenomic surveys and the ITS region, frequently used in plant phylogenetics, is now recognised as a fungal DNA barcode. However, this widespread use does not escape criticism, principally due to issues such as difficulties in classification of paralogous versus orthologous rDNA units and intragenomic variation, both of which may be significant barriers to accurate phylogenetic inference. We recently analysed datasets from the Saccharomyces Genome Resequencing Project, characterising rDNA sequence variation within multiple strains of the baker's yeast <i>Saccharomyces cerevisiae</i> and its nearest wild relative <i>Saccharomyces paradoxus</i> in unprecedented detail. Notably, both species possess single locus rDNA systems. Here, we use these new variation datasets to assess whether a more detailed characterisation of the rDNA locus can alleviate the second of these phylogenetic issues, sequence heterogeneity, while controlling for the first. We demonstrate that a strong phylogenetic signal exists within both datasets and illustrate how they can be used, with existing methodology, to estimate intra-species phylogenies of yeast strains consistent with those derived from whole-genome approaches. We also describe the use of partial Single Nucleotide Polymorphisms, a type of sequence variation found only in repetitive genomic regions, in identifying key evolutionary features such as genome hybridisation events and show their consistency with whole-genome Structure analyses. We conclude that our approach can transform rDNA sequence heterogeneity from a problem to a useful source of evolutionary information, enabling the estimation of highly accurate phylogenies of closely related organisms, and discuss how it could be extended to future studies of multi-locus rDNA systems.
human VPS13C structure predicted by AlphaFold
<p>Full-length human VPS13C isoform 1 comprises 3753 amino acids (a.a.). is predicted with AlphaFold2. Such structure is represented by a 29.3 nm long rod whose backbone is a narrow twisted β-sheet running along its entire length. The β-sheet forms the floor of a hydrophobic groove that extends throughout the rod and thus could mediate the sliding of lipids from one end to the other end of the protein.</p>
A joint embedding of protein sequence and structure enables robust variant effect predictions
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3dRNA/DNA: 3D Structure Prediction from RNA to DNA
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AF2 Predictions of OipA Structure
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AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 1 (1-50).</p> <div> <p>Check related biorxiv preprint: AlphaFold2 knows some protein folding principles; DOI: https://doi.org/10.1101/2024.08.25.609581.</p> <p> </p> </div>
Sequence data and structural data utilized in the study and analysis of grain protein function prediction.
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Data for "Proteome-wide prediction of mode of inheritance and molecular mechanism underlying genetic diseases using structural interactomics"
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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>
Molecular Dynamics Simulations of HADDOCK-predicted Complex Structures of apoE2 and Factor H
<p>Input and output data for the molecular dynamics simulations of the FH5–7/ApoE2 complex. Initial structures generated with the HADDOCK v2.4 web server with 3 nm distance restraints for lysine pairs that were experimentally found to be cross-linked with DSS. </p> <p>Five clusters and the four representative structures provided by HADDOCK were then used for atomistic molecular dynamics simulations. These structures were solvated and simulated with both CHARMM36m and Amber FF14SB force fields for 250 ns each using GROMACS 2021. The recommended simulation parameters were used for both force fields, and they are available in the mdp files. </p> <p>For each of these 5 (clusters) x 4 (structures per cluster) x 2 (force fields) = 40 simulations, the outputs and inputs are provided; the trajectory (xtc), energy file (edr), final structure (gro), run parameter file (tpr), and continue point (cpt) are system-specific, whereas a single topology (top) and index file (ndx) is shared among all simulations with the same force field. The molecule definitions (itp) referred to in the topology are provided in the compressed files.</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>
Predictive Value of Sleep Apnea-specific PTT Response for Incident Subclinical Abnormalities in LV Structure and Function in Cohort of moderate-to Severe OSA
ClinicalTrials.gov study NCT06626906. IPD Sharing: NO. Countries: 0. Publications: 12.
"Predicting Glaucoma Progression With Optical Coherence Tomography Structural and Angiographic Parameters".
ClinicalTrials.gov study NCT04646122. IPD Sharing: Not stated. Countries: 0. Publications: 2.
Automated protein-protein structure prediction of the T cell receptor-peptide major histocompatibility complex
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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.