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464 results for “model structure”
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p>
Structural models of BindCraft designed binders
<p>Structural models of binders designed against various targets described in the BindCraft manuscript.</p>
Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Location Data
<p>This dataset is meant to be used with "Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Results and Data". It provides spatial output data for 4 different setups (spheroid, traditional, 3d gravity, and traditional floating) of an agent-based model of <i>in vitro </i>tuberculosis infection models. </p>
Crustal thicknesses, Moho depths and 3-D density anomaly model for GJI paper: Crustal structure of onshore-offshore Atlantic Canada and environs from constrained 3-D gravity inversion using variable mesh depths by J. Kim Welford
<p>The files are provided as ascii text files in terms of both latitudes/longitudes and eastings/northings. For the 3-D density anomaly model, it is provided with columns of x, y, z, and absolute density. The conversions from latitudes/longitudes to eastings/northings for all of the models and maps in this work are computed with ellipsoid WGS-84 and UTM zone 19 using Generic Mapping Tools.</p>
Application of flow cytometry using advanced chromatin analyses for assessing changes in the sperm structure and DNA integrity in a porcine model
<p><span>Chromatin status is critical for sperm fertility. We tested a multivariate approach for studying pig sperm chromatin, aiming to capture the chromatin structure's complexity with a set of quick and simple techniques, not only DNA damage. Sperm doses from 36 boars (3 ejaculates/boar) were analyzed at days 0 and 11 (cooled storage). Analyses were: CASA (motility) and flow cytometry to assess sperm functionality and chromatin structure by SCSA (DNA fragmentation %DFI and chromatin maturity %HDS), monobromobimane (mBBr, tiol status/disulfide bridges between protamines), chromomycin A3 (CMA3, protamination) and 8-hydroxy-2'-deoxyguanosine (8-oxo-dG, DNA oxidative damage). Data were analyzed by linear models for effects of boar and storage, correlations, and multivariate analysis as hierarchical clustering and principal component analysis (PCA). Storage reduced sperm quality parameters, mainly motility, with no critical oxidative stress increases, while chromatin status worsened slightly (%DFI and 8-oxo-dG increased while mBBr MFI and disulfide bridges decreased). Boar significantly affected most chromatin variables except for CMA3, with storage affecting most except %HDS. At day 0, sperm chromatin variables clustered closely, except for CMA3, and %HDS and 8-oxo-dG correlated with many variables (notably, mBBr). After storage, the relation between %HDS and 8-oxo-dG remained, but correlations among other techniques disappeared, and mBBr variables clustered separately. The PCA suggested a considerable influence of mBBr on sample variance, especially regarding storage, with SCSA and 8-oxo-dG affecting between-sample variability. Overall, CMA3 was the least informative, in contrast with results in other species. The combination of DNA fragmentation, DNA oxidation, chromatin compaction, and tiol status seems a good candidate for obtaining a complete picture of the pig sperm nucleus status, raising many questions for future molecular studies and deserving further research to establish its usefulness as fertility predictors in multivariate models. The meaning of CMA3 should be clarified.</span></p>
Data and code for the article "Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models"
<p>This is the repository for the data and code to reproduce the article "Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models".</p> <p>Summary:</p> <p><span><span>·</span></span><span><span><span> </span><em>Background and Aims</em></span></span></p> <p><span><span>Lidar is a promising tool for fast and accurate measurements of trees. There are several approaches to estimate aboveground woody biomass using lidar point clouds. One of the most widely used methods involves fitting geometric primitives (<em>e.g.</em> cylinders) to the point cloud, thereby reconstructing both the geometry and topology of the tree. However, current algorithms are not suited for accurate estimation of the volume of finer branches, because of the unreliable point dispersions from <em>e.g. </em>beam footprint compared to the structure diameter.</span></span></p> <p><span><span>·</span></span><span><span><span> </span><em>Methods</em></span></span></p> <p><span><span>We propose a new method that couples point cloud-based skeletonization and multi-linear statistical modelling based on structural data to make a model (structural model) that accurately estimates the aboveground woody biomass of trees from high-quality lidar point clouds, including finer branches. The structural model was tested at segment, axis, and branch level, and compared to a cylinder fitting algorithm and to the pipe model theory.