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Dataset results
295 results for “Structure prediction”
Alterations in serum microRNA in humans with alcohol use disorders impact cell proliferation and cell death pathways and predict structural and functional changes in brain
GEO Series GSE71579. Homo sapiens; Mus musculus; Rattus norvegicus; synthetic construct. 133 samples. Type: Non-coding RNA profiling by high throughput sequencing; Non-coding RNA profiling by array.
Tumoral and stromal hMENA isoforms influence tertiary lymphoid structure localization and predict response to immunocheckpoint blockade in lung cancer patients
GEO Series GSE217451. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
The combination of XAGE-1 expression and tertiary lymphoid structures predicts prognosis in cutaneous angiosarcoma (Targeted RNA-Seq)
GEO Series GSE203215. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Data from: Near infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
1. The allocation of non-structural carbohydrates (NSCs) to reserves constitutes an important physiological mechanism associated with tree growth and survival. However, procedures for measuring NSC in plant tissue are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a high-throughput technology that has the potential to infer the concentration of organic constituents for a large number of samples in a rapid and inexpensive way based on empirical calibrations with chemical analysis. 2. The main objectives of this study were (i) to develop a general NSC concentration calibration that integrates various forms of variation such as tree species and tissue types and (ii) to identify characteristic spectral regions associated with NSC molecules. In total, 180 samples from different tree organs (root, stem, branch, leaf) belonging to 73 tree species from tropical and temperate biomes were analysed. Statistical relationships between NSC concentration and NIRS spectra were assessed using partial least squares regression (PLSR) and a variable selection procedure (competitive adaptive reweighted sampling, CARS), in order to identify key wavelengths. 3. Parsimonious and accurate calibration models were obtained for total NSC (r2 of 0·91, RMSE of 1·34% in external validation), followed by starch (r2 = 0·85 and RMSE = 1·20%) and sugars (r2 = 0·82 and RMSE = 1·10%). Key wavelengths coincided among these models and were mainly located in the 1740–1800, 2100–2300 and 2410–2490 nm spectral regions. 4. This study demonstrates the ability of general calibration model to infer NSC concentrations across species and tissue types in a rapid and cost-effective way. The estimation of NSC in plants using NIRS therefore serves as a tool for functional biodiversity research, in particular for the study of the growth–survival trade-off and its implications in response to changing environmental conditions, including growth limitation and mortality.
Data from: Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots
Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human–wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large carnivore species where landscape-scale resource selection data already exist.
Structure prediction of protein-ligand complexes from sequence information with Umol
<p>posebusters_benchmark_set.tar.zst - files for the prediction (features to Umol) and scoring of the pose busters benchmark </p><p>posebusters_pred_native.tar.zst - pdb and sdf files of proteins and ligands. Includes native structures, predicted structures and relaxed predicted structures with plDDT in the B factor column.</p><p>posebusters_scores.csv - contains ligand RMSD and other metrics for the unrelaxed structures predicted with Umol.</p><p>PDBBind_processed.tar.zst - files for the training (features to Umol) using PDBbind version 2020</p><p> </p><p> </p>
DFT-predicted equilibrium structures of Ir(III) complexes for halogen bond-assisted chemo-sensors
<p>Fully relaxed equilibrium structures of <strong>IrF-XB</strong> (without chloride anion coordinated by halogen bonding) as well as <strong>IrF-XBCl</strong> (with chloride anion coordinated by halogen bonding), <strong>IrF-XBBr</strong>, <strong>IrF-XB-Acetate</strong> and <strong>(IrF-XB)2-Acetate</strong> as predicted at the DFT level of theory (B3LYP/def2-SVP) including D3BJ dispersion correction and implicit solvent effects (acetonitrile). All investigated Ir(III)-based molecular sensors were optimized in singlet and triplet multiplicity in order to (subsequently) evaluate the Franck-Condon photophysics as well as the properties of the emissive triplet state. The multiplicity is indicated in the filename.</p>
Effective Molecular Dynamics from Neural-Network Based Structure Prediction Models
<p>Molecular dynamics simulation (61.5 us) data of 28 one- and two-domain proteins from Jussupow & Kaila: Effective Molecular Dynamics from Neural-Network Based Structure Prediction Models</p>
Dataset from: Structure-based prediction of protein-nucleic acid binding using graph neural networks
<p>Datasets used for training/evaluating the neural network models described in our article. Additional documentation related to how these datasets were constructed can be found in the github repository https://github.com/jaredsagendorf/pnabind/tree/master/datasets</p>
AlphaFold-predicted structures of IL-6 dimers
