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1,805 results for “molecule”
A Data Resource for Prediction of Thermodynamic Properties of Small Molecules
<p>We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for entropy for substances composed of stable small molecules, derived from the literature. We developed a model for predicting enthalpy of formation and entropy from semiempirical quantum mechanical calculations of energy and atom counts, and applied the model to a comprehensive database of 16,417 small molecules. The database of small-molecule thermodynamic properties will be useful for predicting the outcome of any process that might involve the generation or destruction of volatile products, such as atmospheric chemistry, volcanism, or waste pyrolysis. Additionally, the collected experimental thermodynamic values will be of value to others developing models to predict enthalpy and entropy.</p>
CH3CH2OCH3 conformer molecule 200 ps MD trajectory with energies and forces
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p> </p> <p>SOAP params:</p> <p>species=["H", "C", "O"],</p> <p>periodic=False,</p> <p>rcut=5.0,</p> <p>sigma=0.5,</p> <p>nmax=5,</p> <p>lmax=5,</p> <p>average="outer" / "inner",</p> <p>crossover=True,</p> <p>dtype="float64",</p> <p>------------------------------------------------</p> <p>SOAP invariants were calculated with DScribe library (https://pypi.org/project/dscribe/1.2.1/)</p> <p> </p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
Molecular dynamics trajectories of C3 H8 O molecule and its structural isomers
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p>------------------------------------------------</p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
Dataset supporting manuscript entitled 'Partitioning of Small Hydrophobic Molecules into Polydimethylsiloxane in Microfluidic Analytical Devices'
<p>This is the dataset supporting the manuscript 'Surface and bulk modifications of polydimethylsiloxane to reduce absorption/adsorption of small molecules in lab-on-a-chip' published in Micromachines</p>
SupportingDataset Identification and Quantification of Within-Burst Dynamics in Singly-Labeled Single-Molecule Fluorescence Lifetime Experiments
<p>The Jupyter notebooks and resulting files used to demonstrate divisor-based mpH<sup>2</sup>MM. The analysis is demonstrated with both simulations and analyses of alpha-synuclein.</p> <ol> <li> <p><em><strong>Notebooks.zip</strong></em>:* Zip file containing the Jupyter notebooks for producing, analyzing and visualizing the simulated photon trajectories. Note: this folder contains all code needed to reproduce simulations. All other files related to the simulations are produced by one of the notebooks in this trajectory. However, as simulations can take a long time, the various results files are included in this repository so that notebooks can be run from intermediate steps.</p> <ol> <li> <p><strong>1-PIFE-pybromo-sims.ipynb</strong> : The code for producing simulated diffusion trajectories and photon-HDF5 files of two-state systems undergoing transition dynamics (the results of this notebook are stored in the sub-folder <em>PyBroMo_photonHDF5</em>)</p> </li> <li> <p><strong>2-PIFE-mpH2MM-sim-[lifetime components].ipynb </strong>: Notebooks performing divisor-based mpH<sup>2</sup>MM on simulated datasets for a given combination of lifetime states. (these notebooks store files that are contained in the sub-folder <em>H2MMresults</em>)</p> </li> <li> <p><strong>3-PIFE-mpH2MM-compiled-plots.ipynb</strong> : Jupyter notebook for producing figures comparing all results globally</p> </li> <li> <p><strong>532nm_IRF_19-10-2021.csb</strong>: the file containing the experimental IRF used in the simulations</p> </li> </ol> </li> <li> <p><em><strong>PyBroMo_photonHDF5.zip</strong></em>:* Zip file containing the simulated results of <em>1-PIFE-pybromo-sims</em> notebook as photon-HDF5 files (1 file per transition rate/lifetime combination)</p> </li> <li> <p><strong>PIFE-sim-dynamicmix_[lifetime components]_result.hdf5</strong>: special HDF5 files containing the results of each notebook in <em>Notebooks</em>, which are used by <em>3-PIFE-mpH2MM-compiled-plots</em></p> </li> <li> <p><strong>PIFE-mpH2MM-alpha-syn-vFinal.ipynb</strong>: divisor-based mpH<sup>2</sup>MM analysis of alpha-synuclein smPIFE data</p> </li> <li> <p><strong>H2MM-Lifetime_example.ipynb</strong>: A demonstration of divisor-based mpH<sup>2</sup>MM using nsALEX-smFRET data. This method could potentially demonstrate states differentiated in lifetimes independently of potential changes in E & S.</p> </li> <li> <p><strong>Template_ltH2MM.ipynb</strong>: An easy-to-follow implementation of divisor-based mpH<sup>2</sup>MM demonstrated on a single alpha-synuclein experimental data acquisition file. This can be used for learning how to implement and analyze single dye fluorescence lifetime data with mpH<sup>2</sup>MM</p> </li> </ol> <p> </p> <p>* For running these notebooks, generally, all files in <em>PyBroMo_photonHDF5.zip</em> should be placed into a single directory (i.e., the files in <em>Notebooks</em>.<em>zip</em> should be placed into the same directory as the files in <em>PyBroMo_photonHDF5</em>.<em>zip</em>) as the notebooks are set to read in files from their current directory.</p>
