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295 results for “Structure prediction”
Computational model results for "Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure"
<p>The mean GIA signals of RSL, u-dot and g-dot with 1σ, 2σ and 3σ uncertainties in North America. </p>
Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules
<p>The content of this repository accompanies the publication "Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules" and contains the complete dataset produced for the fentanyl CSP. </p> <p>Three types of datasets are present : Generation, ML-Reranker and GRACE. <br>Generation refers to all the molecular crystal structures that GRACE has generated using the tailor made force field. It's the starting pool of the reranking exercise, and it contains all the structures that will be selected by the reranking processes.<br>ML-Reranker refers to the data generated by the algorithm proposed in our manuscript. The configurations and energies are obtained by selecting from structures from the generation pool and relaxing their coordinates.<br>GRACE dataset contains the configurations which a user obtains at the end of a standard GRACE reranking procedure. Since GRACE follows differet convergence and minimization critera, the structures obtained in this dataset can differ (non-substantially) from the equivalents found in the ML-Reranker. </p> <p>*.data : contains the indices, energy of the crystal structure (kcal/mol) and, in case of the ml-reranker dataset, the indices mapping the obtained landscape to their generating pool.</p> <p>*.xyz : contains ASE formatted, extended-xyz list of structures corresponding to each exercise.</p> <p>Authors:</p> <p>Andrea Anelli, Hanno Dietrich, Philipp Ectors, Frank Stowasser, Tristan Bereau, Marcus Neumann, Joost van den Ende</p>
Supplementary Data for: Benchmark of density functional theory in the prediction of chemical shielding anisotropies for anisotropic NMR based structural elucidation
<p>Additional Data for the research paper titled: Benchmark of density functional theory in the prediction of chemical shielding anisotropies for anisotropic NMR based structural elucidation.</p> <p>Anisotropy Benchmark for Carbon NS372:</p> <ul> <li>Chemical Shielding Tensor for the molecules in the NS372 test set for carbon (XLSX)</li> <li>Coordinate files for the molcules of the NS372 test set that contained carbon (in NS372-Carbon-COORD-Files.zip)</li> </ul> <p>DFT Benchmark for RCSA for Natural Products:</p> <ul> <li>Chemical Shielding Tensor used for the RCSA analysis of 6 Natural Products (CSV)</li> <li>Turbomole Input and Ouput files for the DFT calculation of the natural products (in RAW_DATA_for_RCSA_Analysis.zip)</li> <li>ConArch+ Input and Ouput files for the RCSA analysis using (in RAW_DATA_for_RCSA_Analysis.zip)</li> <li>Coordinate files used for the RCSA analysis (in RAW_DATA_for_RCSA_Analysis.zip)</li> </ul> <p> </p>
Fig. 1 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 1. The Iguatemi River basin, showing the sampling sites in the Jogui and Iguatemi rivers.
Antibody structure prediction method benchmark results
<p>Antibody Fv structures generated for benchmarking recent antibody structure prediction methods in "Antibody structure prediction using interpretable deep learning." All successfully predicted targets are provided for each method, although only those where every method succeeded are included in accuracy metrics. Each DeepAb prediction includes 50 generated decoys from which the final structure was selected.</p>
Landscape structure, predictability of forest regeneration trajectories, and recovery rate on secondary forests
<p>Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in non- linear ways. This dataset is the synthesis outcome of 22 independent databases from studies of woody plant species recovery as part of the research project entitled "Impacts of landscape structure on secondary tropical forest regeneration". This work aimed to understand the effect of landscape-level disturbance on forest regeneration, specifically through the predictability of trajectories and the recovery rate of these forests.</p> <p>Using a multiscale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density, and basal area are less predictable in landscapes (4-km radius) with intermediate (40-60%) forest cover than in landscapes with high (>60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation in key ecological factors for forest recovery (e.g. seed dispersal, seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only the species richness is positively related to forest cover in relatively small (1-km radius) landscapes. These findings highlight the importance of using a spatially-explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1-4 km radius. </p>
Dataset for "Does fluid structure encode predictions of glassy dynamics?"
