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69 results for “coarse-grain”
Coarse-grained simulations of lipid membranes with various concentrations of embedded proteins
<p>Membranes with a polydisperse set of proteins with lipid/protein ratios of 50 (lp50), 75 (lp75), 100 (lp100), 200 (lp200), and 400 (lp400) are simulated for 100 µs. The protein–protein interactions are scaled down to prevent excessive aggregation. </p> <p>For all simulated lipid/protein ratio, a trajectory (.xtc) file is stored every 10 ns. Note that water beads and ions have been removed for a smaller file size. All beads, including solvent ones, are present in initial and final structures (.gro). Energy terms are stored every 1 ns (.edr). Simulation run input files (.tpr) and checkpoint files (.cpt) enable the rerunning or continuation of the simulations. Topology (.top) and index (.ndx) files for each system are also provided.</p> <p>The topology files for scaled protein–protein interactions work as follows: The bead types of the proteins in their topology files (.itp) are changed to those with a 'p', for example P4–>P4p. The interactions between normal beads are listed in martini_v2.2_standard.itp, while the interactions among 'p' beads as well as the cross terms between 'p' beads and normal beads are given in martini_v2.2_scaled_80.itp. The force constant of the elastic network was increased to enable an integration time step of 20 fs.</p> <p>The lipid head groups are gently restrained in the direction normal to the membrane to prevent excessive fluctuations and they hence simulate the presence of an actin cytoskeleton.</p> <p>The simulation parameters, common for all systems, are listed in the file md.mdp. </p> <p>All files are compatible with Gromacs 5.0.</p> <p>The results extracted from these simulations are presented in the paper</p> <p>M. Javanainen, H. Martinez-Seara, R. Metzler, and I. Vattulainen; Diffusion of Integral Membrane Proteins in Protein-Rich Membranes. J. Phys. Chem. Lett., 2017, 8 (17), pp 4308–4313, DOI: 10.1021/acs.jpclett.7b01758</p> <p>The Martini topologies are obtained form http://cgmartini.nl</p>
ToPoRg-18k: dataset of single-chain radii of gyration distribution for 18,450 architecturally diverse and chemically patterned coarse-grained polymers
<blockquote> <p>Revision: This revision includes four independent trajectory values of the ensemble averages of the mean squared radii of gyration and their standard deviations, which can be used to compute statistical measures such as the standard error.</p> </blockquote> <p>This distribution provides access to 18,450 configurations of coarse-grained polymers. The data is provided as a serialized object using the `pickle' Python module and in csv format. The data was compiled using Python version 3.8. </p> <p><strong>References<br></strong>The specific applications and analyses of the data are described in <br>1. Jiang, S.; Webb, M.A. "Physics-Guided Neural Networks for Transferable Prediction of Polymer Properties"</p> <p><strong>Data<br></strong>There are seven .pickle files that contain serialized Python objects.</p> <ul> <li><strong>pattern_graph_data_*_*_rg_new.pickle</strong>: squared radii of gyration distribution from MD simulation. The number indicates the molecular weight range.</li> <li><strong>rg2_baseline_*_new.pickle</strong>: squared radii of gyration distribution from Gaussian chain theoretical prediction.</li> <li><strong>delta_data_v0314.pickle</strong>: torch_geometric training data.</li> </ul> <p><strong>Usage</strong><br>To access the data in the .pickle file, users can execute the following:</p> <blockquote> <p># LOAD SIMULATION DATA<br>DATA_DIR = "your/custom/dir/"<br>mw = 40 # or 90, 190 MWs</p> <p>filename = os.path.join(DATA_DIR, f"pattern_graph_data_{mw}_{mw+20}_rg_new.pickle")<br>with open(filename, "rb") as handle:<br> graph = pickle.load(handle)<br> label = pickle.load(handle)<br> desc = pickle.load(handle)<br> meta = pickle.load(handle)<br> mode = pickle.load(handle)<br> rg2_mean = pickle.load(handle)<br> rg2_std = pickle.load(handle) ** 0.5 # var</p> <p># combine asymmetric and symmetric star polymers<br>label[label == 'stara'] = 'star'<br># combine bottlebrush and other comb polymers<br>label[label == 'bottlebrush'] = 'comb' </p> <p># LOAD GAUSSIAN CHAIN THEORETICAL DATA<br>with open(os.path.join(DATA_DIR, f"rg2_baseline_{mw}_new.pickle"), "rb") as handle:<br> rg2_mean_theo = pickle.load(handle)[:, 0]<br> rg2_std_theo = pickle.load(handle)[:, 0]</p> </blockquote> <ul> <li><strong>graph</strong>: NetworkX graph representations of polymers.</li> <li><strong>label</strong>: Architectural classes of polymers (e.g., linear, cyclic, star, branch, comb, dendrimer).</li> <li><strong>desc</strong>: Topological descriptors (optional).