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69 results for “coarse-grain”
HADDOCK Coarse-Grained Protein-DNA Docking Dataset
<p>This dataset is the result of the benchmarking done in the publication <a href="https://www.frontiersin.org/articles/10.3389/fmolb.2019.00102/full"><em>MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing</em></a> in which HADDOCK's coarse-grain support is extended to Protein-DNA docking.</p> <p>It contains both the All-atom and Coarse-Grained output of 46 macromolecular complexes derived from the <a href="https://haddock.science.uu.nl/dna/benchmark.html">Protein-DNA benchmark</a> (<a href="https://doi.org/10.1093/nar/gkn386">10.1093/nar/gkn386</a>) as well as 6 complexes with experimental information, CAPRI Target 95 (PRC1 ubiquitination module bound to the nucleosome) and all new parameters added to HADDOCK.</p>
Research data for "Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining"
<p>This dataset supports the paper: "Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining", available at the following DOI: 10.1039/d0sc03287e.</p> <p>The cleaned-up, coarse-grained, and re-scaled structures are provided here in both XYZ and CIF format. The data presented in the journal publication is also included; namely, MDS coordinates, T densities, and A-site heterogeneities for each structure in the dataset. T densities -- defined as: metals per unit volume (nm-3) -- are calculated using the re-scaled structures.</p>
A bottom-up coarse-grained model for interactions of lipids with TiO2 nanoparticles
<p>Supplemetary information data to the paper:</p> <p>M.Ivanov and A.P.Lyubartsev, "Development of a bottom-up coarse-grained model for interactions of lipids with TiO<br> nanoparticles", J. Comput. Chem.m 2024. Doi: <a href="https://doi.org/10.1002/jcc.27310">10.1002/jcc.27310</a></p>
Dataset to "Fine-sediment erosion and ridge morphodynamics in coarse-grained immobile beds"
<p>The dataset is connected to the paper "Fine-sediment erosion and ridge morphodynamics in coarse-grained immobile beds".</p> <p>The dataset is subdivided in "topographic" and "PIV" data. Each dataset refers to an experiment presented in the paper.</p> <p>The filenames of the topographic data specify the location where the data were collected ("upstream" or "downstream"). The topographic data contain the bed elevations measured around each sphere over a measurement area as wide as the channel and covering nine patterns of spheres (see paper for a definition of the pattern of spheres). Each topographic dataset contains four structures:</p> <p>- A time vector "T" identifying the sampling times at which the bed topography was measured (unit: h)</p> <p>- A space vector "x" identifying the streamwise position of the spheres with the origin of the coordinate system positioned at the beginning of the measurement areas (upstream or downstream) - unit cm</p> <p>- A space vector "y" identifying the transverse position of the spheres with the coordinate system centred in the middle of the channel and positively oriented towards the left side of the flume looking in streamwise direction - unit cm</p> <p>- A matrix "Z" identifying the protrusion levels of the spheres (as defined in the paper) as a function of space and time (x times y times T) - unit cm</p> <p> </p> <p>The PIV data were acquired during experiments 1b and 5b at the upstream location. The data are a collection of the velocity fields measured above the top of the spheres and over two pattern of spheres in the middle "m" and in the quarter plane "q" of the channel for three different time instants (t1, t2, t3) during the experiments. Each dataset contains 6 variables:</p> <p>- the "sampling time" of the bursts - unit s</p> <p>- the "acquisition time" of the PIV run during the experiments - unit h</p> <p>- a vector "x" identifying the streamwise position of the velocity vectors for a coordinae system centred in the centre of the sphere and pointing in the longitudinal direction.</p> <p>- a vector "z" identifying the vertical direction of the coordinate system with the origin at the top of the sphere and pointing upwards - unit cm</p> <p>- a matrix "vx" containing 1000 fields of the streamwise velocity component - unit cm/s</p> <p>- a matrix "vz" containing 1000 fields of the vertical velocity component - unit cm/s</p>
Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework"
<p><strong>Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework"</strong></p> <p><br> GLE Optimization: Optimizator of GLE parameters. Uses Matlab</p> <p>MD_GLE: CG GLE and LE simulator. Uses Matlab</p> <p>Water_simulation: Folders for atomistic water system simulation with GROMACS. It requires to be run on Linux with GROMACS <br> and VOTCA packages installed.</p>
Scaling protein-water interactions in the Martini 3 coarse-grained force field to simulate transmembrane helix dimers in different lipid environments
<p>This dataset contains molecular dynamics (MD) trajectories used for preparation of the following manuscript: <br> "Scaling protein-water interactions in the Martini 3 coarse-grained force field to simulate transmembrane helix dimers in different lipid environments". </p>
Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces
<p>CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.</p>
BaTiO3 coarse-grained molecular dynamics simulations
<p>This repository contains the simulation results for BaTiO3 using coarse-grained molecular dynamics package <a title="Feram" href="https://loto.sourceforge.net/feram/" target="_blank" rel="noopener">Feram</a>.</p> <p>The files (1: data.avg, 2: *.csv) use the space-separated format or comma-separated format.</p> <p>(1) data.avg columns:<br>T: temperature in Kelvin<br>Ex Ey Ez: external_E_field along x,y,z in V/Angstrom.<br>exx eyy ezz eyz ezx exy: strain tensor<br>ux uy uz: dipole displacements in Angstrom<br>uxux uyuy uzuz uyuz uzux uxuy: cross-terms of dipole displacements in Angstrom^2<br>dk: dipo_kinetic in eV/u.c.<br>lr: long_range in eV/u.c.<br>dEf: dipole_E_field in V/Angstrom<br>unhar: unharmonic in eV/u.c.<br>s_ho: homo_strain in eV/u.c.<br>c_ho: homo_coupling in eV/u.c.<br>s_inho: inho_strain in eV/u.c.<br>c_inho: inho_coupling in eV/u.c.<br>etot: total energy in eV/u.c.<br>HNP: H_Nose_Poincare in eV/u.c.<br>e2: e2<br>dkt: dipo_kinetic_true in eV/u.c.<br>ak: acuou_kinetic in eV/u.c.<br>sr: short_range in eV/u.c.<br>mod: inho_modulation in eV/u.c.<br>px py pz: px py pz<br>ppx ppy ppz ppyz ppzx ppxy: ppx ppy ppz ppyz ppzx ppxy<br>mx my mz: <ux>, <uy>, <uz> in Angstrom<br>amx amy amz: <|ux|>, <|uy|>, <|uz|> in Angstrom</p> <p>(2) *.csv contains header in each file.</p> <p>(3) avg2csv.ipynb contains script to convert files.</p>
Research data supporting: "A millisecond coarse-grained simulation approach to decipher allosteric cannabinoid binding at the glycine receptor α1"
<p>This repository contains the set of coarse-grained (CG) m<span>olecular snapshots representative </span><span>of the most populated binding modes for glycine receptor (GlyR) in complex with the ligands, along with atomistic backmapped representations. In addition, it contains also CG Martini 3 parameters, charmm36-FF backmapping library file and GROMACS files (itp and gro files) for both ligands.</span></p>
Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics: dataset
<div><strong>Abstract:</strong></div> <div>(from [1])</div> <div>Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead-spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions. </div> <div> </div> <div> </div> <div><strong>Contact:</strong></div> <div>Felix Weber</div> <div>Institute of Applied Mechanics</div> <div>Friedrich-Alexander-Universität Erlangen-Nürnberg</div> <div>Egerlandstr. 5</div> <div>91058 Erlangen</div> <div>Germany</div> <div> </div> <div> </div> <div><strong>Software:</strong></div> <div>All simulations were performed with LAMMPS [2,3] (version 23 June 2022, patch_23Jun2022_update3) </div> <div> </div> <div>Compiler: GNU C++ 11.2.0 with OpenMP not enabled</div> <div>C++ standard: C++11</div> <div> </div> <div>Active compile time flags:</div> <div>-DLAMMPS_GZIP</div> <div>-DLAMMPS_SMALLBIG</div> <div> </div> <div>Installed packages:</div> <div>BPM CLASS2 DPD-BASIC EXTRA-DUMP EXTRA-FIX EXTRA-MOLECULE INTEL KSPACE MANYBODY </div> <div>MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT </div> <div> </div> <div>Moreover, we employ a self-avoiding random walker [4,5] implemented in MATLAB [6] for the initial positioning of the polymer chains and nanoparticles.