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24 results for “quantum calculation”
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
TREXIO files used for the validation tests in the paper entitled 'TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods'.
<p>The TREXIO files used for the validation tests in the paper entitled TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods. The detail about the TREXIO library is described in the JCP article [J. Chem. Phys. 158, 174801 (2023)] and the GitHub repository [https://github.com/TREX-CoE/trexio]. The TREXIO files were generated using TREXIO version 2.3.2 (and the corresponding Python API version 1.3.2).</p>
Scripts, Data, and Figures for "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems"
<div> <p>This archive contains the scripts required to run all calculations presented in "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems," the figure generation scripts and attendant processed data, and a copy of MesoHOPS version 1.4.0, as used in the paper.</p> </div>
Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for orbital and ionization energies) results discussed in the paper titled "Predictive simulations of core electron binding energies of halogenated species adsorbed on ice surfaces from relativistic quantum embedding calculations" by Richard Asamoah Opoku, Céline Toubin, and André Severo Pereira Gomes.</p>
Conformation and structural features of diuron and irgarol: insights from quantum chemistry calculations.
<p>A set of conformational relevant structures of Diuron and Irgarol (two biocides) obtained from conformational analyses carried out using Density Functional Theory (DFT). The geometries are given in the mol2 format.</p>
Quantum calculation results for "Butyl Acetate Pyrolysis and Combustion Chemistry: Mechanism Generation and Shock Tube Experiments"
<p>This repository contains the quantum calculation results associated with the paper "Butyl Acetate Pyrolysis and Combustion Chemistry: Mechanism Generation and Shock Tube Experiments" by Xiaorui Dong, Gianmaria Pio, Farhan Arafin, Andrew Laich, Jessica Baker, Erik Ninnemann, Subith S. Vasu, and William H. Green.</p> <p>In the BA_QM.zip, there are five subfolders:</p> <ul> <li>The "BA_Habs" folder has 18 entries related to the calculations of butyl acetate H abstraction reactions</li> <li>The "BA_Retroene" folder has 6 entries related to the calculations of butyl acetate retro-ene reactions</li> <li>The "Species" folder has 606 entries related to the calculations of non-TS species included in the kinetic mechanisms.</li> <li>The "TS_XYZ" and "XYZ" folders contain the XYZ files of the calculated TS and non-TS geometries, respectively.</li> </ul> <p>For each species and TS calculation, the CBS-QB3 optimization/single point energy calculation is stored in the "composite" folder, the frequency calculation is stored in the 'freq' folder, and scan jobs for torsional modes (if available) are stored in the 'scan_XXXX' folders. For reaction and TS entries, folders are named in a user-readable way. For species, folders are named according to their SMILES representation.</p>
QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods
<p>This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. <em>J. Phys. Chem. A</em> <strong>2023</strong>, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes:</p> <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul> <p> </p>
Quantum Chemical Calculations for the Reaction of Methoxide with OTP and CL
<p>DFT calculations at the BP/def2-TZVPD level are executed with Turbomole 7.3 and COSMO-RS calculations with COSMOtherm 19.0.4.</p> <p> </p> <p>One folder is used for one molecule which take part in the following reactions.<br> Educt + Nu- -> Intermediate -> Product<br> Charged system:<br> CL + MeO- -> 2a- -> 3a-<br> OTP + MeO- -> 2b- -> 3b-<br> Neutral system:<br> CL + MeOH -> 2aH -> 3aH<br> OTP + MeOH -> 2bH -> 3bH</p> <p>Folderstructure:<br> opt: Geometric structure optimization with PBEh-3c, frequency analysis<br> sp-vac: Electronic Structure of the vacuum state<br> sp-sol: Electronic Structure of the solute as ideal conductor<br> sp-cosmors: Calculation of solvation enthalpy</p> <p>Quantum Chemical Data Collection (QCDC) creates data.json<br> Further information can be found here:<br> https://github.com/lucasteiner/qcdc<br> </p>
Molecular geometries and energies from quantum mechanical calculations and small molecule force field evaluations.