</li> <li><strong>meta</strong>: Identifiers for unique architectures (optional).</li> <li><strong>mode</strong>: Identifiers for unique chemical patterns (optional).</li> <li><strong>rg2_mean</strong>: Mean squared radii of gyration from simulations.</li> <li><strong>rg2_std</strong>: Corresponding standard deviation from simulations.</li> <li><strong>rg2_mean_theo</strong>: Mean squared radii of gyration from theoretical models.</li> <li><strong>rg2_std_theo</strong>: Corresponding standard deviation from theoretical models.</li> </ul> <p><strong>Help, Suggestions, Corrections?</strong><br>If you need help, have suggestions, identify issues, or have corrections, please send your comments to Shengli Jiang at sj0161@princeton.edu</p> <p><strong>GitHub</strong><br>Additional data and code relevant for this study is additionally accessible at <a href="https://github.com/webbtheosim/gcgnn">https://github.com/webbtheosim/gcgnn</a> </p>
Coarse-grained simulations of the Sec61 and TRAP complexes in a multi-component membrane
<p>to be added</p>
Dataset for "Broad chemical transferability in structure-based coarse-graining"
<p>Dataset for "Broad chemical transferability in structure-based coarse-graining"</p>
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 with OpenMP not enabled<br>C++ standard: C++11</p> <p>Active compile time flags:<br>-DLAMMPS_GZIP<br>-DLAMMPS_SMALLBIG</p> <p>Installed packages:<br>CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</p> <p>Output quantities (columns of *.Dat files):<br>Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</p>
Coarse-grained simulations of cholesterol in symmetric lipid bilayers with varying degrees of lipid chain unsaturation
<p>Membranes consisting of 520 phospholipids varying levels of chain unsaturation together with 56 (10 mol%) cholesterol molecules were simulated at 298 K. The phospholipids had either 1 (DOPC), 2 (DLiPC), 4 (DAPC), or 6 (DDPC) double bonds in both of their chains. The Martini force field [1] was used and the membranes were generated using insane [2].</p> <p>The simulations were run for 75 microsecond using the GROMACS simulation suite [3]. Simulation parameters are found in the common mdp file (note that the temperature varies between simulations).</p> <p>The upload contains simulation inputs and outputs that allows the replication, extension, or analysis of the simulation data:</p> <ul> <li>Topology files (top) and molecular definitions (itp)</li> <li>Index files (ndx)</li> <li>A common run parameter file (mdp)</li> <li>A run input file (tpr)</li> <li>Trajectory file (xtc) written every 1 ns</li> <li>Energy file (edr) written every 100 ps</li> <li>Log file (log)</li> <li>Final structure file (gro)</li> <li>Continue point file (cpt)</li> </ul> <p>The files are named LLLL_CG_CHOLXX_TTT.FFF, where</p> <ul> <li>LLLL is the type of phospholipid</li> <li>CG stands for coarse-grained (All atom data in a separate upload)</li> <li>CHOLXX stands for the cholesterol concentration (CHOL10 for 10 mol%)</li> <li>TTT is the temperature</li> <li>FFF is the tile type (see above)</li> </ul> <p>Note that topologies/index files are the same regardless of temperature, and hence their file names do not have the TTT section.</p> <p>[1] <strong>DOI: </strong>10.1021/jp071097f</p> <p>[2] <strong>DOI: </strong>10.1021/acs.jctc.5b00209</p> <p>[3] <strong>DOI: </strong>10.1016/j.softx.2015.06.001</p>
Coarse-grain Models of HIV-1 Nucleoids
<p>These are PDB-format files that may be read by most molecular<br> graphics programs. The script JSmol.script will create a customized<br> view with appropriate radii, using JSmol. The PDB format is modified<br> in the following ways:<br> --coordinates are in nanometers<br> --RNA strands have atom name "P" and residue name "RNA"<br> --integrase subunits have atom name "CA" and residue name "IN"<br> --nucleocapsid subunits have atom name "N", and residue names<br> "NCO" for experimentally-observed positions and<br> "NCR" for randomly-placed positions</p> <p>Each file contains 25 instances of the nucleoid model.<br> Each model includes 2 gRNA strands, 2000 nucleocapsid subunits, and 140/0 integrase subunits.<br> Nine models are included in this release, exploring two types of variables:<br> 1) Three types of starting models: <br> "selfavoidingGag" assumes that the RNA forms a non-overlapping random walk on<br> the lattice of gag proteins in the immature virion<br> "overlappingGag" assumes a similar random walk, but the RNA strands may overlap<br> "random" assumes that RNA is released from gag and randomly fills the interior of<br> the virion before condensation <br> 2) Three assumptions about integrase interaction:<br> 35 integrase tetramers, 70 integrase dimers, and no integrase</p> <p>More information is available at:<br> http://ccsb.scripps.edu/latticenucleoid/HIVnucleoid</p>
