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295 results for “Structure prediction”
Predicted secondary structures (2-D representation of this self-folding) of the terminal untranslated regions (UTR) of FvMV1 and FaMV1-162
<p>Schemes (a) and (c) represent the 5`-UTR and 3`-UTR of FvMV1 with dG = -82.52 and dG = -29.55, respectively. Schemes (b) and (d) represent the 5`-UTR and 3`-UTR of FaMV1-162 with dG = -73.96 and dG = -13.93, respectively. The +ssRNA molecules were folded, and the free energy was calculated with the RNA Folding Form V 2.3 Energies (MFOLD) program. For these calculations, the following conditions were sectioned: 25ºC, 1M NaCl and 0M divalent ions. The rendering of the structures has been defined with natural angles and annotated using colored base characters, based on p-num information. Colors are ranged from red to black representing the probability as well-determined (1) to poorly determined (0), respectively.</p>
Supplementary Data for Secondary structure and DNA binding domain prediction
<p><strong>This project contains the following extended data:</strong></p> <ul> <li><strong>Supplementary Table 1: </strong>Summary table of DNA binding domains (DBD), the counts of target regions within the genome and statistical analysis. (DNA_BINDING_DOMAINS_ID.tsv)</li> <li><strong>Sequence</strong>: ecCEBPα secondary structure prediction with RNAplfold at a pairing probability cut off of 0.1. N represents all sequences with pairing probability greater than 0.1. (predicted_secondary_structure_of_ecCEBPA.fa)</li> </ul>
Host-pathogen protein interactions predicted using structure
<p>This dataset accompanies a manuscript describing a method to predict host-pathogen protein interactions using structure:</p> <p>Host-pathogen protein interactions predicted by comparative modeling.<br /> Davis FP, Barkan DT, Eswar N, McKerrow JH, Sali A. Protein Sci (2007) 16:2585-2596.<br /> http://www.proteinscience.org/cgi/doi/10.1110/ps.073228407</p> <p>The files contain predictions made for 10 human pathogens including species of Mycobacterium, Apicomplexa, and Kinetoplastida. The species.all.zip files contain all interactions predictions for each species along with the filter criteria that each interaction passed. The species.filter.zip files contains the same information, but only for the subset of interactions that passed the biological and network-level filters. These files can be viewed in any spreadsheet program, such as Excel.</p> <p> </p> <p>Predictions were made for interactions between human and </p> <ol> <li>Mycobacterium tuberculosis: mtuber</li> <li>Mycobacterium leprae: mleprae</li> <li>Leishmania major: lmajor</li> <li>Trypanosoma brucei: tbrucei</li> <li>Trypanosoma cruzi: tcruzi</li> <li>Cryptosporidium hominis: chominis</li> <li>Cryptosporidium parvum: cparvum</li> <li>Plasmodium falciparum: pfalciparum</li> <li>Plasmodium vivax: pvivax</li> <li>Toxoplasma gondii: tgondi</li> </ol>
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>
Structural and socioeconomic features of cities predict migratory bird species richness
<p>Cities are aggregates of human activities where our decisions shape the environment creating heterogeneity across urban centers that can have significant ecological effects on wildlife. Many bird species are found in cities during the breeding season, which implies they find sufficient resources in cities to support them during this energetically costly time. As populations of many migratory bird species are declining, knowledge of how they are affected by urbanization is needed. Yet, we know little about how the species richness of migratory birds varies across different types of cities. Here we ask if cities' structural and socioeconomic features can predict the species richness of migratory birds that generally select different breeding habitats during the breeding season. We used eBird data from census-designated urban areas in the United States to model the relationship between features of cities (housing density, median income, city age, and commuting time), environmental disturbance (measured by the human footprint index) and species richness by fitting generalized linear models to data. We show that commuting time was the most important factor determining species richness across cities and the rest of the city features were weakly associated with species richness. Overall species were responding to city variation in similar ways. While we expected that cities with more disturbance would have lower species richness, our results indicate that some species are able to tolerate even highly disturbed cities and that cities in certain regions may act as a refuge to birds. This knowledge is important for our general understanding of cities as habitat for birds and how migratory birds respond to across-city variation during the breeding season.</p>
ABodyBuilder2 predicted structures of paired antibody sequences from Observed Antibody Space.
