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464 results for “model structure”
Lithospheric structure and strength variations in Antarctica from joint modeling of elevation, geoid and seismic data
<p>These models include Moho depth, LAB depth and integrated lithospheric strength based on a 1D approach involving thermal analysis under local isostasy and a rheological method.</p>
Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C
<p>Supplementary example data for the work presented in "<em>Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C</em>". </p> <p><strong>Abstract: </strong></p> <p>Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.</p> <p> </p>
The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling
<p>This record pertains to the integrative model of the desmosome ODP based on data from X-ray crystallography, electron cryo-tomography, immuno-electron microscopy, yeast two-hybrid experiments, co-immunoprecipitation, in vitro overlay, in vivo co-localization assays, in-silico sequence-based predictions of transmembrane and disordered regions, homology modeling, and stereochemistry information. The modeling was performed using Bayesian integrative structure determination via IMP (Integrative Modeling Platform).</p> <p>This record contains a) compressed version of Github folder: input data, scripts for modeling and results including bead models and localization probability density maps,and b) the ensemble of major cluster models for the main and supplementary runs reported in the paper.</p>
Data table 5 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from pull-down experiment using cerebellar cortex lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 2_cortex isogenic mouse IgG control dynabeads.xlsx" </div> <div> </div>
Data table 4 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from a pull-down experiment using cerebellum lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 3_cerebellum isogenic mouse IgG control dynabeads.xlsx<span><br></span></div>
Data table 2 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table containing a list of proteins identified by MS from samples after a pull-down experiment using cerebral cortex lysates with Dyna beads coated with control isogenic mouse IgG. The proteins from the list were considered false positive interactors of ataxin-3 in the cortex.</p>
Data table 1 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table contains LFQ intensity and other raw MS data for proteins identified in fractions 3,4,5 (i), 11, 12, 13, (ii) 18, 19, and 20 (iii) from ki150 and ki21 model brains. These fractions showed enrichment in ATXN3 protein. The fractions are visualized in Figure 4 (<a href="http://doi.org/10.3389/fnmol.2023.1122308" target="_blank" rel="noopener">doi.org/10.3389/fnmol.2023.1122308</a>)</p>
Impact of water models on structure and dynamics of enzyme tunnels
<ul> <li>1-initial_topologies_coordinates.tar.gz <ul> <li>primary input coordinates and parameter-topology files of all initial systems (LinBwt, LinB32, and Linb86 variants of haloalkane dehalogenase) in OPC and TIP3P water models</li> <li>prepared with the tleap module of AMBER18 package</li> <li>parm7 and crd formatted</li> </ul> </li> <li>2-cap_domain_gate_distances.tar.gz <ul> <li>datasets with minimum distance calculation between Asp146 and Leu176</li> <li>calculated by CPPTRAJ module of AMBER 18 for each performed simulation</li> <li>plain text formatted</li> </ul> </li> <li>3-protein_trajectories-linbwt.tar.gz <ul> <li>three replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of LinBwt in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-closed.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-closed.tar.gz <ul> <li> two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>4-basic_analyses.tar.gz <ul> <li>datasets on RMSF, RMSD, RoG, and RDF from the CPPTRAJ module of AMBER 18</li> <li>for selected replicas 3x LinBwt, 2x LinB32-closed, 2x LinB32-open, 2x LinB86-closed and 2x LinB86-open</li> <li>plain text and PDB formatted</li> </ul> </li> <li>5-caver_analyses.tar.gz <ul> <li>results of tunnel analyses for two replicas of open & closed state each for LinB32 & LinB86 in OPC and TIP3P, and three replicas of LinBWT in OPC and TIP3P</li> <li>generated by CAVER 3.0 using "Divide-and-conquer approach" (MethodsX, 10, 2023, 101968)</li> <li>comprising csv and pdb formatted: tunnel_profiles.csv and bottlenecks.csv, stripped_system.10001.pdb, v_origins.pdb</li> <li>For this and following analyses, the names of the trajectories were modified as follows: <ul> <li>linbwt_opc1_2 = md1_opc_linbwt; linbwt_opc2_2 = md2_opc_linbwt; linbwt_opc3_2 = md3_opc_linbwt;</li> <li>linbwt_tip3p1_2 = md1_tip3p_linbwt; linbwt_tip3p2_2 = md2_tip3p_linbwt; linbwt_tip3p3_2 = md3_tip3p_linbwt;</li> <li>linb32-closed_opc1_2 = md1_closed_opc_linb32; linb32-closed_opc2_2 = md2_closed_opc_linb32;</li> <li>linb32-open_opc1_2 = md1_open_opc_linb32; linb32-open_opc2_2 = md2_open_opc_linb32;</li> <li>linb32-closed_tip3p1_2 = md1_closed_tip3p_linb32; linb32-closed_tip3p2_2 = md2_closed_tip3p_linb32;</li> <li>linb32-open_tip3p1_2 = md1_open_tip3p_linb32; linb32-open_tip3p2_2 = md2_open_tip3p_linb32;</li> <li>linb86-closed_opc1_2 = md1_closed_opc_linb86; linb86-closed_opc2_2 = md2_closed_opc_linb86;</li> <li>linb86-open_opc1_2 = md1_open_opc_linb86; linb86-open_opc2_2 = md2_open_opc_linb86;</li> <li>linb86-closed_tip3p1_2 = md1_closed_tip3p_linb86; linb86-closed_tip3p2_2 = md2_closed_tip3p_linb86;</li> <li>linb86-open_tip3p1_2 = md1_open_tip3p_linb86; linb86-open_tip3p2_2 = md2_open_tip3p_linb86.