</div> <div> </div> <div> </div> <div><strong>License:</strong></div> <div>Creative Commons Attribution 4.0 International</div> <div> </div> <div> </div> <div><strong>Context:</strong></div> <div>This dataset contains the results presented in [1] and the necessary data to obtain those.</div> <div> </div> <div> </div> <div><strong>Content:</strong></div> <div>Throughout this data set, LAMMPS lj units are used. The files to reproduce our simulations and their results are structured as follows:</div> <div>- 01_neat: Neat polymer systems</div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations, including the notch insertion (token "initcrack")</div> <div> - 1.1: Simulations for different sample sizes/numbers of chains (token "chains") at constant molar mass/number of beads per chain</div> <div> - 1.3: Simulations for different widths of the Dirichlet boundary (token "diri")</div> <div> - 2.1: Simulations for different critical bond lengths (token "bondcrit")</div> <div> - 2.2: Simulations for different bond breaking probabilities (token "bondcprob")</div> <div> - 3.1: Simulations for different crack widths (token "crackwidth")</div> <div> - 3.2: Simulations for different crack lengths (token "crackdepth")</div> <div> - 4: Simulations for different strain rates (token "strainrate")</div> <div> - 5: Simulations for different temperatures (token "tem")</div> <div> - 6: Simulations for different molar masses/numbers of beads per chain (token "chain-len")</div> <div>- 02_PNC: Polymer nanocomposite (PNC) systems </div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations for different filler radii (token "rF") and filler contents/numbers (token "nF"), including the notch insertion (token "initcrack")</div> <div>- parameter_study: Postprocessing of the MD results </div> <div> - parameter_study.xlsx: Overview of the simulations with their respective parameters and statistical analysis of stiffness, strength, and toughness from filtered stress-strain curves (Savitzky-Golay filter applying a linear polynomial and frame length 21)</div> <div> - .csv files of the single sheets of parameter_study.xlsx:</div> <div> - samples.csv: Individual specimens</div> <div> - averages.csv: Statistical analysis of the different samples corresponding to one batch</div> <div> </div> <div>Each simulation directory contains:</div> <div>- LAMMPS input script (*.in) of the simulation</div> <div>- input.prm: Input parameters of the simulation (read by the input script)</div> <div>- LAMMPS data file (*.data, molecular style) of the investigated sample</div> <div>- LAMMPS_out: Resulting LAMMPS data files, log files and simulation results in tabulated form</div> <div> - additional files for the tensile tests: </div> <div> - brokenbonds.dat: Fix print output for fix brokenbondsprint (step time brokenbondsPerStep brokenbondsSum)</div> <div> - stressstrain.dat: Time-averaged data for fix dumpOpt (step v_strain_xx v_OBSstrain_xx v_Piola_xx) with the local strain at the crack tip v_OBSstrain_xx</div> <div> - thermo_out.Dat: Thermodynamic output in condensed tabulated form</div> <div> - thermo_out_SG.Dat: Thermodynamic output in condensed tabulated form, filtered by a Savitzky-Golay filter (linear polynomial, frame length 21)</div> <div> - thermo_out_STD.Dat: Standard deviation between the filtered and unfiltered data</div> <div>- job.out: Simulation log file</div> <div>- meta.info: Meta data of the simulation run</div> <div> </div> <div>Naming convention:</div> <div>- 01_neat: GTPm-[number of chains]_chains-[number of beads per chain]_chain_len-[temperature]_tem-[parameter value]_[parameter]-[sample]</div> <div> - [parameter]: Parameter studied, i.e. diri/bondcrit/bondcprob/crackwidth/crackdepth/strainrate/tem (see above)</div> <div> - [parameter value]: Value of the parameter studied</div> <div> - [sample]: Sample ID</div> <div>- 02_PNC: GTPm_rF-[filler radius]_nF-[number of fillers]_[sample]</div> <div> - [sample]: Sample ID</div> <div> </div> <div>Output quantities (columns of *.Dat files):</div> <div>- Step: time step</div> <div>- Time: time</div> <div>- TotEng: total energy</div> <div>- PotEng: potential energy</div> <div>- KinEng: kinetic energy</div> <div>- E_pair: pair energy</div> <div>- E_bond: bond energy</div> <div>- E_angle: angle energy</div> <div>- E_dihed: dihedral energy</div> <div>- Temp: temperature</div> <div>- Press: hydrostatic pressure</div> <div>- Pxx: xx component of pressure tensor</div> <div>- Pyy: yy component of pressure tensor</div> <div>- Pzz: zz component of pressure tensor</div> <div>- Pxy: xy component of pressure tensor</div> <div>- Pxz: xz component of pressure tensor</div> <div>- Pyz: yz component of pressure tensor</div> <div>- Volume: volume of simulation box</div> <div>- Lx: box length in x direction</div> <div>- Ly: box length in y direction</div> <div>- Lz: box length in z direction</div> <div>- Density: mass density</div> <div>- c_RG: radius of gyration</div> <div>- c_RG[1]: squared radius of gyration tensor (xx component)</div> <div>- c_RG[2]: squared radius of gyration tensor (yy component)</div> <div>- c_RG[3]: squared radius of gyration tensor (zz component)</div> <div>- c_RG[4]: squared radius of gyration tensor (xy component)</div> <div>- c_RG[5]: squared radius of gyration tensor (xz component)</div> <div>- c_RG[6]: squared radius of gyration tensor (yz component)</div> <div>- c_bondave[1]: bond energy averaged over all atoms</div> <div>- c_bondave[2]: bond distance averaged over all atoms</div> <div>- c_bondave[3]: squared bond distance averaged over all atoms</div> <div>- c_angleave[1]: angle energy averaged over all atoms</div> <div>- c_angleave[2]: angle averaged over all atoms degree</div> <div>- c_angleave[3]: cosine of angle</div> <div>- c_angleave[4]: squared cosine of angle</div> <div>- c_MSD[1]: mean squared displacement x-direction</div> <div>- c_MSD[2]: mean squared displacement y-direction</div> <div>- c_MSD[3]: mean squared displacement z-direction</div> <div>- c_MSD[4]: total mean squared displacement</div> <div>- c_COM[1]: x coordinate of center of mass</div> <div>- c_COM[2]: y coordinate of center of mass</div> <div>- c_COM[3]: z coordinate of center of mass</div> <div>- v_strain_xx: xx component of engineering strain tensor </div> <div>- v_strain_yy: yy component of engineering strain tensor </div> <div>- v_strain_zz: zz component of engineering strain tensor </div> <div>- v_vMisesequivstress: von Mises equivalent stress</div> <div>- v_Piola_xx: xx component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yy: yy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_zz: zz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xy: xy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xz: xz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yz: yz component of the virial stress tensor normalized by the initial volume</div> <div>- v_strain_xy: xy component of engineering strain tensor </div> <div>- v_strain_xz: xz component of engineering strain tensor </div> <div>- v_strain_yz: yz component of engineering strain tensor </div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div>[1] F. Weber, V. Dötschel, P. Steinmann, S. Pfaller, M. Ries, "Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics", Engineering Fracture Mechanics, vol. 307, p. 110270, 2024.</div> <div>[2] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics", Journal of computational physics, vol. 117, no. 1, pp. 1-19, 1995.</div> <div>[3] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "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.</div> <div>[4] V. Dötschel, S. Pfaller, and M. Ries, "Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model", Polymers and Polymer Composites, vol. 31, pp. 1–11, 2023.</div> <div>[5] M. Ries, V. Dötschel, J. Seibert, and S. Pfaller, A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites, Zenodo, 2022, https://doi.org/10.5281/zenodo.6245699.</div> <div>[6] The MathWorks, Inc., "Matlab. the language of technical computing", https://de.mathworks.com/help/matlab/.</div> <div> </div> <div> </div> <div><strong>Funding:</strong></div> <div>The authors gratefully acknowledge funding by various sources:</div> <div>The overall research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/2-2023. Sebastian Pfaller is furthermore funded by the DFG projects 396414850 (Individual Research Grant 'Identifikation von Interphaseneigenschaften in Nanokompositen') and 505866713 together with the Agence nationale de la recherché (ANR, French Research Agency) – ANR-22-CE92-0049 (Individuel Research Grant 'BIO ART'). In addition, scientific support and HPC resources have been provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b136dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG project 440719683.</div>