<p>This folder contains data in support of "Does fluid structure encode predictions of glassy dynamics?", T. M. Obadiya and D. M. Sussman, arXiv preprint arXiv:2211.00604, (2022). It has three subfolders (described below); all data has been stored in Python's pickle format using protocol version 4, with the pickled files structured as dictionaries.</p> <p>## "Trajectories/" folder</p> <p>Each file in this folder is the raw saved output of a molecular dynamics simulation of an 80:20 Kob-Andersen mixture. To generate the trajectories of particles in this mixture, we first equilibrated a system with random initial conditions for 5000 tau at a temperature of T=0.45. We used the final configuration of this as an initial seed for our other simulations: a snapshot was loaded as the initial configuration for our other simulations, each of which was allowed to equilibrate for 1000 tau at its target temperature. The simulations were done in an NVT ensemble with the coupling constant of the thermostat set to 10 tau. The simulations were evolved using a timestep of dt = 0.001 tau, with frames saved every 1 tau.</p> <p>Each file name contains the temperature at which the simulation was run, and all simulations are of a set of N=4096 particles. The pickle dictionary for each file contains three elements.<br> "Box_size" contains an array of three elements describing the cubic box simulation box's size along the x, y, and z axes.<br> "Particle types" has a list of 4096 integers with 0 corresponding to a particle of type A and 1 corresponding to a particle of type B.<br> "Positions" contains the particle positions for every frame in the saved trajectory.</p> <p>## "Training_data_V1/" folder</p> <p>This folder contains the training data for five training temperatures used in the above paper. The paper considered two different training sets (defined by using either phop or cumulative squared displacement as the dynamical label), and the training sets corresponding to this choice of dynamical label are in the corresponding subfolders.</p> <p>Within each subfolder, the file name indicates the temperature at which the data were obtained. The pickle dictionary for each file contains two elements.<br> "X_train_rawdata" for every particle in the training set, this contains a list of 100 local structural features (corresponding to the AA and AB local radial distribution functions described in the paper above -- all particles in the training set are particles of type A); the "rawdata" part of the name indicates that these features have not been standardized.<br> "Y_train" is the corresponding dynamical label, with 1 indicating a large value of dynamical label and a -1 indicating a small value of the dynamical label.</p> <p>## "Training_data_V2/" folder</p> <p>This folder contains similar training data for the same five training temperatures, but does not make any assumptions about how to define the local structural features. The pickle dictionaries have the same names as in the Training_data_V1, and the "Y_train" element has the same structure.</p> <p>Here, though, "X_train_rawdata" for each element contains a list of 4-vectors that correspond to the positions of all particles within 5 sigma of the particle whose dynamical label is being considered. The first 3 elements of each 4-vector give the relative separation between the target particle and its neighboring particle, and the 4th element is the particle type of the neighboring particle (0 for particles of type A, and 1 for particles of type B).<br> </p>
Datasets for benchmarking RNA 2D structure prediction algorithms.
<p>Datasets for benchmarking ML approaches in RNA 2D structure prediction task.</p>
Trypanosoma brucei predicted protein structures, part 2 of 2
<p>AlphaFold2-predicted protein structures for the <em>Trypanosoma brucei</em> (TREU927) proteome, predicted using input multiple sequence alignments optimised for the Discoba lineage in which <em>T. brucei </em>sits. The structure prediction methodology was exactly as described in <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p> <p>This deposition contains part 2 of 2. To get the full dataset, also download TbruceiTREU927_part1.zip from <a href="https://zenodo.org/record/7940748">doi:10.5281/zenodo.7940748</a>.</p> <p>Data are organised with one directory per <em>T. brucei </em>TREU927 gene ID (eg. Tb927.1.3600). Within each directory you will find:</p> <p><strong><gene id>_predmap.png</strong> A left to right representation of the linear protein sequence with one pixel per amino acid. Each horizontal bar represents one structure prediction, colour coded by pLDDT. For small proteins, there will likely be one prediction of the full-length protein. For large proteins, there may be many overlapping predictions.</p> <p><strong><gene id>_<start aa>-<end_aa> </strong>A directory containing structure prediction of that gene ID between the start and end amino acid. Within this directory you will find:</p> <p><strong><gene id>_<start aa>-<end_aa>.json</strong> The full data in a JSON format, including linear protein sequence and metadata, along with 5 predicted protein structures ranked from best to worst overall pAE. For each predicted protein structure, the structure (PDB format), its pLDDT per residue and pairwise pAE.