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34 results for “structural calculations”
Docked structures from "Optimizing active learning for free energy calculations"
<p>This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ailsci.2022.100050" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ailsci.2022.100050</span></span></a>). AM1-BCC charges are stored in the field "AM1Cache" in the SD file. The charges can be extracted using the code sample below. </p> <p> </p> <pre><code>from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") am1_charges = pickle.loads(base64.b64decode(mol.GetProp("AM1Cache"))) assert len(am1_charges) == mol.GetNumAtoms(), "Charge cache has different number of charges than mol atoms"</code></pre>
Dataset: Electronic Spectra of Ytterbium Fluoride from Relativistic Electronic Structure Calculations
<p>This is the dataset for the manuscript entitled: "Electronic Spectra of Ytterbium Fluoride from Relativistic Electronic Structure Calculations" by J. V. Pototschnig, K. G. Dyall, L. Visscher and A. S. P. Gomes. It contains output files of various calculations and scripts to process them.</p>
Dyall dz, tz, and qz basis sets for relativistic electronic structure calculations
<p>This archive contains the Dyall basis sets for relativistic atomic and molecular electronic structure calculations. They are given in the format required by the DIRAC program (see <a href="http://diracprogram.org">diracprogram.org</a>), which is essentially a list of the exponents for each angular momentum for each element. The basis sets are of double-, triple-, and quadruple-zeta quality. For each quality, there are three basis set types: valence (v<em>N</em>z), core-valence (cv<em>N</em>z) and all-electron (ae<em>N</em>z). These basis sets include correlating functions for the relevant shells (valence, valence+outer core, all shells). In addition, for each of these basis sets there is another set that contains diffuse functions for the s, p, and d elements, optimized for the anion or extrapolated from neigboring elements where the anion is unbound or weakly bound. These sets are labeled av<em>N</em>z, acv<em>N</em>z, and aae<em>N</em>z. References for the basis sets are included in the basis set files.</p> <p>The archive files containing descriptions and recommendations for each basis set, as well as SCF coefficients and lists of exponents, are available <a href="https://doi.org/10.5281/zenodo.7606546">here</a>.</p>
Rereferenced Chemical Shift files used to select dihedrals for the 5 secondary structure conformations used to calculate Conformational Variability (ConVa).
<p>These Chemical Shifts in this repository were processed with ShiftCrypt (1) and then processed as described in the manuscript. </p> <p> </p> <p>1- Gabriele Orlando, Daniele Raimondi, Luciano Porto Kagami, Wim F Vranken, ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values, <em>Nucleic Acids Research</em>, Volume 48, Issue W1, 02 July 2020, Pages W36–W40, <a href="https://doi.org/10.1093/nar/gkaa391">https://doi.org/10.1093/nar/gkaa391</a></p>
Effect of variable thresholds on calculating linkage disequilibrium and population structure, using Plink 1.9
<p>The figure presented here shows how the number of LD-independent SNPs and the apparent population structure can change drastically, depending on what input thresholds are used for the calculations. The population sample consists of 220 fruit-fly (Drosophila melanogaster). Most of the population structure plots indicate four subpopulations, which on further investigation using Fst indicate that this is caused by defined trans-centromeric regions, without evidence for genotyping error, and probably reflective of historic admixture. In the plots of population structure, the number in each box indicates the number of independent SNPs which were used in the IBD calculatations. Points are coloured by order in which each fly was sequenced, and some error is noticable for the beige points in the top-right plots.</p> <p>The Plink program provides a useful method for selecting single-nucleotide polymorphisms (SNPs) which are independent of linkage disequilibrium (LD), and also of visualising the genetic relatedness between individuals in a population sample, using identity-by-descent analysis (IBD). The selection of LD-independent SNPs requires three user-specfied paramaters, alongside the genotype data: i. Window-size, in kilobases (Kb) within which all pairwise comparisons between SNPs will be made, ii. Step-size, in number of SNPs, iii. r2 threshold between any two SNPs, below which they are considered to be independent (fixed here at 0.5).</p>
Dataset: Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations
<p>This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations", by Xiang Yuan, Luvas Visscher and Andre Severo Pereira Gomes. It also contains the figures used in the manuscript.</p>
Conformation and structural features of diuron and irgarol: insights from quantum chemistry calculations.