Data for: Coarse-Grained Simulations of Columnar Ionic Liquid Crystals: Comparison with Experiments
<p>This repository contains the python scripts required for running coarse-grained simulations of columnar ionic liquid crystals, as detailed in the corresponding manuscript. The scripts have been tested with HOOMD-blue v.2.9.7 and python v.3.9.</p> <ul> <li>The script "equilibration_run_npt.py" performs the initial equilibration in the NPT ensemble using an isotropic barostat at atmospheric pressure.</li> <li>The script "production_run_nvt.py" performs the production run in the NVT ensemble.</li> </ul> <p>Both scripts take two input parameters from the command line, that is the name of the start configuration and the temperature T. For example, use the command "python3 production_run_nvt.py ILC8_303.0K_end_production.gsd 303" to load the file "ILC8_303.0K_end_production.gsd" and perform a production run at T=303K.</p> <p>In addition, we have included the final snapshots of our simulations in the .gsd format (https://gsd.readthedocs.io/). The file name indicates the specific ILC molecule and temperature, at which it was simulated. For example, the file "ILC8_303.0K_end_production.gsd" contains the final frame of the NVT simulations of the molecule ILC-8 at temperature T=303K.</p>
Experimental data of an article on the impact of coarse-grain protrusion on near-bed hydrodynamics
<p>This deposit contains experimental data related to the article entitled " impact of coarse-grain protrusion on near-bed hydrodynamics" that will be publised in Journal of Geophysical Research - Earth surface. It consists in post-processed data (turbulence statistics) for flows around hemispheres protruding from a bed of fine sediments. Calculated statistics (time-averaged and double-averaged) are provided along other inferred quantities (friction velocity, etc...) and experimental parameters (protrusion level, discharge, etc..) in matlab metadata format. Matlab functions are given to plot all the figures of the article.</p>
Drug-membrane transfer free energies for coarse-grained trimers and tetramers
<p>The databases contain drug-membrane transfer free energies for coarse-grained Martini trimers and tetramers inserted in a one-component DOPC membrane. We also provide a database of atomistic-resolution compounds mined from the GDB and that map to trimers.</p>
Resolution limit of data-driven coarse-grained models spanning chemical space
<p>This repository contains all databases referenced in the supporting information of the paper titled "Resolution limit of data-driven coarse-grained models spanning chemical space" by Kiran H. Kanekal and Tristan Bereau.</p>
Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A Structure-Based Framework for Selective Inhibitor Design and Optimization
<p><span>Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named </span><span>Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models.</span> <span>Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.<br><br>This project provide the source dataset used to train and evaluate the overall model.<br></span></p>
Comparative performance of coarse-grained IDP models at different resolutions
<p>Files for simulations. Molsim start scripts and final outputs, MARTINI Stark start scripts (including force field files, and some intermediate data). Not all intermediate data available, but all data necessary to reproduce simulations should be included.</p> <p>MARTINI simulations follow the procedure "Dry energy minimization" -> "Solvation" -> "Salting" -> "Energy minimization" -> "NVT equilibration" -> "NPT equilibration" -> "Production Run", where the output from the previous step is the input into the next step. For some folders, the output from previous step has been copied, but not always. In case of another simulation, but using a bigger box, some files from the simulation with smaller box were re-used. Thus, if a file seems to be missing, check the folder with corresponding simulation using a smaller box.</p> <p>Some "analysis files" are included, e.g. the radius of gyration for each time step, in order to allow for the reproduction of graphs in upcoming publication, and further exploration.</p>
