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17 results for “water molecules”
GFN2-xTB structures of iCOM adsorbed on a cluster model of water molecules derived from a periodic model of crystalline ice
<p>This dataset contains the atomic coordinates in the <a href="http://www.moldraw.unito.it/">.</a>xyz format of the GFN2-xTB optimized structures of 20 iCOMs adsorbed at the surface of a cluster of 84 water molecules mimicking the periodic model of crystalline water icy grain as described by Ferrero, S.; Zamirri, L., Ceccarelli, C.; Witzel, A.; Rimola, A.; Ugliengo, P. ApJ, (2020) 904:11. For all considered structures we also provided a specific file in the Gaussian format with the computed harmonic frequencies. Each file can be easily converted in input for the variety of quantum mechanical programs, like VASP, QE, Gaussian 16 etc.</p> <p> </p> <p> </p>
How many water molecules does it take to dissociate HCl?
<p>The potential energy surface of HCl/water clusters with two water molecules, with molecular geometry and vibrational frequencies obtained at the CCSD(T) level using a Def2-TZVP basis.</p>
The proteolytic cleavage of TLR8 Z-loop by furin protease - molecular recognition, reaction mechanism and role of water molecules DATASET_v2
<p>The dataset comprises:<br>i) AlphaFold-Multimer predictions for TLR8LRR-furin complex<br>ii) The optimised structures of QM cluster models for reactant (RE), intermediate1-3 (INT1-INT3), and product (PROD)<br>iii) The optimised structures of QM/MM model for RE, INT1-INT3, PROD<br>iv) Input structures used in MD simulations and parameterization files for non-standard residues for RE, INT1-INT3, PROD<br>v) PyMOL sessions from AQUA-DUCT calculations for RE, INT1-INT3, PROD</p>
Training and validation data for MOF-801(Zr) with adsorbed water molecules.
<p>We employ MACE 0.3.5 (github.com/acesuit/mace) to train an ML potential to the extended XYZ file `data.xyz`, which contains atomic geometries and potential energy and force labels. The system is MOF-801(Zr) at various water loadings. Data was generated in an active learning fashion using psiflow (github.com/molmod/psiflow).</p>
Aligning of water molecules into proton-conducing transmembrane water wires by oxygen atoms of phospholipid ester linkers
<p>A media AVI file that shows how oxygen atoms of ester linkers of the two converging phospholipid molecules form an "oxygen passage" along which water molecules align in a proton-conducting wire. Further details could be found in our article</p>
Investigation of the behavior of dioxadet molecules in water by molecular dynamics
<p>A set of files for all-atom molecular dynamic simulations of a dioxadet molecule in water for four parametrizations obtained with a number of tools: ATBuilder, Amber tools, and Swiss Parameters.</p>
Progeny Project MW1 surfactant at water-vacuum interface simulation data, 1 to 48 molecules per surface
<p>"MW1" terthiophene based surfactant molecule at the water-vacuum interface from the PROGENY project ,<br> gromacs 2021.2 input and output.<br> From the trajectories contained in this archive density profiles and surface tensions can be extracted.</p> <p>Different number of molecules per surface in each sub directory. See Readme.txt.</p>
Progeny Project MW1 surfactant at water-vacuum interface simulation data, 60 to 154 molecules per surface
<p>"MW1" terthiophene based surfactant molecule at the water-vacuum interface from the PROGENY project ,<br> gromacs 2021.2 input and output.<br> From the trajectories contained in this archive density profiles and surface tensions can be extracted.</p> <p>Different number of molecules per surface in each sub directory. See Readme.txt.</p>
Progeny Project MW1 surfactant at water-vacuum interface simulation data, 161 to 175 molecules per surface
<p>"MW1" terthiophene based surfactant molecule at the water-vacuum interface from the PROGENY project ,<br> gromacs 2021.2 input and output.<br> From the trajectories contained in this archive density profiles and surface tensions can be extracted.</p> <p>Different number of molecules per surface in each sub directory. See Readme.txt.</p>
32 Water molecule simulation
<p>This is a microcanonical ensemble (NVE) simulation of 32 water molecules at a temperature<br> of 300 K, approximated with the Lennard-Jones force-field, using cp2k. The physical simulation is done employing a step-size of 0.1 fs for 100,000 steps. The simulation is done without periodic spatial boundaries in order to have a<br> system that fundamentally evolves over time.</p>
256 DPPC Molecules bilayer in pure Water, simulated at 288K (gel) or 358K (fluid)