<p>A set of conformational relevant structures of Diuron and Irgarol (two biocides) obtained from conformational analyses carried out using Density Functional Theory (DFT). The geometries are given in the mol2 format.</p>
Amorphous Niobium Oxide Structures Calculated from First Principles using Density Functional Theory and Molecular Dynamics
<p>The dataset contains fifteen different amorphous niobium oxide structures. Nine of the structures have the same stoichiometry as Nb2O5. The other six are defect structures containing 1 or 2 oxygen vacancies, or 1 or 2 interstitial oxygens, or 1 Nb vacancy. Each of the structure files is in the VASP POSCAR file format. Each structure was created using ab-initio molecular dynamics at 5000~K to liquidate the structure, then snapshots of the structure were taken every 2 ps, and geometry optimizations were performed on each individual snapshot. The naming convention is relatively simple: 'conf_x_POSCAR' is a stoichiometric POSCAR, and 'conf_x_oadd1_POSCAR' is a defect structure originating from structure 'x' with a single oxygen interstitial. The defect labels correspond to 1 oxygen interstitial (oadd1), 2 oxygen interstitials (oadd2), 1 oxygen vacancy (ovac1), 2 separated oxygen vacancies (ovac2), 2 nearest neighbor oxygen vacancies (ovac2nn), and 1 Nb vacancy (nbvac).</p>
NMR and structure calculation data of the TccC3 toxin from Photorhabdus luminescens
<p>NMR and structure calculation data of the protein TcART (a section of TccC3 from<em> Photorhabdus luminescens</em>) as presented in the publication</p> <p>Belyy, A., Lindemann, F., Roderer, D. <em>et al.</em> Mechanism of threonine ADP-ribosylation of F-actin by a Tc toxin. <em>Nat Commun</em> <strong>13</strong>, 4202 (2022). https://doi.org/10.1038/s41467-022-31836-w</p> <p>and my thesis</p> <p>"A Structural View on Mechanisms of Bacterial Communal Life and Toxicity" submitted to the Free University Berlin (<a href="http://dx.doi.org/10.17169/refubium-41725">http://dx.doi.org/10.17169/refubium-41725</a>).</p>
UltraScan Solution Modeler (US-SOMO) hydrodynamic parameter, structural small angle scattering and SESCA circular dichroism (CD) calculations on AlphaFold predicted structures
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Snapshots, frequency contact maps analysis, Poisson Boltzmann calculations, and data scripts for characterization of structural and energetic differences between conformations of the SARS-CoV-2 spike protein
<p><strong>Molecular dynamics simulation</strong> trajectories, which have been performed using the Amber ff14SB force field running with the Amber18 package at the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Simulation methodology and further details are described in [1] and [2]. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu).</p> <p>The <strong>Poisson Boltzmann </strong>energy calculations have been achieved by using the input_files.tar.xz found here and solving the Poisson Boltzmann equation with pygbe. A more detailed example and tutorial can be found at [4]. For further details contact Horacio V Guzman.</p> <p><strong>The dataset contains </strong></p> <ul> <li><strong>A total of 30 snapshots of the three trajectories (10 snapshots each system = two per replica x 5 replicas/system):</strong></li> </ul> <ol> <li>SARS-CoV-2002 spike protein with three RBD in the down positions: "COV2-DDD/PDB/" .</li> <li>SARS-CoV-2002 spike protein with one RBD in the up and two RBD in the down positions: "COV2-UDD/PDB/".</li> <li>SARS-CoV-2002 spike protein with two RBD in the up and one RBD in the down positions: "COV2-DUU/PDB/".</li> </ol> <ul> <li><strong>Input files for Poisson-Boltzmann analysis</strong>:</li> </ul> <ol> <li>PoissonBoltzmann/input_files.tar.xz</li> </ol> <ul> <li><strong>Data for the frequency contact map and processing scripts</strong>:</li> </ul> <ol> <li>cov2-ddd.pdb, cov2-udd.pdb, cov2-duu.pdb reference PDB files.</li> <li>Contact maps [3] at "COV2-DDD/CONTACT_MAP/", "COV2-UDD/CONTACT_MAP/", "COV2-DUU/CONTACT_MAP/".</li> <li>frequency.lua: get frequency of contacts from a set of contacts map files.</li> <li>diff_frequency.lua: get differential frequency of contacts from a set of frequency files.</li> <li>Frequency of contacts listed in frequency.data files at "COV2-DDD/", "COV2-UDD/" and "COV2-DUU/" directories.</li> </ol> <p>Read the "INFO" files for further informations.</p> <p>This dataset and the code is part of a collaboration between:</p> <ul> <li>The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)</li> <li>Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).