</span></span></p> <p><span><span>·</span></span><span><span><span> </span><em>Key Results</em></span></span></p> <p><span><span>The model accurately predicted the biomass with 1.6% nRMSE at the segment scale from a k-fold cross-validation. It also gave satisfactory results when up-scaled to the branch level with a significantly lower error (13% nRMSE) and bias (-5%) compared to conventional cylinder fitting to the point cloud (nRMSE: 92%, bias: 82%), or using the pipe model theory (nRMSE: 31%, bias: -27%).</span></span></p> <p><span><span>The model was then applied to the whole-tree scale and showed that the sampled trees had more than 1.7km of structures on average and that 96% of that length was coming from the twigs (<em>i.e.</em> <5 cm diameter). Our results showed that neglecting twigs can lead to a significant underestimation of tree aboveground woody biomass (-21%).</span></span></p> <p><span><span>·</span></span><span><span><span> </span><em>Conclusions</em></span></span></p> <p><span><span>The structural model approach is an effective method that allows a more accurate estimation of the volumes of smaller branches from lidar point clouds. This method is versatile but requires manual measurements on branches for calibration. Nevertheless, once the model is calibrated, it can provide unbiased and large-scale estimations of tree structure volumes, making it an excellent choice for accurate 3D reconstruction of trees and estimating standing biomass.</span></span></p>
Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold
<p>Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (Na<sub>V</sub>) channel - Na<sub>V</sub>1.8, human voltage-gated calcium (Ca<sub>V</sub>) channel – Ca<sub>V</sub>1.1, and human voltage-gated potassium (K<sub>V</sub>) channel – K<sub>V</sub>1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of Na<sub>V</sub>1.8, Ca<sub>V</sub>1.1, and K<sub>V</sub>1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.</p>
Predicting zeta potential of liposomes from their structure: A nano-QSAR model for DOPE, DC-Chol, DOTAP, and EPC formulations
<p>Data set for publication: "<em>Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations.</em>"; Computational and Structural Biotechnology Journal 25 (2024) 3–8; https://doi.org/10.1016/j.csbj.2024.01.012 </p>
Arthropod food webs in the foreland of a retreating glacier: Gut content analysis and structural equation modeling (SEM)
<p>Below- and above-ground arthropod communities were explored at a glacier foreland area in low Arctic Southwest Greenland aiming for a better understanding of the mechanisms behind the arthropod succession driven by increasing temperatures in the context of an Arctic climate change scenario. Arthropods were sampled in 2015 and 2016 along a downslope transect where the microclimate became warmer downhill a chronosequence towards a climax vegetation. The arthropod data sets were analyzed in relation to an environmental data set. Bottom-up controlled population developments were important in the early phase of the vegetation development while top-down prevailed in the later phase of the vegetation development. The shift from bottom-up to top-down cascades between arthropod predators and their potential prey populations was mainly driven by increasing temperatures away from the glacier. Structural equation modeling (SEM) shows bottom-up and top-down controlled food chains as bottom-up control was important for spider and harvestman populations while top-down control was important for ground beetle populations. These mechanisms are closely related to the hunting strategies of the predators as bottom-up mechanisms are connected to a sit-and-wait behavior while top-down mechanisms are related to active-search behavior. The SEM analyzes were supported by DNA metabarcoding as well as by the literature. A consequence of the strong top-down cascades in the later phase of the succession is high rates of intra-guild predation (IGP) among all arthropod predators. Particularly in the guts of the linyphiid spider, <em>Collinsia holmgreni </em>Thorell 1871, trophic linkages to other linyphiid and lycosid spiders were detected. The IGP ratio of <em>C. holmgreni</em> was negatively correlated with the activity density of available ground-living prey. Probably as a consequence of the high IGP among the linyphiid spiders, cold-adapted linyphiid species like <em>C. holmgreni</em> decreased in numbers downhill and became extinct in the warmer climax vegetation, where lycosid spiders dominated. SEM shows that the declining activity densities of the soil fauna, such as collembolans and mites, due to predation, are responsible for the increase in organic matter content in the topsoil.</p>