<p><strong>Publication:</strong> MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes</p> <p><strong>Published on:</strong> 08 April 2021</p> <p><strong>Journal</strong>: <em><a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.26508">J Comp Chem</a>, </em>2021, DOI: 10.1002/jcc.26508</p> <p><strong>Description</strong>: Simulation files used to train our Python module to identify the thermodynamic phase of individual lipid molecules in a bilayer, as well as the simulation files analysed by the machine learning models. More information on the module can be found on <a href="https://vivien-walter.github.io/mllpa/">its website</a>.</p> <p>The training files are named dppc_gel.gro and dppc_fluid.gro. They respectively correspond to the final frame of the systems simulated at 288K and 358K. All other files are the files analysed by the module.</p> <p><strong>System composition:</strong></p> <ul> <li>DPPC molecules: 256 with 130 atoms each</li> <li>Water molecules: 42,492 with 3 atoms each </li> <li>Simulation box dimensions (approx.): 9 x 9 x 20 nm</li> </ul> <p><strong>Simulation details:</strong></p> <ul> <li>Software: Gromacs (v. 2020)</li> <li>Forcefield: Charmm36 (v. June 2015) - Water: TIP3P</li> <li>Thermostat: Nose-hoover (0.4ps, 2 groups)</li> <li>Barostat: Parrinello-Rahman semi-isotropic (2.0ps, 1.0 bar on each axis, 4.5e-5 bar-1)</li> <li>Duration: 25 ns (after stabilisation)</li> </ul>
Effect of THz spectra of L-Arginine molecules by the combination of water molecules
<p>Programs for processing terahertz spectroscopy</p>
Enthalpic classification of water molecules in target-ligand binding
<p>This repository contains supporting data for the manuscript entitled: Enthalpic classification of water molecules in target-ligand binding.</p>
Solid-state NMR data set for SI of "Probing a Hydrogen-π Interaction Involving a Trapped Water Molecule in the Solid State"
<p>These datasets are part of the Supplementary Information of "Probing a Hydrogen-π Interaction Involving a Trapped Water Molecule in the Solid State". All experimental details are given in the mentioned document.</p>
212 DPPC Molecules bilayer in pure Water, simulated at temperatures ranging from 288K to 358K
<p><strong>Publication:</strong> A machine learning study of the two states model for lipid bilayer phase transitions</p> <p><strong>Published on:</strong> 12 August 2020</p> <p><strong>Journal</strong>: <a href="https://pubs.rsc.org/en/content/articlelanding/2020/cp/d0cp02058c?fbclid=IwAR1Nzre773-uWrlf4vpc7dzEGoQYeWxSKPESSE9BNA3VGeg6xvBdkFOvEo0#!divAbstract"><em>Phys. Chem. Chem. Phys.</em></a>, 2020, DOI: 10.1039/D0CP02058C</p> <p><strong>Description</strong>: Simulation files used to train our machine learning algorithm to identify the thermodynamic phase of individual lipid molecules in a bilayer, as well as the simulation files analysed by the machine learning models. Code source for the ML algorithm can be found on <a href="https://github.com/vivien-walter/mllpa">Github</a>.</p> <p>The training files are named gel.gro and fluid.gro. They respectively correspond to the final frame of the systems simulated at 288K and 358K.</p> <p>All other files are the files analysed by the machine learning models.</p> <p><strong>System composition:</strong></p> <ul> <li>DPPC molecules: 212 with 130 atoms each</li> <li>Water molecules: 29,826 with 3 atoms each </li> <li>Simulation box dimensions (approx.): 8 x 8 x 20 nm</li> </ul> <p><strong>Simulation details:</strong></p> <ul> <li>Software: Gromacs (v. 2016.4)</li> <li>Forcefield: Charmm36 (v. June 2015) - Water: TIP3P</li> <li>Thermostat: Nose-hoover (0.4ps, 2 groups)</li> <li>Barostat: Parrinello-Rahman semi-isotropic (2.0ps, 1.0 bar on each axis, 4.5e-5 bar-1)</li> <li>Duration: 50 ns</li> </ul>
Data from: Experimental measurements of water molecule binding energies for the second and third solvation shells of [Ca(H2O)n]2+ complexes
Further understanding of the biological role of the Ca2+ ion in an aqueous environment requires quantitative measurements of both the short- and long-range interactions experienced by the ion in an aqueous medium. Here, we present experimental measurements of binding energies for water molecules occupying the second and, quite possibly, the third solvation shell surrounding a central Ca2+ ion in [Ca(H2O)n]2+ complexes. Results for these large, previously inaccessible, complexes have come from the application of finite heat bath theory to kinetic energy measurements following unimolecular decay. Even at n = 20, the results show water molecules to be more strongly bound to Ca2+ than would be expected just from the presence of an extended network of hydrogen bonds. For n > 10, there is very good agreement between the experimental binding energies and recently published density functional theory calculations. Comparisons are made with similar data recorded for [Ca(NH3)n]2+ and [Ca(CH3OH)n]2+ complexes.
Data from: Experimental measurements of water molecule binding energies for the second and third solvation shells of [Ca(H2O)n]2+ complexes
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