</li> </ul> </li> </ul> </li> <li>6-transport_tools_analyses.tar.gz <ul> <li>results of comparative analyses for all simulations generated in 5-caver_analyses.tar.gz</li> <li>generated by TransportTools 0.9.3</li> <li>comprising csv, pdb, plain text and py formatted: configuration file (config_TT.ini), tunnel_profiles (data folder) for all filtered tunnels and bottlenecks (data folder) for all filtered tunnels, statistics (statistics folder) and visualization (visualization folder)<br> </li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Constant plane shift model: structure analysis of martensitic phases in Ni50Mn27Ga22Fe1 beyond non-modulated building blocks
<p>Difraction data for lattice parameter calculation, Origin files</p>
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models
<p>This repository contains data and models used in the following paper.</p> <p>Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. <a href="https://www.nature.com/articles/s42256-024-00809-7">Generative AI for designing and validating easily synthesizable and structurally novel antibiotics</a>. <em>Nature Machine Intelligence, </em>2024.</p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/SyntheMol">SyntheMol</a> code. More details about how to use the data and models with the code are available <a href="https://github.com/swansonk14/SyntheMol/tree/main/docs">here</a>.</p> <p>The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1).</p> <p>Data</p> <p> 1_training_data: The <em>Acinetobacter baumannii</em> inhibition data used to train antibiotic property prediction models.</p> <p> 2_chembl: Known antibiotic and antibacterial molecules from <a href="https://www.ebi.ac.uk/chembl/">ChEMBL</a>, which are used to compute the novelty of generated antibiotic candidates.</p> <p> 4_real_space: Data files and statistics for the <a href="https://enamine.net/compound-collections/real-compounds/real-space-navigator">Enamine REAL Space</a>. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022).</p> <p> 5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP.</p> <p> 6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine.</p> <p>The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.</p>
Structural models of overlapping dinucleosome for SAXS-based ensemble modeling
<p>Around transcription start sites, chromatin remodelers form overlapping dinucleosomes (OLDNs). OLDNs contain two structural units, octasome and hexasome. In the octasome, the same histone octamer as a canonical nucleosome is wrapped by DNA. In the hexasome, a histone hexamer that lacks an H2A-H2B dimer is wrapped by DNA. Changes in positional relationship between structural units of a biomolecular complex resulting from molecular motion or fluctuation often have functional significance. To understand fluctuation of OLDNs, we conducted SAXS-based ensemble modeling. Here structural models of OLDNs for the ensemble modeling are uploaded. The structures were obtained by performing coarse-grained molecular dynamics simulations followed by reverse-mapping of the coarse-grained models to atomistic models.</p>
Benchmark Dataset for Structure Refinement Methods of Protein Complex Models
<p>This is the dataset used in our work entitled " Benchmarking of Structure Refinement Methods for Protein Complex Model " by Jacob Verburgt and Daisuke Kihara, which is under review.</p> <p>ZDOCK Derived Benchmark Dataset:</p> <p>The primary benchmark set used in was directly derived from the <a href="https://zlab.umassmed.edu/benchmark/">ZDOCK Benchmark set</a>. The ZDOCK set contains four structures per target: An unbound ligand, an unbound receptor, a bound ligand, and a bound receptor. The benchmark set is available in such a way where the coordinates of the bound subunits are oriented identical to their complex structure, and the unbound subunits are superimposed onto their respective bound subunits. Our dataset creates the optimially oriented "unbound" complexes by combining the superimposed and unbound subunits, along with removal of waters, ligands, and other non-protein atoms. These unbound complexes are saved in the dataset in the form "XXXX_c_u.pdb", where XXXX is the PDB ID.</p> <p>From the complete ZDOCK Benchmark of 230 targets, 18 targets were removed due to containing multiple ligand chains, which is incompatible with the standard ligand to receptor model used within CAPRI. The ZDOCK PDB ID’s of these targets are 1AKJ", "1BJ1", "1DE4", "1EER", "1EXB", "1EZU", "1GP2", "1I9R", "1JMO", "1K74", "1N2C", "1QFW", "2HMI", "3EO1", "3HMX", "4FQI", "4GXU", and "9QFW".</p> <p>There are an additional 8 targets where the superimpostion of the ligand and receptor structures onto the complex led to entanglement of the chains and were subsequently removed from the dataset. The ZDOCK PDB IDs for these targets are "1BGX", "1H1V", "1IRA", "1R8S", "1Y64", "2OT3", "3AAD", "4GAM".</p> <p>Note:</p> <p>In the work, we also used CAPRI scoring model dataset derived from CAPRI rounds 38-45. This dataset is unable to be distributed directly by us due to CAPRI guidelines, but can be derived from "Scoring round" models from the <a href="https://www.ebi.ac.uk/pdbe/complex-pred/capri/">CAPRI Website </a>.</p> <p>The targets considered were T122-T125, T131-T133, and T136, as these were targets which contained globular protein ligands and receptors. Please contact us directly if you have any further questions on this dataset.</p>