Structural models of viral proteins described in Krupovic M, et al., Proc Natl Acad Sci U S A. 2024
<p>This archive contains structural models in PDB format described in Krupovic M, Kuhn JH, Fischer MG, Koonin EV. Natural history of eukaryotic DNA viruses with double jelly-roll major capsid proteins. Proc Natl Acad Sci U S A. 2024</p>
Incoherent phenomena in anisotropic periodic structures: from modeling to experimental demonstration
<p>Experimental measurement of difracted light Fig. 9 (subplot a consists of Grating_520_90_740_medium, columns 14 and 16) and Fig. 10 (reflection and transmission spectra of diffraction order generated by attached Python code) in the article Incoherent phenomena in anisotropic periodic<br>structures: from modeling to experimental demonstration.</p>
Cognition-Associated Protein Structural Changes in a Rat Model of Aging are Related to Reduced Refolding Capacity – Peptide Quantifications
<p>Cognitive decline during aging represents a major societal burden, causing both personal and economic hardship in an increasingly aging population. There are a few well-known proteins that can misfold and aggregate in an age-dependent manner, such as amyloid β and α-synuclein. However, many studies have found that the proteostasis network, which functions to keep proteins properly folded, is impaired with age, suggesting that there may be many more proteins that incur structural alterations with age. Here, we used limited-proteolysis mass spectrometry (LiP-MS), a structural proteomic method, to globally interrogate protein conformational changes in a rat model of cognitive aging. Specifically, we compared soluble hippocampal proteins from aged rats with preserved cognition to those from aged rats with impaired cognition. We identified several hundred proteins as having undergone cognition-associated structural changes (CASCs). We report that CASC proteins are substantially more likely to be nonrefoldable than non-CASC proteins, meaning they typically cannot spontaneously refold to their native conformations after being chemically denatured. The potentially cofounding variable of post-translational modifications is systematically addressed, and we find that oxidation and phosphorylation cannot significantly explain the limited proteolysis signal. These findings suggest that noncovalent, conformational alterations may be general features in cognitive decline, and more broadly, that proteins need not form amyloids for their misfolded states to be relevant to age-related deterioration in cognitive abilities.</p> <p>This deposition provides processed peptide quantifications for all LC-MS/MS proteomics experiments conducted for this study.</p>
Dataset for: Anniés et al., "Accessing structural, electronic, transport and mesoscale properties of Li-GICs via a complete DFTB-model with machine-learned repulsion potential"
<p>GPrep training data, GPrep jupyter notebook, .skf files.</p> <p>The GPrep code is available at https://doi.org/10.5281/zenodo.3697913</p>
molxspec: Deep learning models for predicting MS2 spectra from molecular structures
<p>This repository contains a pre-processed dataset derived from the <a href="https://gnps.ucsd.edu">GNPS public repository</a> of natural product mass spectra as well as pretrained model weights for four different types of model architectures using pytorch (version 1.9.0). The contents are as follows:</p> <ul> <li>gnps_processed_data.tgz: Contains tab separated files of molecule/MS2 spectra pairs derived from GNPS after filtering for invalid structures, too large molecules (bigger than 2000 M/Z spectra), and structures that yielded valid 3D geometry optimization. The processing steps were done for positive ionization mode (pos_* files), though negative ionization data is also included (neg_* files)</li> <li>models.tgz: Contains pytorch format pretrained models for four different architecutres: MLP (a residual block multilayer perceptron trained on ECFP molecular fingerprints), BERT (the same MLP but trained on pretrained representations from the Zinc V1 pretrained ChemBERTa models on SMILES), GCN (a graph convolution architecture), and EGNN (an equivariant graph neural network). Models were trained on pos_processed_gnps_shuffled_with_3d_train.tsv found in the gnps_processed_data.tgz file described previously.</li> </ul>
Structural model of PORCN .cif and .pdb files