</p> <p><strong><gene id>_<start aa>-<end_aa>_1.pdb</strong> The PDB file of the highest ranked structure.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-pae.png</strong> A plot of pAE, for the highest ranked structure, at one pixel per amino acid. Shade of green represents pAE for that amino acid pair, see below.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-plddt.png</strong> A plot of pLDDT, for the highest ranked structure, at one horizontal pixel per amino acid. Bar height and colour both represent pLDDT for that amino acid, see below.</p> <p>PDB structure and pAE/pLDDT of lower ranked models are embedded in the JSON file.</p> <p>All pLDDT and pAE plots use the colour scales used by https://alphafold.ebi.ac.uk/: For pLDDT: > 90 (dark blue), 90 > pLDDT > 70 (light blue), 70 > pLDDT > 50 (orange), < 50 (yellow) discontinuous. For pAE: 0 (white angstrom) to dark green (32 angstrom) continuous.</p> <p>If you use this resource, please cite this Zenodo deposition and <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p>
Trypanosoma brucei predicted protein structures, part 1 of 2
<p>AlphaFold2-predicted protein structures for the <em>Trypanosoma brucei</em> (TREU927) proteome, predicted using input multiple sequence alignments optimised for the Discoba lineage in which <em>T. brucei </em>sits. The structure prediction methodology was exactly as described in <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p> <p>This deposition contains part 1 of 2. To get the full dataset, also download TbruceiTREU927_part2.zip from <a href="http://zenodo.org/record/7948119">10.5281/zenodo.7948119</a>.</p> <p>Data are organised with one directory per <em>T. brucei </em>TREU927 gene ID (eg. Tb927.1.3600). Within each directory you will find:</p> <p><strong><gene id>_predmap.png</strong> A left to right representation of the linear protein sequence with one pixel per amino acid. Each horizontal bar represents one structure prediction, colour coded by pLDDT. For small proteins, there will likely be one prediction of the full-length protein. For large proteins, there may be many overlapping predictions.</p> <p><strong><gene id>_<start aa>-<end_aa> </strong>A directory containing structure prediction of that gene ID between the start and end amino acid. Within this directory you will find:</p> <p><strong><gene id>_<start aa>-<end_aa>.json</strong> The full data in a JSON format, including linear protein sequence and metadata, along with 5 predicted protein structures ranked from best to worst overall pAE. For each predicted protein structure, the structure (PDB format), its pLDDT per residue and pairwise pAE.</p> <p><strong><gene id>_<start aa>-<end_aa>_1.pdb</strong> The PDB file of the highest ranked structure.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-pae.png</strong> A plot of pAE, for the highest ranked structure, at one pixel per amino acid. Shade of green represents pAE for that amino acid pair, see below.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-plddt.png</strong> A plot of pLDDT, for the highest ranked structure, at one horizontal pixel per amino acid. Bar height and colour both represent pLDDT for that amino acid, see below.</p> <p>PDB structure and pAE/pLDDT of lower ranked models are embedded in the JSON file.</p> <p>All pLDDT and pAE plots use the colour scales used by https://alphafold.ebi.ac.uk/: For pLDDT: > 90 (dark blue), 90 > pLDDT > 70 (light blue), 70 > pLDDT > 50 (orange), < 50 (yellow) discontinuous. For pAE: 0 (white angstrom) to dark green (32 angstrom) continuous.</p> <p>If you use this resource, please cite this Zenodo deposition and <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p>
A structured evaluation of regression models for predicting CO2 concentration from plasma emission spectra, dataset
<p>Dataset for publication: <a href="https://doi.org/10.1016/j.sab.2022.106467">https://doi.org/10.1016/j.sab.2022.106467</a>.</p> <p>The recorded spectra are stored as comma separated values, the set includes a meta data-file (.mat-file), and a column descriptions (columns.pdf).</p>
Dataset for: Exploring Battery Cathode Materials in the Li-Ni-O Phase Diagrams using Structure Prediction
<p>The Li-Ni-O phase diagram contains several electrochemically active ternary phases. Many compositions and structures in this phase space can easily be altered by (electro-)chemical processes, yielding many more (meta-)stable structures with interesting properties. In this study, we use<em> ab initio </em>random structure searching (AIRSS) to accelerate materials discovery of the Li-Ni-O phase space. We demonstrate that AIRSS can efficiently explore structures (e.g. LiNiO<sub>2</sub>) displaying dynamic Jahn-Teller effects. A thermodynamically stable Li<sub>2</sub>Ni<sub>2</sub>O<sub>3</sub> phase which reduces the thermodynamic stability window of LiNiO<sub>2</sub> was discovered. AIRSS also encountered many dynamically stable structures close to the convex hull. Therefore, we confirm the presence of metastable Li-Ni-O phases by revealing their structures and properties. This work will allow Li-Ni-O phases to be more easily identified in future experiments and help to combat the challenges in synthesizing Li-Ni-O phases.