<p>We used ABodyBuilder2 (https://doi.org/10.1038/s42003-023-04927-7) to model ~1.5M paired antibody structures from paired antibody sequences in Observed Antibody Space (https://opig.stats.ox.ac.uk/webapps/oas/oas_paired/). We have save the structures in folders and sub folders that correspond to the OAS files they came from. Parent folders are named according to study. Within each parent folder are sub folders names according to the files (named by SRA ID) containing sequences. Each structure is then named with the parent file followed by the row number from this file.</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>
Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction
<p>Dataset produced for the Project PRELUDIUM19 2020/37/N/NZ2/00967 entitled: "Towards automatic derivation of geometry-based descriptors as surrogates for complex structural approaches in enzyme-substrate prediction"</p> <p>The dataset counts with the three families of enzymes used: dehalogenase, aldehyde reductase and nitrilase.</p> <p>For each enzyme, the docked structures, docked parameters and scripts to analyze them further are present. Moreover, the protocol that derives geometric descriptors from docked structures is also present.</p> <p>This work was supported by the National Science Centre, Poland (grant no. 2020/37/N/NZ2/00967)</p>
Adaptive sampling-based structural prediction reveals opening of a GABAA receptor through the αβ interface
<p>These are the simulation of an open state of α1β2<span>γ</span><span>2 GABAA receptor. There are five replicates, each for 200 ns, saved with every ns freequency. The prot_masses.pdb is the desenstized starting state model and the .dcd files are simualtion frames of the open state. </span></p>
Dataset for "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction"
<p>Datasets used in "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction".</p> <p>The provided zip file contains:</p> <ul> <li><strong>Datasets 1–3:</strong> For each dataset, directories include input sequences (FASTA), multiple sequence alignments (MSAs in AFA format), and normalized predicted structures from six deep learning tools. Dataset 3 also contains references - RNA chains extracted from complexes. These folders also include a CSV file with all metrics for all RNAs and tools.</li> <li><strong>Dataset 4:</strong> A text file listing the PDB IDs of RNAs included in this dataset, which is a subset of Dataset 3.</li> <li><strong>Dataset complexes:</strong> RNA chains extracted from predicted complexes, where predictions are made by AlphaFold 3 web server and, in some cases, RoseTTAFoldNA. There are also job files for the AlphaFold 3 web server used to obtain these predictions. Same as for previous datasets, this folder also includes a CSV file with all metrics for these RNA chains.</li> </ul> <p>The structure of the dataset and details of each folder are explained in the included README file.</p> <p> </p>
Predicted structures of SLC-protein complexes and controls
<p>Solute carrier (SLC) transporters form a major superfamily which transport a wide range of substrates across cellular and organellar membranes. To study their regulation on protein level and position SLCs in the human interactome, we conducted a large-scale interrogation of the protein-protein interactions (PPIs) of SLCs employing affinity purification combined with mass spectrometry (AP-MS). This study resulted in thousands of novel protein interactions of SLCs. For a subset of SLC protein complexes, we performed structural predictions using AlphaFold (v2.2, v2.3 and v3). The dataset attached contains the structures in PDB/CIF format. The structures were further discussed in the associated manuscript. In addition, an annotation table is provided, which summarizes the scores for each modelled complex. </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>
Both source and recipient range phylogenetic community structure can predict the outcome of avian introductions
<p>Competing phylogenetic models have been proposed to explain the success of species introduced to other communities. Here, we present a study predicting the establishment success of birds introduced to Florida, Hawaii, and New Zealand using several alternative models, considering species' phylogenetic relatedness to source and recipient range taxa, propagule pressure, and traits. We find consistent support for the predictive ability of source region phylogenetic structure. However, we find that the effects of recipient region phylogenetic structure vary in sign and magnitude depending on inclusion of source region phylogenetic structure, delineation of the recipient species pool, and the use of phylogenetic correction in the models. We argue that tests of alternative phylogenetic hypotheses including the both source and recipient community phylogenetic structure, as well as important covariates such as propagule pressure, are likely to be critical for identifying general phylogenetic patterns in introduction success, predicting future invasions, and for stimulating further exploration of the underlying mechanisms of invasibility.</p>
A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2
<p>Data produced and analyzed in the manuscript "A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2" by Pasquadibisceglie et al.</p> <p><br> If you include these data in your manuscript, please cite: Pasquadibisceglie A, Leccese A and Polticelli F (2022) A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2. <em>Front. Chem.</em> 10:1004815. doi: 10.3389/fchem.2022.1004815</p>
Non-structural carbohydrates predict survival in saplings of temperate trees under carbon stress