<p>Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction.<br> The quality of these studies relies on the quality of the force fields used to represent the systems. <br> Focusing on small molecules of fewer than 50 heavy atoms, this data compares nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2.<br> On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data.<br> <br> The data was created using scripts of the <a href="https://github.com/MobleyLab/benchmarkff/commit/fa45247aa9867f504c02eb6f62d8459a94a0a936">benchmarkff github repository</a>.</p> <p>A corresponding manuscript is submitted, a preprint is available on ChemRxiv:<br> <a href="https://doi.org/10.26434/chemrxiv.12551867.v2 ">Lim, Victoria T.; Hahn, David F.; Tresadern, Gary; Bayly, Christopher I.; Mobley, David (2020): Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields. ChemRxiv. Preprint</a></p> <p>Read below or the file README.md for further information and description of the content:</p> <pre><code class="language-markdown"># README Version: 04 Nov 2020 For Python scripts that are NOT found in these directories, please check the [BenchmarkFF Github repo](https://github.com/MobleyLab/benchmarkff/tree/master/tools). ## Procedure 1. Prep OPLS3e file for analysis: standardize format by OpenEye in case of differences and convert from kJ/mol to kcal/mol. ``` cd prep python convert_extension.py -i opls3e_minimized.sd -o opls3e.sdf ``` 2. Remove mols that couldn't parameterize by ALL FFs. ``` python get_by_tag.py -i opls3e.sdf -s "SMILES QCArchive" -list trim3.txt -o trim3_full_opls3e.sdf ``` 3. Run analysis. ``` conda activate parsley # calc ddE, RMSD, and TFD distributions python compare_ffs.py -i match.in -t 'SMILES QCArchive' --plot > metrics.out # match_minima, only in 01_analysis_all and 02_analysis_all_smaller_cutoff python match_minima.py -i match.in --plot --cutoff 1.0 --readpickle # look at specific subsets, only in 01_analysis_all python color_by_moiety.py -i match.in -p metrics.pickle -s N-N.dat azetidine.dat octahydrotetracene.dat -o scatter_tfd_3_ # look at outliers,only in 01_analysis_all and 02_analysis_all_smaller_cutoff python tailed_parameters.py -i refdata_trim_overlap_full_openff_unconstrained-1.2.0.sdf -f <offxml file> --metric 'TFD' --cutoff 0.12 --tag "TFD to trim_overlap_full_qcarchive.sdf" --tag_smiles "SMILES QCArchive" > output_tfd.dat ``` ## Brief description of contents * High level: ``` . ├── 00_prep │ ├── convert_extension.py │ ├── opls3e_minimized.sd OPLS3e minimized structures from Schrodinger Maestro │ ├── opls3e.sdf standardized through OpenEye tools │ ├── opt_openff*.sdf OpenFF minimized conformations ├── 01_analysis_all compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) ├── 02_analysis_all_smaller_cutoff compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) with a smaller cutoff of .3 for match_minima ├── 03_analysis_latest_ffs compare only the latest versions of ffs (qm, GAFF2, MMFF94S, OpenFF-1.2, OPLS3e) ├── 04_analysis_openff_only compare only OpenFF ffs (qm, Smirnoff, OpenFF-X.X) └── README.md ``` * Inside an output directory: ``` YY_analysis_* various output files of above mentioned scripts, some are listed and described below: ├── bar*.png parameter coverage bar plots ├── ddE.dat relative energies data ├── fig_density_*.png scatter plots of ddE vs (RMSD or TFD) for each force field ├── match.in input file for compare_ffs.py ├── metrics.out output file for compare_ffs.py ├── metrics.pickle pickle file for compare_ffs.py -- you can read this into compare_ffs instead of rerunning the full analysis ├── refdata_*.sdf output SDF files with stored RMSD / TFD scores with reference to QM for each structure ├── relene_*.dat relative energies of matched conformers ├── ridge_dde.png compared energies plot ├── ridge_rmsd.svg compared rmsds plot ├── ridge_tfd.svg compared tfds plot ├── fig_scatter_*.png scatter plots of ddE vs (RMSD or TFD). these are noisy; I don't use these ├── trim3_*.sdf input SDF files for compare_ffs.py listed in match.in file ├── violin*.* violin plot showing ddE distributions ``` </code></pre>
Development of predictive models of the kinetics of a hydrogen abstraction reaction combining quantum-mechanical calculations and experimental data
<p>The files contain the electronic structure calculations for all the levels of theory tested in this work.</p>
Data Set "Benchmarking of Vibrational Exciton Models Against Quantum-Chemical Localized-Mode Calculations"