</li> <li>Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).</li> <li>School of engineering in bioinformatics, University of Talca, Talca, Chile.</li> </ul> <p>[1] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. (2020). All-atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme (Version 0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3817447</p> <p>[2] Chad W. Hopkins, Scott Le Grand, Ross C. Walker, and Adrian E. Roitberg. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. Journal of Chemical Theory and Computation 2015 11 (4), 1864-1874. http://doi.org/10.1021/ct5010406</p> <p>[3] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. Quantitative determination of mechanical stability in the novel coronavirus spike protein. Nanoscale, 2020,12, 16409-16413. <a href="https://doi.org/10.1039/D0NR03969A">https://doi.org/10.1039/D0NR03969A</a></p> <p>[4] https://github.com/pyF4all</p>
Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures
<p><br /> This work demonstrates how databases of diffusion-related properties can be developed from high-throughput ab initio calculations. The formation and migration energies for vacancies of all adequately stable pure elements in both the face-centered cubic (fcc) and hexagonal close packing (hcp) crystal structures were determined using ab initio calculations. For hcp migration, both the basal plane and z-direction nearest-neighbor vacancy hops were considered. Energy barriers were successfully calculated for 49 elements in the fcc structure and 44 elements in the hcp structure. These data were plotted against various elemental properties in order to discover significant correlations. The calculated data show smooth and continuous trends when plotted against Mendeleev numbers. The vacancy formation energies were plotted against cohesive energies to produce linear trends with regressed slopes of 0.317 and 0.323 for the fcc and hcp structures respectively. This result shows the expected increase in vacancy formation energy with stronger bonding. The slope of approximately 0.3, being well below that predicted by a simple fixed bond strength model, is consistent with a reduction in the vacancy formation energy due to many-body effects and relaxation. Vacancy migration barriers are found to increase nearly linearly with increasing stiffness, consistent with the local expansion required to migrate an atom. A simple semi-empirical expression is created to predict the vacancy migration energy from the lattice constant and bulk modulus for fcc systems, yielding estimates with errors of approximately 30%.</p> <p>Files:</p> <p>figure_excel_files.zip:</p> <p>Excel files for figures in the publication, and excel files of main data tables for FCC and HCP vacancy formation energies and vacancy migration energies.</p> <p>fcc_hvf_hvm.tar.gz and hcp_hvf_hvm.tar.gz:</p> <p>Raw VASP files corresponding to FCC and HCP vacancy formation energies and vacancy migration energies.<br /> <br /> bulk_modulus.tar.gz:</p> <p>Raw VASP files corresponding to FCC bulk modulus calculations.<br /> </p>
Permittivity of AuAg alloys along with calculations and measurements of the alloyed Fano-resonant structures
<p>Here I present the data corresponding to the manuscript "Low temperature annealing method for fabricating alloy nanostructures and metasurfaces: Unlocking a novel degree of freedom" (arXiv:2105.02461). In this work, I have provided a technique of alloying AuAg at low temperature. Here I present the measured ellipsometric data of the AuAg alloys alloyed at low temperatures and measurements of the single structure AuAg alloyed 4-rod Fano-resonance nanostructures. I have also provided the calculations of the Fano-resonant structures using the permittivity of the Rioux model as well as well as the permittivity obtained from the Rioux model. Each .zip file is associated with a Readme file explaining everything in greater details.</p>
Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations
<p>The files contains all the data that are shown in the figures of the article:</p> <p>Rumble, C.; Vauthey, E. Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations. Phys. Chem. Chem. Phys. 21 (2019). 10.1039/C9CP00795D</p>
NEB structures for {332} BCC twining calculations
<p>This is a set of NEB structures for calculations the {332} twinning and TRIP martensitic transformation</p>
Data from: Ab initio grand canonical Monte Carlo calculation of grain boundary composition and structure
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Urban Forest Effects Model (UFORE) to calculate forest structure and function from sample ground data. Two part set.