Data for Morphological analysis of chiral rod clusters from a coarse-grained single-site chiral potential
<p>Data relating to the paper 'Morphological analysis of chiral rod clusters from a coarse-grained single-site chiral potential'. See the README for details.</p>
Coarse-grained simulation of a POPC lipid bilayer
<p>A POPC bilayer consisting of 200 lipids and 2250 water beads (10% represented by antifreeze particles) was simulated for 1 microsecond using GROMACS 2019.2. All simulation outputs, as well as topologies (top, itp), index file (ndx) and run input parameters (mdp) are provided.</p>
Coarse-grained simulation of a dilute lipid membranes with 7 protein types
<pre>A simulation of a DPPC lipid membrane with one copy of seven protein types. This is an extension to the dataset [1] with an even more dilute membrane. Topologies and mdp files can be obtained from [1], and all the simulation parameters are equal to those described in [1] and in the related publication [2]. The trajectory does not contain the solvent and is stored every 10 ns. [1] https://doi.org/10.5281/zenodo.846428</pre> <p>[2] M. Javanainen, H. Martinez-Seara, R. Metzler, and I. Vattulainen; Diffusion of Integral Membrane Proteins in Protein-Rich Membranes. J. Phys. Chem. Lett., 2017, 8 (17), pp 4308–4313, DOI: 10.1021/acs.jpclett.7b01758<br> </p>
Dataset for "Constructing many-body dissipative particle dynamics models of fluids from bottom-up coarse-graining"
<ul> <li>The trajectory files used for processing time correlation functions, as reported in the original paper, are provided here.</li> <li>The analysis tools can be found in the following GitHub repository: <a href="https://github.com/jaehyeokjin/ManyBodyDPD/tree/main/Time-Correlation" target="_new" rel="noopener">ManyBodyDPD/Time-Correlation</a>. These tools are designed to work with the two trajectory files included in this repository.</li> </ul>
Rheology of sliding leaflets in coarse-grained DSPC lipid bilayers accompanying files
<p>Molecular dynamics configuration files accompanying the manuscript<a href="https://arxiv.org/abs/2007.05784"> arXiv:2007.05784</a>, published in Physical Review E.</p>
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
<p>Training, test datasets of the paper "Chemically Transferable Generative Backmapping of Coarse-Grained Proteins".</p> <p>The PDB files are downloaded from PED [1] and processed according to the instruction described in our paper [link]. </p> <p> </p> <p>[1] <strong>PED in 2021: a major update of the Protein Ensemble Database for intrinsically disordered proteins</strong></p> <p>Lazar, T., Martı́nez-Pérez, E., Quaglia, F., Hatos, A., Chemes, L.B., Iserte, J.A., Méndez, N.A., Garrone, N.A., Saldaño, T.E., Marchetti, J., Velez Rueda, A.J., Bernadó, P., Blackledge, M., Cordeiro, T.N., Fagerberg, E., Forman-Kay, J.D., Fornasari, M.S., Gibson, T.J., Gomes, G-N.W., Gradinaru, C.C., Head-Gordon, T., Ringkjøbing Jensen, M., Lemke, E.A., Longhi, S., Marino-Buslje, C., Minervini, G., Mittag, T., Monzon, A.M., Pappu, R.V., Parisi, G., Ricard-Blum, S., Ruff, K.M., Salladini, E., Skepö, M., Svergun, D., Vallet, S.D., Varadi, M., Tompa, P., Tosatto, S.C.E., Piovesan D.</p> <p>(2021) <em>Nucleic Acids Research</em>, Database Issue, <a href="https://www.ncbi.nlm.nih.gov/pubmed/33305318"><strong>PubMed </strong><em>link</em></a>, <a href="https://academic.oup.com/nar/article/49/D1/D404/6030232"><strong>NAR </strong><em>link</em></a></p> <p> </p> <p> </p>
Data from: Evidence of local adaptation to fine- and coarse-grained environmental variability in Poa alpina in the Swiss Alps
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Coarse-grained simulations of a phase-separated membrane with different LINCS settings (part 2/3)
<p>systems with lincs_iter = 2. To be written.</p>
Coarse-grained simulations of a phase-separated membrane with different LINCS settings (part 1/3)
<p>systems with lincs_iter = 1. To be written.</p>
Coarse-grained simulations of a phase-separated membrane with different LINCS settings (part 3/3)
<p>Additional simulations with lincs_order 10 & 12</p>
Gas permeability through swelling porous media: Insights from coarse-grained pore-scale simulations
<p>CGMD data for gas transport in swelling porous media</p>
Dataset for "Microscopic Theory of Density Scaling: Coarse-Graining in Space and Time"
<p>Dataset for "Microscopic Theory of Density Scaling: Coarse-Graining in Space and Time"</p>
Dataset 1:Coarse-grained simulations of adeno-associated virus and its receptor reveal influences on membrane lipid organisation and curvature.
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