<p>This data set accompanies the publication "Benchmarking of Vibrational Exciton Models Against Quantum-Chemical Localized-Mode Calculations" <br>by Anna M. van Bodegraven, Kevin Focke, Mario Wolter, and Christoph R. Jacob <br>(TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory '01_AIM':</p> <p> - input (structure.pdb, topol.top and *_input.txt) and results (*.log and<br> Hamiltonian/AtomPos/Dipole/Parameters.txt) from frequency calculations<br> with the Amide-I-maps (AIM) program for six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip), <br> each in vacuo or water with three different maps (Jansen, Skinner, <br> Tokmakoff) for 11 snapshots based on a MD run.<br> To rerun the calculations, you will have to change the paths in <br> the *_input.txt files (topfile, trjfile, sourcedir) accordingly<br> <br>Directory '02_SNF':</p> <p> - results (*.dat, *.out, coord and control) from frequency calculations <br> using Turbomole, SNF, and LocVib for six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip) <br> each in vacuo or water for snapshots based on an MD run.<br> <br>Directory '03_NMA':</p> <p> - coordinates and results from pyADF for NMA molecules each alligned <br> with a peptide bond from the six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip) <br> each in water for 11 snapshots based on a MD run and input <br> (*_input.txt) and results (*.log and Hamiltonian/AtomPos/Dipole/Parameters.txt) <br> from calculations with the Amide-I-maps (AIM) program<br> <br>Directory '04_Handling_Data':</p> <p> - contains all used notebooks to extract the data, plot the figures <br> and calculate the errors<br> - RMSD_Error_vacuo/water.ipynb is used to calculate the overall shifts <br> and generates a map-dependent mean value to shift the frequencies of AIM<br> - Frequencies.ipynb and Couplings.ipynb are used to plot the figures <br> - RMSD_values_vacuo/water.ipynb show the calculations for the statistical <br> analysis<br> To use the notebooks, start with the Dictionary_setup_for_data_for_paper.ipynb <br> to set up the main dictionary from the calculated data</p>
Data for: Alchemical free-energy calculations at quantum-chemical precision
<p><span><span>In the last decade, machine-learned potentials (MLP) have </span><span>demonstrated</span><span> the capability to predict vario</span><span>us</span><span> QM properties learned from </span><span>a set of reference</span><span> QM calculations. </span><span>Accordingly</span><span>,</span><span> hybrid QM/MM simulation</span><span>s </span><span>can be accelerated</span><span> by replacement of </span><span>expensive</span><span> QM calculation</span><span>s</span><span> with </span><span>efficient </span><span>MLP </span><span>energy prediction</span><span>s</span><span>.</span> <span>At the same time</span><span>, alchemical free energy </span><span>perturbation</span><span>s</span><span> (FEP) </span><span>remain</span> <span>un</span><span>ach</span><span>ie</span><span>vable</span><span> at the QM level of theory.</span> <span>In this work</span><span>,</span><span> we extend the capabilities of the Buffer Region Neural Network </span><span>(</span><span>BuRNN</span><span>) </span><span>QM</span><span>/MM</span><span> scheme towards </span><span>FEP</span><span>.</span> <span>BuRNN</span> <span>introduces a buffer region that experiences full electronic polarization by the QM region to minimize artifacts</span> <span>at </span><span>the </span><span>QM/MM interface</span><span>. </span><span>A </span><span>MLP</span> <span>is </span><span>used to </span><span>predict the energies for the QM </span><span>region</span><span> and its interactions with the buffer region</span><span>. Furthermore, </span><span>BuRNN</span> <span>allow</span><span>s</span><span> us to implement </span><span>FEP </span><span>directly into</span> <span>the </span><span>MLP </span><span>H</span><span>amiltonian</span><span>. </span><span>Here</span><span>, </span><span>we describe the alchemical change </span><span>from methanol to methane in water </span><span>at</span><span> the </span><span>MLP</span><span>/MM level as a proof of concept.</span></span><span> </span></p>
Data Set "Accurate quantum-chemical fragmentation calculations for ion–water clusters with the density-based many-body expansion"