Within the City of Baltimore, 195 permanent 1/10 circular plots were established based on a stratified random sample among land uses in 1999. These plots were re-measured in 2004 and 2009 and will be re-measured again in 2014. On each plot, all trees (as defined as woody vegetation with a stem diameter at 4.5 ft (dbh) greater than one-inch) are recorded. For each tree, data are recorded on species, dbh, height, crown width, condition, crown competition, percent canopy missing and distance and direction to nearby residential buildings. These data are analyzed using the i-Tree model (www.itreetools.org) to assess ecosystem services and values. However, more importantly, these plots along with a comparable set of plots established in Syracuse, NY in 1999 are the first and most spatially comprehensive set of long-term urban forest monitoring data within cities globally. These data are being used to understand how urban forest structure and associated ecosystem services are changing through time in the City of Baltimore.
Energy and forces annotated atomic structures of platinum using first-principles calculations
<p>Dataset for training machine learning potential of platinum.</p> <p>Detailed explanation of data generation is described in the preprint. below</p> <p>Chun, H., Kang, J., Kang, D., Heo, J., Cho, H., Heo, J., … & Han, B. (2022). Tracking the 3d atomic structures during thermal treatment and catalytic activity of individual pt nanoparticles.. https://doi.org/10.21203/rs.3.rs-1441062/v1</p>
Dataset for deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
<p>Dataset files of atomic structures and Hamiltonian matrices of graphene, MoS<sub>2</sub>, bilayer graphene and bilayer bismuthene.</p> <p>Please note that the DFT results in this dataset were calculated using OpenMX. This means that if you want to use a DeepH model trained on this dataset to calculate properties, you need to use the <a href="https://github.com/mzjb/overlap-only-OpenMX">overlap calculated using OpenMX</a>. The orbital information required for overlap calculations can be found in the <a href="https://www.nature.com/articles/s43588-022-00265-6">paper</a>.</p>
Calculated structures of РТТА-nLi and Cartesian coordinates
<p>The file contains quantum-chemical calculations of the interaction of PTTA oligomers with different numbers of lithium atoms. And also investigated the data of interaction in the presence of two solvents: dimethyl carbonate (DMC) and tetraglyme (G4).</p> <p> </p> <p>The file contains calculated structures and cartesian coordinates of complex PTTA4[Li<sup>+</sup>(DMC)<sub>2</sub>] and products of reductive metallation of PTTA2 oligomer with 8 Li atoms and with 12 Li atoms.</p> <p>Calculated structures of the ion pairs [Li<sup>+</sup>(DMC)<sub>4</sub>] [PF<sub>6</sub><sup>-</sup>] and [Li<sup>+</sup>G4] [PF<sub>6</sub><sup>-</sup>].</p> <p>Calculated structures of complexes PTTA4[Li<sup>+</sup>(DMC)<sub>2</sub>] [PF<sub>6</sub><sup>-</sup>], PTTA4[Li<sup>+</sup>G4] [PF<sub>6</sub><sup>-</sup>] and PTTA4{[Li<sup>+</sup>(DMC)<sub>2</sub>] [PF<sub>6</sub><sup>-</sup>]}<sub>2</sub>.</p> <p>Calculated structures of complexes (PTTA3)<sub>3</sub>[Li<sup>+</sup>(DMC)<sub>2</sub>] [PF<sub>6</sub><sup>-</sup>] and (PTTA3)<sub>3</sub> [Li<sup>+</sup>G4] [PF<sub>6</sub><sup>-</sup>].</p> <p>Calculated specific metalation energies DЕ<sub>n </sub> of PTTA2 oligomer.</p> <p>Calculated structures of the isomers РТТА2(Li)<sub>2</sub> with Li atoms coordinated at (N,N), (O,O), (O,N) and (N,O) chelate knot.</p> <p>Calculated structures of РТТА2(Li)<sub>4</sub>, РТТА2(Li)<sub>6</sub> and dimers [PTTA2(Li<sub>4</sub>)]<sub>2</sub> and [PTTA2(Li<sub>6</sub>)]<sub>2</sub>.</p>
ScienceDex guides
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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.