Supplemental material to 'A variational rigid-block modelling approach to nonlinear elastic and kinematic analysis of failure mechanisms in historic masonry structures subjected to lateral actions'
<p>This repository contains the data necessary to reproduce the content of the article:</p> <blockquote> <p>A variational rigid-block modelling approach to nonlinear elastic and kinematic analysis of failure mechanisms in historic masonry structures subjected to lateral actions (2021). Earthquake Engineering & Structural Dynamics, 1–23. <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/eqe.3512">https://doi.org/10.1002/eqe.3512</a></p> </blockquote> <p>The file <strong>01_Dataset.zip</strong> contains the dataset. The companion document <strong>00_Dataset_description.pdf </strong>describes the content of the dataset, guiding the analyst to its use in order to (i) reproduce the article's results and (ii) compare the article's results to new results brought by the analyst, e.g. by comparison with other numerical models.</p> <p>Version history</p> <p>v2: updated references in 00_dataset description.pdf </p>
Supplementary material to 'New Equivalent Frame Models for Historical Masonry Structures'
<p>This repository provides the OpenSEES models used for producing the results presented in the paper as well as the data with results of the IDAs.</p>
An Empirical Study of Textual and Structural Statistical Models for Maven-Based Build Systems
<p>Replication package for the manuscript entitled "An Empirical Study of Textual and Structural Statistical Models for Maven-Based Build Systems". See README.md file for details about how to use the package.</p>
Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset
<p>This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.</p>
Computationally modelled structure of type IV pilus PilA of Aggregatibacter actinomycetemcomitans
<p>Some bacterial type IV pili structures have been determined with Cryo-EM, X-ray diffraction or similar method. The type IVa pilus PilA of <em>Aggregatibacter actinomycetemcomitans</em>, an oral pathogen, shares sequence identity with other bacterial type IVa pili. To understand the structure of AaPilA at the molecular level, we performed computational modeling studies. To model the filament, we utilized the GalaxyGemini web server using different monomer conformations from molecular dynamics simulations as a seed; then, we extended the structure to 14-mer by employing the cryo-EM map file of type IV pilus from enterohemorrhagic <em>E. coli</em>. The structures were predicted for a full-length monomer PilA_D7S, N-terminally truncated monomer PilA_D7S_Δ1-27 and 14-homo-oligomer PilA_D7S_14mer. PilAD7S_Δ1-27 and PilAD7S share the same secondary structural elements: an N-terminal α-helix, four antiparallel β-strands, a hypervariable segment and a flexible C-terminus. The homo-oligomeric PilAD7S from our computational model resembles the interface seen in the cryo-EM structure of <em>N. meningitidis</em> type IV pili.</p>
Molecular modeling of self-assembling peptides MELD structures
<p>Top 10 MELD structures for each system used in the "Molecular modeling of self-assembling peptides" paper.</p>
Molecular Models obtained in the paper "In situ architecture of the ER-mitochondria encounter structure"
<p>The zip file contains prototypical NAMD input files to carry out the molecular dynamics flexible fitting simulations performed in the paper, and the initial and final structures (best models) obtained using the MDFF protocol.</p>
Modelling knowledge organization systems and structures
<p>In the last few decades, knowledge organization systems (KOS), especially thesauri,<br> classification schemes and lists of subject headings, have largely followed or conformed<br> with the established data models defined by standards, recommendations or best practices.<br> This long list contains some widely used models, such as ISO5964 Part 1, ISO2788, Z39.19,<br> BS 5723 and BS 6723, (Dextre Clarke, 2008) IFLA Principles Underlying Subject Heading<br> Languages (SHLs), and MARC 21 Format for Classification Data.<br> The FRSAD (Functional Requirements for Subject Authority Data) conceptual model is<br> the third member of the FRBR family, developed under the auspices of IFLA. The report<br> was approved in 2010 and will be published in 2011. FRSAD is a general conceptual model<br> that focuses on the subject relationship and therefore provides a theoretical framework for<br> all KOS and their data models. In addition, it also assists in the assessment of the potential<br> for international sharing and (re)use of subject authority data both within the library sector<br> and beyond.<br> In this paper FRSAD is compared to SKOS and SKOS XL as data models (with implementa-<br> tion examples).</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.