Project files provided as supporting information to the manuscript "Kinetics of radiation-induced DNA double-strand breaks through coarse-grained simulations"
<p><strong>README file to the project files provided as supporting information to the manuscript “Kinetics of radiation-induced DNA double-strand breaks through coarse-grained simulations"</strong></p> <p>Authors: Manuel Micheloni, Lorenzo Petrolli, Gianluca Lattanzi and Raffaello Potestio<br> ==================================</p> <p>The .zip file is structured as follows:</p> <p>##############<br> 0_DNA_Sequence<br> ##############</p> <p>The folder contains:<br> • DNAsequence.txt: the DNA molecule employed in the study. <br> • ecseq.txt: the structure of the DNA. Each nucleotide (LAMMPS residue ID) is associated with the chemical type of the nucleic base.</p> <p>###################<br> 1_DSB_MDSimulations<br> ###################</p> <p>• MDSimulations: <br> The LAMMPS MD simulation scripts.<br> The subfolders are structured and named after different DSB motifs (folders 0,1, …,3) providing different external forces (folders 1000, 1100, 1200 refers to the respective average end-to-end distance). The latter subfolders provides:</p> <p>I. LMP_script provides the LAMMPS input file.</p> <p>II. the local internal energy contributions from the nucleotides involved with the residual contact interface between broken DNA moieties (data/1.E); </p> <p>III. coordinates of the nucleotides involved with the residual contact interface between the DNA moieties (data/2.POS);</p> <p>IV. the binary LAMMPS data file, employed as starting coordinates for the DSB MD simulation (/input_structure).</p> <p><br> • ScriptsAndAnalysis:<br> The scripts employed to analyze the DSB process. </p> <p>I. /00_GeneralScripts all the MATLAB functions employed for the analysis.</p> <p>II. /1_SigmoidalFitting provides the scripts from a sigmoidal fitting procedure [1] on the internal energy profile of the nucleotides between the strand breaks (Section 2 of the Supplementary Material). <br> [1] R P (2022). sigm_fit (https://www.mathworks.com/matlabcentral/fileexchange/42641-sigm_fit), MATLAB Central File Exchange. Retrieved July 2, 2022.</p> <p><br> ##################<br> 2_DNAFreeDiffusion<br> ##################</p> <p>• SIM:<br> It contains the MD simulations of the freely-diffusing DNA. Namely, the MSD of the DNA molecule, the input structure, and the LAMMPS simulation scripts are reported in /data, /input_structure, and /LMP_scripts respectively.</p> <p><br> • ANALYSIS:<br> It provides the script employed to estimate the diffusion coefficient of the DNA molecule and the time-scaling factor \Gamma(\zeta).</p> <p>#######################<br> 3_ForceAnalysis<br> #######################</p> <p>• data<br> It contains the forces computed from the MD simulations of the intact DNA molecules subject to the external force of 0.42, 0.88 and 3.06 pN. All data are stored in pickle format.<br> The README file contains additional information.</p> <p><br> NOTE1: Most data/scripts are saved according to the format .mat, employed by MATLAB Ⓡ, a numerical computing environment and proprietary programming language developed by MathWorks.</p> <p>NOTE2: For the force data manipulation, we acknowledge the use of LammpsFileManipulation package (https://pypi.org/project/LammpsFileManipulation/)</p>
Tests of Martini3 coarse-grained triglycerides
<p>LTF: Initial tests of Martini coarse-grained triglycerides triolein and trimyristin together with atomistic reference simulations</p>
Data from: Evidence of local adaptation to fine- and coarse-grained environmental variability in Poa alpina in the Swiss Alps