<p>1. Pre-processed files</p> <p>2. Processed files:</p> <ul> <li>CIF files from AF3 were converted to PDB format</li> <li>only C_alpha atoms were kept</li> <li> polyG linkers were deleted and chain IDs for the second part were renamed to "B"</li> <li>chain "A" was aligned to reference structure using the non-swapped amino acids</li> <li> the models were classified into "non-swapped", "swapped" and "other"</li> </ul> <table> <tbody> <tr> <td>#</td> <td>Approach</td> <td>Weights name</td> <td>Templates</td> <td>Dropout</td> <td>N_models (different weights)</td> <td>N_runs (different seeds)</td> </tr> <tr> <td>1</td> <td>AlphaFold 2.3.2 as a monomer with 50×Gly linker</td> <td>monomer_ptm</td> <td>Y</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>2</td> <td> </td> <td>monomer_ptm</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>3</td> <td>ColabFold 1.5.5 as a monomer with 50×Gly linker</td> <td>alphafold2_ptm</td> <td>Y</td> <td>Y</td> <td>5</td> <td>5</td> </tr> <tr> <td>4</td> <td> </td> <td>alphafold2_ptm</td> <td>Y</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>5</td> <td> </td> <td>alphafold2_ptm</td> <td>N</td> <td>Y</td> <td>5</td> <td>5</td> </tr> <tr> <td>6</td> <td> </td> <td>alphafold2_ptm</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>7</td> <td>AlphaFold 2.3.2 as a dimer</td> <td>multimer</td> <td>Y</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>8</td> <td> </td> <td>multimer</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>9</td> <td>ColabFold 1.5.5 as a dimer</td> <td>alphafold2_multimer_v3</td> <td>Y</td> <td>Y</td> <td>5</td> <td>5</td> </tr> <tr> <td>10</td> <td> </td> <td>alphafold2_multimer_v3</td> <td>Y</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>11</td> <td> </td> <td>alphafold2_multimer_v3</td> <td>N</td> <td>Y</td> <td>5</td> <td>5</td> </tr> <tr> <td>12</td> <td> </td> <td>alphafold2_multimer_v3</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> <tr> <td>13</td> <td>AlphaFold3 as a monomer with 50×Gly linker</td> <td>Default (accessed on 24.06.2024)</td> <td> </td> <td> </td> <td>5</td> <td>20</td> </tr> <tr> <td>14</td> <td>AlphaFold3 as a dimer</td> <td>Default (accessed on 24.06.2024)</td> <td> </td> <td> </td> <td>5</td> <td>20</td> </tr> <tr> <td>15</td> <td>SPEACH_AF</td> <td>alphafold2_ptm</td> <td>N</td> <td>N</td> <td>5</td> <td>5</td> </tr> </tbody> </table>
Dataset for Crashworthiness in preliminary design: Mean crushing force prediction for closed-section thin-walled metallic structures
<p>This dataset is the official implementation of the following paper published in the International Journal of Impact Engineering journal:</p> <blockquote> <p>Shreyas Anand, René Alderliesten, Saullo G. P. Castro. "Crashworthiness in preliminary design: Mean crushing force prediction for closed-section thin-walled metallic structures". International Journal of Impact Engineering, 2024. <a href="https://doi.org/10.1016/j.ijimpeng.2024.104946" target="_blank" rel="noopener">10.1016/j.ijimpeng.2024.104946</a> </p> </blockquote> <h2>Highlights</h2> <div> <div> <ul> <li> <div>Evaluation of analytical models to predict mean crushing force</div> </li> <li> <div>Analysis of both extensional and inextensional analytical crushing models.</div> </li> <li>Use of numerical and experimental dataset to improve existing analytical models.</li> <li>Generalized expression for predicting mean crushing force</li> </ul> </div> </div> <h2>Abstract</h2> <div> <div>To design crash structures for disruptive aircraft designs, it is required to have fast and accurate methods that can predict crashworthiness of aircraft structures early in the design phase. Axial crushing is one of the key energy absorbing mechanisms during a crash event. In this study, various analytical models proposed for calculation of mean crushing force for thin-walled tubular structures are compared with a database of numerical and experimental values to ascertain their accuracy. Improvements to some of the models have also been proposed. Finally a generalized model based on the studied and improved analytical models for prediction of mean crushing force for closed section thin-walled tubular structures is introduced. The generalized model demonstrates high accuracy when compared against experimental/numerical dataset as evidenced by a high coefficient of determination (R^2) value of 0.97 and can therefore be used to estimate the mean crushing force for closed-section thin-walled metallic tubular structures with various cross-sectional shapes and crushing modes early in the design phase.</div> </div>
A joint embedding of protein sequence and structure enables robust variant effect predictions
<p>Data related to the GitHub repository KULL-Centre/_2023_Blaabjerg_SSEmb, which is also stored on Zenodo here: <span><span><a href="../doi/10.5281/zenodo.13765792" target="_blank" rel="noopener noreferrer">https://zenodo.org/doi/10.5281/zenodo.13765792</a>.</span></span></p>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 6 (1-83).</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>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 3 (1-60).</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>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 2 (1-60).</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>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 1 (51-91) and 8 (1-5).</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>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 3 (61-120).</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>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 4 (41-55).</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>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 4 (1-40).</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>
AlphaFold_ab_initio iterative structure predictions sub trajs for faster download
<p>PDB ids start from 7 (1-51).</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>
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.