Enabling spectrally resolved single-molecule localization microscopy at high emitter densities: Dataset
<p>The data in this dataset accompanies the various figures present in the publication 'Enabling spectrally resolved single-molecule localization microscopy at high emitter densities'. Contained are tiff files used to create the figures 2-4 and Supplementary figures 1 and 2, as well as csvs after processed with the steps described in the paper (and contained in protocol text files).</p>
Long-Term, Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores
<p>Supporting data for paper "Long-Term, Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores"</p>
Trajectories and Code from "Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states" Zhu et al. 2022
<p>Trajectories, GROMACS input files, and analysis code from the manuscript "Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states" Zhu et al. 2022 (Nature Communications, In Press)</p> <p>https://www.biorxiv.org/content/10.1101/2021.12.23.474012v1.abstract</p>
Ball-and-stick models of the octane molecule
<p><strong>Ball-and-stick models of the octane molecule</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Octane is a hydrocarbon and an alkane. Octane has many structural isomers that differ by the amount and location of branching in the carbon chain. As with all low-molecular-weight hydrocarbons, octane is volatile and very flammable. "Octane" is colloquially used as a short form of "octane rating," particularly in the expression "high octane". "Octane rating" is an index of a fuel's ability to resist engine knock in engines having different compression ratios, which is a characteristic of octane's branched-chain isomers, especially iso-octane. The octane rating of gasoline is not directly related to the power output of an engine. Using gasoline of a higher octane than an engine is designed for cannot increase power output. An octane rating, or octane number, is a standard measure of a fuel's ability to withstand compression in an internal combustion engine without detonating. Octane rating does not relate directly to the power output or the energy content of the fuel per unit mass or volume, but simply indicates gasoline's capability against compression. Whether or not a higher-octane fuel improves or impairs an engine's performance depends on the design of the engine. In broad terms, fuels with a higher-octane rating are used in higher-compression gasoline engines, which may yield higher power for these engines. Such higher power comes from the fuel's higher compression by the engine design, and not directly from the gasoline. In contrast, fuels with lower octane are ideal for diesel engines because diesel engines do not compress the fuel, but rather compress only air and then inject fuel into the air that was heated by compression.</p> <p> </p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Single molecule, full-length transcript sequencing provides insight into the extreme metabolism of ruby-throated hummingbird Archilochus colubris
<p>Hummingbirds can support their high metabolic rates exclusively by oxidizing ingested sugars, which is unsurprising given their sugar-rich nectar diet and use of energetically expensive hovering flight. However, they cannot rely on dietary sugars as a fuel during fasting periods, such as during the night, at first light, or when undertaking long-distance migratory flights, and must instead rely exclusively on onboard lipids. This metabolic flexibility is remarkable both in that the birds can switch between exclusive use of each fuel type within minutes and in that de novo lipogenesis from dietary sugar precursors is the principle way in which fat stores are built, sometimes at exceptionally high rates, such as during the few days prior to a migratory flight. The hummingbird hepatopancreas is the principle location of de novo lipogenesis and likely plays a key role in fuel selection, fuel switching, and glucose homeostasis. Yet understanding how this tissue, and the whole organism, achieves and moderates high rates of energy turnover is hampered by a fundamental lack of information regarding how genes coding for relevant enzymes differ in their sequence, expression, and regulation in these unique animals. To address this knowledge gap, we generated a de novo transcriptome of the hummingbird liver using PacBio full-length cDNA sequencing (Iso-Seq), yielding a total of 8.6Gb of sequencing data, or 2.6M reads from 4 different size fractions. We analyzed data using the SMRTAnalysis v3.1 Iso-Seq pipeline, including classification of reads and clustering of isoforms (ICE) followed by error-correction (Arrow). We performed orthology analysis to identify closely related sequences between our transcriptome and other avian and human gene sets. We also aligned our transcriptome against the Calypte anna genome where possible. Finally, we closely examined homology of critical lipid metabolic genes between our transcriptome data and avian and human genomes. We confirmed high levels of sequence divergence within hummingbird lipogenic enzymes, suggesting a high probability of adaptive divergent function in the lipogenic liver pathways. Our results have leveraged cutting-edge technology and a novel bioinformatics pipeline to provide a compelling first direct look at the transcriptome of this incredible organism.</p>
Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α-Fig. 1df
<p>smTIRF-FRET Data for Fig 1, for "Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α"</p>
Engineering dynamic gates in binding pocket of penicillin G acylase to selectively degrade bacterial signaling molecules
<p>(01-mutants_design.tar.gz) Mutants design:</p> <ol> <li>Input structures of ecPGA from the PDB database (PDB IDs: 1GK9, 1GM7, and 1GM9), processed to resemble wild-type state, repaired by RepairPDB module of FoldX 4</li> <li>Double-point mutants preparation, analysis and filtering: <ol> <li>text files including configuration for FoldX 4</li> <li>inputs and outputs of CAVER 3.02 calculations on FoldX 4 PDB files of ecPGA double-point mutants</li> <li>input configuration file and TransportTools library 0.9.4 calculations outputs generated based on the inputs produced in step 01 and 02 above</li> <li>CSV files containing complete information about FoldX 4 stability prediction and geometrical properties from CAVER 3.02 and TransportTools library version 0.9.4 for ecPGA double-point mutants</li> </ol> </li> <li>Triple-point mutant preparation, analysis and filtering: <ol> <li>text files including configuration for FoldX 4</li> <li>inputs and outputs of CAVER 3.02 calculations on FoldX 4 PDB files of ecPGA triple-point mutants</li> <li>input configuration file and TransportTools library 0.9.4 calculations outputs generated based on the inputs produced in step 01 and 02 above</li> <li>CSV files containing complete information about FoldX 4 stability prediction and geometrical properties from CAVER 3.02 and TransportTools library version 0.9.4 for ecPGA triple-point mutants</li> </ol> </li> </ol> <p>(02-docking.tar.gz) Preparation of protein-ligand complexes using molecular docking for wild-type ecPGA and 6 best designed triple-point mutants with 6 various bacterial signaling molecules:</p> <ol> <li>PDB files of ligand, PDBQT files of the receptor and PDB files of the complexes selected from docking experiment:</li> </ol> <p>Full names of presented protein variants:<br>ecPGA_wt, wild-type Escherichia coli penicillin G acylase<br>LAF, Phe138αLeu & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_22<br>LSF, Phe138αLeu & Met142αSer & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_98<br>MAF, Phe138αMet & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_23<br>MSF, Phe138αMet & Met142αSer & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_99<br>VAF, Phe138αVal & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_30<br>YAF, Phe138αTyr & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_33<br>Full names of presented AHLs:<br>C06, N-hexanoyl-L-homoserine lactone;<br>C06-3O, N-3-oxo-hexanoyl-L-homoserine lactone;<br>C08, N-octanoyl-L-homoserine lactone;<br>C08-3O, N-3-oxo-octanoyl-L-homoserine lactone;<br>C10, N-decanoyl-L-homoserine lactone;<br>C12-3O, N-3-oxo-dodecanoyl-L-homoserine lactone</p> <p>(03-protein_ligand_MDs.tar.gz) Ligand-enzyme complexes molecular dynamics for wild-type ecPGA and 6 best designed triple-point mutants with 6 various bacterial signaling molecules:</p> <ol> <li>Force field parameters in Amber format</li> <li>Input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format</li> <li>Output files generated during post-processing of production runs trajectories in a form of text files generated by cpptraj</li> </ol> <p>(04-free_enzymes_MDs.tar.gz) Free enzymes molecular dynamics of 3 best triple-point ecPGA (VAF, YAF and MSF) mutants prioritized based on protein-ligand molecular dynamics simulations and experimental assays:</p> <ol> <li>Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber format</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format</li> <li>Post-processing analysis of generated trajectories: <ol> <li>Text files with distances, CSV files containing result of PCA and clustering, PNG files with clustered PCA results</li> <li>Inputs and outputs of MDpocket analysis and visualization of the pocket frequency grid as an isomesh</li> <li>CAVER input configuration files in text format, CAVER output data including parsed CSV and text files for visualization of entrance opening time evolution and cavity profiles inspection</li> <li>cpptraj generated text files including RMSD, distances and chi1 angles measurements</li> </ol> </li> </ol> <p>All plots were generated using matplotlib or seaborn Python libraries. Figures containing structural representations were generated using PyMOL 2.0.1.</p> <p>(05-ecPGA_VAF_YAF_MSF_penG_MDs.tar.gz) PenG-enzyme complexes molecular dynamics for wild-type ecPGA and 3 best designed triple-point mutants (VAF, YAF, MSF):</p> <ol> <li>PenG force field parameters in Amber (GAFF) format</li> <li>Input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format and analysis output files generated during post-processing of production runs trajectories in a form of text files</li> <li>Reactive Stabilization Score [RSS] statistics summarized in CSV files</li> </ol>