In the alpine landscape, characterized by high spatiotemporal heterogeneity and barriers, divergent selection is likely to lead to local adaptation of plant populations either through adaptive genetic differentiation or through phenotypic plasticity. The relative importance of these processes has rarely been investigated in relation to the spatial scale of environmental heterogeneity. In this study, we used reciprocal transplantation experiments of populations across nearby and distant field sites to shed light on these complementary processes. We reciprocally transplanted populations of the widespread alpine grass, Poa alpina, within and across regions in the Swiss Alps. We inferred local adaptation at the metapopulation level by comparing fitness of plants transplanted to their site of origin and to nearby or distant novel sites. Additionally, we measured specific leaf area (SLA) and performed selection analyses to investigate directional selection on mean trait value at each field site and on the degree of plasticity of this trait to assess whether plastic responses were adaptive. In parallel, all populations were genotyped with microsatellite markers to assess neutral molecular differentiation. Molecular differentiation was high among populations within and among regions, indicating restricted gene flow among P. alpina populations. Reproductive biomass was highest in individuals grown in their region of origin, revealing local adaptation to coarse-grained environmental variability. Similarly, inflorescence height, associated with reproductive biomass, reflected adaptation to fine- and coarse-grained environmental variability. Furthermore, we found evidence that plasticity in SLA across coarse-grained habitats was correlated with plant fitness, suggesting that plasticity in this trait is adaptive. Synthesis. Our results revealed adaptive genetic differentiation between P. alpina populations in the Swiss Alps reflecting local adaptation. Furthermore, high phenotypic plasticity in SLA contributed to the maintenance of fitness homoeostasis across habitats. Hence, adaptive genetic differentiation and phenotypic plasticity play a complementary role for adaption of P. alpina to environmental heterogeneity in the Swiss Alps and both may be critical to mitigate local extinction risk under rapid climate change.
Coarse-grained Simulation data (part 2 of 2) for "Long-chain GM1 gangliosides alter transmembrane domain registration through interdigitation"
<p>Coarse-grained simulation data for the paper "Long-chain GM1 gangliosides alter transmembrane domain registration through interdigitation", Biochimica et Biophysica Acta (BBA) - Biomembrane Volume 1859, Issue 5, May 2017, Pages 870–87, DOI: 10.1016/j.bbamem.2017.01.033</p> <p>The simulations were performed using GROMACS 5.0.x [1]. The Martini force field [2,3] was employed.</p> <p>This part (2/2) of the upload contains data for the 10 replica simulations of the systems with GM1 with a normal tail and of the systems with a smaller concentration (1.5%) of GM1 with an extended tail. Trajectories, each 10 microseconds long and stored every 1ns, are given in xtc format. A common run input file for each type of system is given in the tpr format for analysis, although the initial structures of the replicas are different. These files are compatible with GROMACS 5. A common index file in ndx format and a common topology in top format are given for each system type. Simulation parameters, common for all simulated systems, are given in the mdp file. The itp files can be obtained from the Martini homepage http://cgmartini.nl/ </p> <p>The file names denote what kind of tail was used (normal vs. extended), the concentration of GM1 (1.5% (1) vs. 6% (6)), and the replica simulations are numbered from 1 to 10. For the systems without GM1 (NoGM1), the concentration is obviously not given, and there are only 8 replicas (see part 1).</p> <p>Part 1 of this upload is available at https://doi.org/10.5281/zenodo.831672.</p> <p>[1] M.J. Abraham et al., GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015, 1–2, pp. 19–25, DOI: 10.1016/j.softx.2015.06.001 </p> <p>[2] S.J. Marrink et al., The MARTINI Force Field:  Coarse Grained Model for Biomolecular Simulations. J. Phys. Chem. B, 2007, 111, pp 7812–7824, DOI:<strong> </strong>10.1021/jp071097f</p> <p>[3] C.A. López et al., Martini Force Field Parameters for Glycolipids. J. Chem. Theory Comput., 2013, 9, pp 1694–1708, DOI: 10.1021/ct3009655 </p>
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