</p> <p>This dataset contains the raw research data and key analysis files for "Exploring Battery Cathode Materials in the Li-Ni-O Phase Diagrams using Structure Prediction". </p> <ul> <li>`known_phases_aiida_data.zip` and `new_phases_exports.aiida.zip` contain the archives exported from the <a href="https://www.aiida.net">AiiDA framework</a> which was used to perform parts of the DFT calculations for this project. </li> <li>`search_data.zip` contains search seed files and the structures generated by the searches.</li> <li>`data_analysis.zip` contains the data and notebooks to reproduce the figures and tables shown in the manuscript.</li> </ul>
Alphafold predicted structures of VPS13 proteins from model organisms
<p>This upload contains AlphaFold-predicted structures of VPS13 proteins from a variety of organisms. Given the large size of these proteins, only partial sequences were predicted with AlphaFold(1) and the resulting structures were aligned in PyMOL(2). A summary of the structures uploaded here is presented as a collection of domain cartoons in the "VPS13 domain organization across eukaryotic evolution.pdf" file. </p> <p>The structures were generated with AlphaFold v2.029 on the Yale High Performance Cluster. Each *.zip file contains the best ranked predictions (out of five) for each sequence (*.pdb files) and the PyMOL assembled full structure (*.pse file). In a few cases, where a good alignment was not possible due to long disordered regions in the C-terminal portions (mostly in proteins from <em>D. discoideum</em> and <em>A. thaliana</em>), the full structures were aligned manually in PyMOL based on the continuity of the lipid transfer groove. The structures in PyMOL can be colour-coded by the confidence value of AlphaFold predictions using the following prompt:</p> <p>set_color n0, [0.051, 0.341, 0.827]<br> set_color n1, [0.416, 0.796, 0.945]<br> set_color n2, [0.996, 0.851, 0.212]<br> set_color n3, [0.992, 0.490, 0.302]<br> color n0, b < 100; color n1, b < 90<br> color n2, b < 70; color n3, b < 50</p> <p>Considering that full length structures were assembled by aligning different protein fragments and in view of the presence of flexible loops with low prediction confidence scores, the relative positions of different folded domains are not necessarily correct.</p> <p> </p> <p><strong>References</strong></p> <p>1. J. Jumper, <em>et al.</em>, Highly accurate protein structure prediction with AlphaFold. <em>Nature</em> 596, 583–589 (2021).</p> <p>2. The PyMOL Molecular Graphics System, Version 2.0. Schrödinger LLC.</p>
Petascale Homology Search for Structure Prediction - MSAs
<p>Multiple sequence alignments (MSAs) of CASP15 targets used in "Petascale Homology Search for Structure Prediction" publication. </p> <p>Each MSA is constructed from a combination of different sequence databases, using different search tools, 1) ColabFoldDB (CFDB) using ColabFold search module, 2) Sequence Read Archive (SRA) using MMseqs2, and 3) UniRef30+BFD using HHblits (HH).</p> <ul> <li><strong>Queries</strong>: Regular CASP15 TS targets, excluding TBM-easy</li> <li><strong>MSAs (A3M) </strong> <ul> <li>CFDB (cfdb.tar.gz)</li> <li>SRA + CFDB (sra_cfdb.tar.gz)</li> <li>HH + CFDB (hh_cfdb.tar.gz)</li> <li>HH + SRA + CFDB (hh_sra_cfdb.tar.gz)</li> </ul> </li> </ul>
Dataset for Peptide binder design with inverse folding and protein structure prediction
<p>Dataset for a paper on peptide design</p> <p> </p> <p><br> mutated_peptides - results for randomly intriduced mutations in protein-peptide complexes that can be predicted at 2 Å (Figure 1)<br> pdb_peptide - variation in the number of recycles (1-10) for 96 peptides (Figure 1)<br> minibinder - results for the minibinder set (Figure 2)<br> Pfam - results for the Pfam set (Figures 4+5)<br> protein_mpnn - results on protein_mpnn test set (Figure 6)</p> <p> </p> <p> </p>
Supplementary Data for "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"
<p>Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation".</p><p>Includes:</p><ul><li>All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models.</li><li>Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes.</li><li>Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).</li></ul>
Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
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Landscape structure, predictability of forest regeneration trajectories, and recovery rate on secondary forests
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Data from: Chemical structure predicts the effect of plant-derived low-molecular weight compounds on soil microbiome structure and pathogen suppression
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Data from: Evaluating LiDAR-derived structural metrics for predicting bee assemblages in managed forests
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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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.