Raw data to: Acetyl-CoA synthetase activity is enzymatically regulated by lysine acetylation using acetyl-CoA or acetyl-phosphate as donor molecule
<p>Initial configuration (PDB) and TIGER2hPE ensemble of all four simulations (R1-4) reported in this study in DCD trajectory format:</p> <ol> <li>AcuA + AcsA + Acetyl-CoA</li> <li>Acua + AcsA + Acetyl-CoA (AcuA:K549 deprotonated)</li> <li>AcuA + AcsA + CoA + AcP (Acetyl-Phosphate)</li> <li>AcuA + AcsA + Desulfo-CoA</li> </ol> <p> </p>
Data for "Atomic-scale perspective on individual thiol-terminated molecules anchored to single S vacancies in MoS2"
<p>This repository provides the original datasets for the manuscript "Atomic-scale perspective on individual thiol-terminated molecules anchored to single S vacancies in MoS2". It includes the original experimental data as well as the iPython Notebooks used to treat it in "<a href="../api/records/10160204/draft/files/Data_and_Analysis.zip/content" target="_blank" rel="noopener noreferrer">Data_and_Analysis.zip</a>" (see readme in individual folders for precise information), the datasets for the structure search and molecular dynamics calculations in "<a href="../api/records/10160204/draft/files/structure_search_and_MD.zip/content" target="_blank" rel="noopener noreferrer">structure_search_and_MD.zip</a>", as well as the data for the DFT calculations for the projected electronic density of states (PDoS) and the orbital densities of Fig.5 and 8 in "<a href="../api/records/10160204/draft/files/fig5.zip/content" target="_blank" rel="noopener noreferrer">fig5.zip</a>" and "<a href="../api/records/10160204/draft/files/fig8.zip/content" target="_blank" rel="noopener noreferrer">fig8.zip</a>", respectively.</p> <p> </p>
Fig. 9 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 9. Evolutionary hypothesis of interrelationships among the four free-living litostomatean lineages studied. This scenario was suggested on the basis of morphology and the consensus secondary structure of the ITS2 molecules. CK – circumoral kinety, DB – dorsal brush, OB – oral bulge, OO – oral bulge opening, P – proboscis, PE – perioral kinety, PR – preoral kineties, SK – somatic kineties.
Fig. 5 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 5. Quartet likelihood-mapping showing distribution of phylogenetic signal in the 18S-A and the CON-1 alignment for three possible relationships among the four main free-living litostomatean lineages studied. The corners of the triangles show the percentage of fully resolved trees, i.e., phylogenetically informative signal. The rectangular areas show the percentage of trees that are in conflict. The central triangle shows the percentage of unresolved star-like trees, i.e., phylogenetically uninformative signal. Coding of free-living litostomatean lineages: H – Haptorida, P – Pleurostomatida, R – Rhynchostomatia, S – Spathidiida.
Fig. 4 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 4. Super-network of 66 free-living litostomatean taxa constructed from 80 randomly selected post-burn-in trees from the Bayesian inference of the 18S-A–D, ITSR-C and ITSR-D as well as the CON-1 and CON-2 alignments. The super-network was constructed in the program SplitsTree, using the Z-closure option, tree size weighted mean, ten runs, and the refined heuristic technique. For details on taxa and characteristics of the alignments analyzed, see Supplementary Table S1 and S2.
Fig. 3 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 3. Phylogeny based on the 18S rRNA gene and the ITS1-5.8S-ITS2 region of 56 free-living litostomatean taxa (alignment CON-1). Posterior probabilities for the Bayesian inference and bootstrap values for maximum likelihood were mapped onto the 50% majority rule ML tree. Dashes indicate posterior probabilities below 0.50 and ML bootstrap values below 50%. The scale bar indicates five substitutions per ten nucleotide positions. For details on taxa, evolutionary model used, and characteristics of the CON-1 alignment, see Supplementary Table S1 and S2.
Fig. 1 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 1. Phylogeny based on the 18S rRNA gene of 64 free-living litostomatean taxa (alignment 18S-A). Posterior probabilities for Bayesian inference and bootstrap values for maximum likelihood were mapped onto the 50% majority rule Bayesian consensus tree. Dashes indicate ML bootstrap values below 50%. Sequences in bold were obtained during this study. The scale bar indicates two substitutions per one hundred nucleotide positions. For details on taxa, evolutionary model used, and characteristics of the 18S-A alignment, see Supplementary Table S1 and S2.
Fig. 8 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 8. Structure logo of ITS2 helices II and III in various higher litostomatean taxa. The height of a base is proportional to its frequency in multiple sequence alignments.
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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.