<p>1. Non-structural carbohydrates (NSCs) mediate plant survival when the plant's carbon (C) balance is negative, suggesting that NSCs could predict plant survival under C stress. To examine this possibility, we exposed saplings of six temperate tree species to diverse levels of C stress created by the combination of two light conditions (full light availability and deep shade) and two defoliation levels (severe defoliation and non-defoliation). We then measured survival, biomass, and total NSCs and soluble sugar (SSs) concentrations in different organs of both dead and live saplings.</p> <p>2. We estimated mean NSCs and SSs contents and concentrations per sapling and fitted logistic generalized mixed-effects models to determine if NSCs and SSs predict survival. Using inverse prediction modelling, we also determined whether there is a common NSCs and SS threshold across species at the time of sapling's death.</p> <p>3. Defoliation and shade reduced the mean sapling's NSCs and SSs contents, indicating C stress. Mean sapling NSCs and SSs contents and concentrations predicted survival and the robustness of the models improved with the inclusion of species. At death, saplings of the exotic deciduous tree species Acer pseudoplatanus exhibited significantly lower mean NSCs and SSs contents than saplings of the evergreen conifer species Podocarpus nubigenus and lower stem NSCs and SSs concentrations than the broadleaf evergreen species Drimys winteri.</p> <p>4. The energetic role that NSCs and SSs play in plants under C stress was evidenced by the capacity of these compounds to predict sapling survival under C stress. No common threshold of NSCs and SSs contents or concentrations for sapling survival amongst species was found, indicating that the level of these compounds may not be good proxies for interspecific comparisons of tolerance to C stress. Presumably, there are species-specific limits for the mobilization and use of NSCs and SSs in metabolism.</p> <p>5. Our results anticipate that the inclusion of NSCs and SSs in modelling will improve predictions regarding tree responses to ongoing climate change. Nonetheless, a better understanding of the many roles that carbohydrates play in plant survival under C stress is required to scale predictions up to the community level.</p>
Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
<p>Urbanization and stream order alter freshwater habitat complexity (defined as the degree of variation in physical habitat structure). More complex habitats have more variation in habitat structure. Habitat complexity affects species composition and shapes animal ecology, behavior, and cognition. We used a delayed detour test to measure whether motor self-regulation and behavior of Western mosquitofish, <em>Gambusia affinis, </em>varied with habitat structural complexity that was quantified for nine populations. We predicted that motor self-regulation, motivation, and risk-taking behavior would increase with increasing habitat complexity, yet we found the opposite relationship. Lower complexity habitats offer less refuge which could increase predation pressure and select for greater risk-taking by fish with greater motor self-regulation. Our findings provide insight into how habitat complexity is related to cognitive processes and behavioral outcomes, and provide an explanation for why some species have a higher tolerance for urbanized environments.</p>
AlphaFold 3 predicted NLR resistosome structures
<p><strong>Abstract</strong></p> <p>In our paper "A disease resistance protein triggers oligomerization of its NLR helper into a hexameric resistosome to mediate innate immunity," we used the NbNRC2 hexamer structure to evaluate the capabilities of the newly introduced AlphaFold 3 in predicting activated CC-NLR oligomers. Our analysis highlights AlphaFold 3 effectiveness in confidently modelling the N-terminal alpha1-helices of NbNRC2 and other CC-NLRs, a structurally elusive region critical for NLR function but challenging to resolve through conventional structural methods. This study not only underscores the utility of AlphaFold 3 in enhancing our understanding of plant immune receptors but also extends its application to complex oligomerization processes in innate immunity. Here, we provide the Supplementary data accompanying the paper which includes metadata and predicted structures for 29 NLR immune receptors, providing valuable resources for further research in plant pathogen resistance.</p> <p> </p> <p><strong>Index:</strong></p> <ul> <li>Metadata:</li> <li> <ul> <li>Metadata, sequence, and model statistics of all modelled NLRs: <ul> <li>Data S1.xlsx</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>Predicted structures:</li> <li> <ul> <li>AlphaFold 2 vs 3 vs 3+lipids comparison for NbNRC2: <ul> <li>AF_benchmark.zip</li> </ul> </li> </ul> </li> </ul> <ul> <li> <ul> <li>[AlphaFold 3] NRC2 oliogmoeric type benchmarks (N = 10): <ol> <li>NRC2_tetramers.zip</li> <li>NRC2_pentamers.zip</li> <li>NRC2_hexamers.zip</li> <li>NRC2_heptamers.zip</li> <li>NRC2_octamers.zip</li> </ol> </li> </ul> </li> </ul> <ul> <li> <ul> <li>[AlphaFold 3] Pentamer vs Hexamer comparison for 11 representative NRC proteins: <ul> <li>NRCs.zip</li> </ul> </li> <li>[AlphaFold 3] Pnetamer vs Hexamer comparison for AtZAR1 (PDB:6j5t) and TmSr35 (PDB:7xe0) experimentally validated cryo-EM structure: <ul> <li>Benchmarks.zip</li> </ul> </li> <li>[AlphaFold 3] Pentamer vs Hexamer comparison for a selection of 8 CC, 6 CCG10, and 2 CCR-NLRs: <ul> <li>CC.zip</li> <li>CCG10.zip</li> <li>CCR.zip</li> </ul> </li> </ul> </li> </ul>
Predicting Publication of Clinical Trials Using Structured and Unstructured Data
<p>This dataset (N=76,950) links metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a bibliographic database of academic journal articles), and can be used to model whether a clinical trial will get published or not.</p>
T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: prediction of mechanical and structural properties
<p>Dataset for the manuscript titled "T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: assessment of mechanical and structural properties"</p>
Simulated and experimental data distributed to the CASP13 participants in protein structure prediction assisted with sparse NMR data
<p>All simulated and experimental data distributed to the CASP participants in protein structure prediction assisted with sparse NMR data in CASP13.</p> <p>Also available at http://predictioncenter.org/casp13/results.cgi?view=targets&model=first&tr_type=others&sub_type=N&groups_id=</p> <p> </p>
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.