<p>Wnt signaling is essential for normal development and is a therapeutic target in cancer. The enzyme PORCN, or porcupine, is a membrane-bound O-acyltransferase (MBOAT) that is required for the post-translational modification of all Wnts, adding an essential mono-unsaturated palmitoleic acid to a serine on the tip of Wnt hairpin 2. Inherited mutations in PORCN cause focal dermal hypoplasia, and therapeutic inhibition of PORCN slows the growth of Wnt-dependent cancers. Here, based on homology to mammalian MBOAT proteins we develop and validate a molecular structural model of PORCN. The model accommodates palmitoleoyl-CoA and Wnt hairpin 2 in two tunnels in the conserved catalytic core, shedding light on the catalytic mechanism. The model predicts how previously uncharacterized human variants of uncertain significance can alter PORCN function. Drugs including ETC-159, IWP-L6 and LGK-974 dock in the PORCN catalytic site, providing insights into PORCN pharmacologic inhibition. This structural model provides mechanistic insights into PORCN substrate recognition and catalysis as well as the inhibition of its enzymatic activity and can facilitate the development of improved inhibitors and the understanding of disease relevant PORCN mutants.</p>
Model and data for: Economical defense of resources structures territorial space use in a cooperative carnivore
<p>Manuscript Abstract: Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using gray wolves (<i>Canis lupus</i>). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs, and tradeoffs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk, and seasonality. It accomplished this using simple behavioral rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behavior.</p> <p>Model & Data Abstract: In support of the above manuscript, all model files and data to re-create the analyses for the manuscript are included on Dryad. The model can be run in NetLogo (installation file included), using the associated input files to build the Montana landscape for wolves. Expertise in NetLogo is strongly recommended for using this model. Output files are likewise included along with code to create each plot in the manuscript and SI. Software files for the model and code to create each plot in the manuscript are located at Zenodo: https://doi.org/10.5281/zenodo.5802243.</p>
STRUCTURAL ULS MODEL DATASET
<p>This is the database corresponding to the structural ultimate limit state (ULS) analysis of LiftWEC. Three datasets are uploaded: 1) Operational loads, 2) Non-operational loads and 3) Structural dynamics. The operational loads analysis presents shear forces, bending moments and deflections computed for cases 108-114 and 140-146 of the experimental runs performed by the LiftWEC consortium. The non-operational loads analysis presents the computed stresses on one side of the rotor as it is towed in calmed water and in waves. Lastly, the structural dynamics dataset presents an example of motion of the central hub supported by a compliant v-frame and an example of the motion of a single foil supported by a passive radial spring. Three stiffnesses are used for the structural dynamics study: low, medium and high stiffness. Further details of the datasets can be found in the PDF document: “LW-WP01-AAG-T01-1x0 LiftWEC DATASET ULS STRUCTURAL ANALYSIS”.</p>
Neurochemical and cognitive changes precede structural abnormalities in the TgF344-AD rat model
<p>Raw data for manuscript accepted in Brain Communications, awaiting official publication. All datasets contain subject information and modality-specific data. Data was acquired longitudinally at four timepoints from hemizygous TgF344-AD rats (transgenic AD model bred on a Fischer 344 background) and wildtype littermates. </p> <p>1) Barnes Maze probe data. Probe data acquired 48 hours after last training trial. </p> <p>2) Magnetic Resonance Spectroscopy data: absolute concentrations are provided, as well as relative to total creatine. </p> <p>3) Magnetic Resonance Imaging data: absolute brain volumes generated from absolute jacobians. "Brain-size-corrected" volumes are referred to as "relative" volumes and are generated from relative jacobians. </p>
Data of the protocol paper: Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST
<p>The archived file contains data of the test cases used used in the protocol paper. For each protocol, there's a folder containing input files the protocol takes, and a folder for the protocol output files.</p>
Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data
Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible, and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30,040 hectares) along a 3,322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in productivity, opening up possibilities to fully harness remote sensing data and reliably scale up from traits to map global productivity in a more direct, efficient, and cost-effective manner.
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.