<p>This data set accompanies the publication "Accurate quantum-chemical fragmentation calculations for ion–water clusters with the density-based many-body expansion"</p> <p>It contains:</p> <p>- xyz files of all considered molecular structures.</p> <p>- PyADF input scripts for running the eb-MBE and db-MBE calculations.</p> <p>- raw results data from the eb-MBE and db-MBE calculations</p> <p>- Jupyter notebooks for generating the plots and tables</p>
Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations
<p>The files contains all the data that are shown in the figures of the article:</p> <p>Rumble, C.; Vauthey, E. Structural Dynamics of an Excited Donor-Acceptor Complex from Ultrafast Polarized Infrared Spectroscopy, Molecular Dynamics Simulations, and Quantum Chemical Calculations. Phys. Chem. Chem. Phys. 21 (2019). 10.1039/C9CP00795D</p>
Quantum calculation results for "Automatically Generated Model for Light Alkene Combustion"
<p>This repository contains the quantum calculation results associated with the paper "Automatically Generated Model for Light Alkene Combustion" by Gianmaria Pio, Xiaorui Dong, Ernesto Salzano, and William H. Green.</p> <p>In the alkene_qm_data.zip, files are organized according to species. For each species, CBS-QB3 calculation is stored in the 'composite' folder, frequency calculation is stored in the 'freq' folder, and scan jobs for torsional modes (if available) are stored in the 'scan_XXXX' folders.</p> <p> </p>
Data Set "Quantum-chemical calculation of two-dimensional infrared spectra using localized-mode VSCF/VCI"
<p>This data set accompanies the publication "Quantum-chemical calculation of two-dimensional infrared spectra using localized-mode VSCF/VCI"</p> <p>It contains:</p> <p>- xyz files of all considered molecular structures.</p> <p>- Results data from the harmonic and anharmonic vibrational calculations.</p> <p>- Data and code for calculating 2D-IR spectra.</p>
Insights from quantum chemical calculations into inner and outer-sphere complexation of plutonium(IV) by monoamide and carbamide extractants - Dataset
<p>Supplementary information for "Insights from quantum chemical calculations into inner and outer-sphere complexation of plutonium(IV) by monoamide and carbamide extractants"<br> The attached 'Pu-inner-outer.zip' unpacks two directories that contains the optimized molecular structures in xyz format.<br> - Inner-sphere/ for the inner-sphere complexes<br> - Outer-sphere/ for the outer-sphere complexes in their geometries I and II<br> </p>
Core Binding Energy Calculations: A Scalable Approach with the Quantum Embedding Based Equation-of-Motion Coupled-Cluster Method
<p>This data includes the HF-optimized orbitals, coupled cluster amplitudes (T1, T2), and EOM-CCSD left and right eigenvectors at the CC-PCVDZ basis set. It can be used to reproduce the data for "Core Binding Energy Calculations: A Scalable Approach with the Quantum Embedding Based Equation-of-Motion Coupled-Cluster Method."</p>
All input files for quantum calculations in VASP
<p>Here, all input files for quantum calculations in VASP have been uploaded. All the structure files extracted from MD simulations are in POSCAR format.</p>
Data from: Accurate geometries for "mountain pass" regions of the Ramachandran plot using quantum chemical calculations
Unusual local arrangements of protein in Ramachandran space is not well represented by standard geometry tools used in either protein structure refinement using simple harmonic geometry restraints or in protein simulations using molecular mechanics force fields. By contrast, quantum chemical computations using small poly-peptide molecular models can predict accurate geometries for any well-defined backbone Ramachandran orientation. For conformations along transition regions – ϕ from -60 to 60° – a very good agreement with representative high-resolution experimental X-ray (≤1.5 Å) protein structures is obtained for both backbone C^{-1}-N-C_{alpha} angle and the nonbonded O^{-1}…C distance, while "standard geometry" leads to the "clashing" of O…C atoms and Amber FF99SB predicts distances too large by about 0.15 Å. These results confirm that quantum chemistry computations add valuable support for detailed analysis of local structural arrangements in proteins, providing improved or missing data for less understood high-energy or unusual regions.
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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.