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691 results for “Molecular dynamics”
Molecular dynamics simulations data of Caspase-3 enzyme with pentapeptide ligand DEVDG and its chiral mutant DEVdG having D-Asp at fourth position
<p>Amino acids in proteins are maintained in one specific L chiral form in the body. D-amino acids are not normally incorporated into proteins and their accumulation has been associated with several conditions including schizophrenia, amyotrophic lateral sclerosis, and other age-related disorders. However, the mechanisms by which the accumulation of D-amino-acids in proteins may lead to pathophysiological consequences remain poorly understood. In this work, we studied a model protease system, caspase-3 that specifically hydrolyses the 4’–5’ peptide bond of the pentapeptide substrate DEVDG. Through extensive molecular dynamics simulations, free energy calculations and distance maps, we reveal that caspase-3 naturally rejects the pentapeptide containing D-Asp substrate, DEVdG and prevents catalytic activity by caspase. The importance of this chiral discriminating capacity is evident from chiral-selective in vivo experimental assays to detect caspase-bound D-Asp in Drosophila where altering the chiral balance created impaired caspase activity and impaired apoptosis, increased tumour formation, and premature death. The modelling data reveals the molecular level charge balancing that enforces the chiral recognition necessary to maintain homeostasis across the cell, tissue, and organ level.</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>
Data from "Allostery and evolution: a molecular journey throught the structural and dynamical landscape of an enzyme super family."
<p>This data accompanies the paper entitled Allostery and evolution: a molecular journey throught the structural and dynamical landscape of an enzyme super family.</p> <p>The zip archive contains: </p> <p>1- Starting configurations of the proteins after equilibration in PDB format and trajectories of unrestrained molecular dynamics simulations with the positions of the proteins every 100 ps in XTC gromacs format are provided for all systems. </p> <p>2- The free energy profiles and histograms are provided for all umbrella sampling simulations and the scripts used to run it with gromacs.</p>
Advanced Molecular Dynamics Model for Investigating Biological-Origin Microfibril Structures
<p>This data contains all necessary input file to construct the micro fibril.</p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (parameters, trajectories)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7. <br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p>
Molecular Dynamics (MD) Simulation Data for Dynamics Underlie the Drug Recognition Mechanism by the Efflux Transporter EmrE
<p>MD simulations on the proton bound (PDB 8UWU), deprotonated on E14A (PDB 8UWU), TPP Bound (PDB 8UWU) on our NMR derived structures.</p> <p> </p> <p>MD simulations on the proton bound (7MH6) and deprotonated on E14A (7MH6) on X-ray structures. </p> <p> </p> <p>Total raw simulation data would be too large for uploading to repositories. To reduce size of file, starting structure and tpr files are uploaded. Final structure at 2.5 μs are also uploaded. </p>
Data supporting: "Interaction of MRI Contrast Agent [Gd(DOTA)]− with Lipid Membranes: A Molecular Dynamics Study"
Open the record for dataset details and reuse information.
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>
Molecular dynamics trajectories and portable binary run files for the self-assembly of heparin and amyloid-β(16-22) with the ProMPT forcefield
<h1>About this repository</h1> <p>Self-assembly simulations of heparin and amyloid-β(16-22) were performed at various numbers of peptides (<em>N_pep</em>), number of heparin molecules (<em>N_hep</em>), degrees of polymerization of heparin (<em>hep_dp</em>) and rigidity factors (<em>rig_f</em>) for heparin. This repository contains one folder per system, characterized by a combination of four system variables: <em>N_pep</em>, <em>N_hep</em>, <em>hep_dp</em> and <em>rig_f. </em></p> <p>The simulations were performed on the GROMACS 2019.4 molecular dynamics engine, with the ProMPT forcefield for coarse-grained molecular dynamics. Four independent trials: <em>trial_A</em>, <em>trial_B</em>, <em>trial_C</em> and <em>trial_D</em> were performed per system, each with different starting velocities. Each trial was run for 3000 ns.</p> <h1>Contents</h1> <h2><code>> heparin_abeta_trajectories.zip</code></h2> <p>The repository contains the zip file <code>heparin_abeta_trajectories.zip</code> with 13 folders named according to the convention,<code> "{<em>N_pep</em>}pep_{<em>N_hep</em>}hep_dp{<em>hep_dp</em>}_{<em>rig_f</em>}xRigid"</code>. For example, data for the system consisting of 16 peptides (<em>N_pep</em>), 1 heparin (<em>N_hep</em>), 18 monosaccharides in length (<em>hep_dp</em>) with a rigidity factor of 100 (<em>rig_f</em>) would be stored in the directory <code>16pep_1hep_dp18_100xRigid/</code>.</p> <p>If the system did not contain heparin, <em>N_hep, hep_dp</em> and <em>rig_f</em> were set to 0 by default. For example, data for the system consisting of 16 peptides (<em>N_pep</em>) and no heparin would be stored in the directory <code>16pep_0hep_dp0_0xRigid/</code>.</p> <p>A directory such as <code>16pep_1hep_dp18_100xRigid/</code> will have the following contents:</p> <ul> <li><code><strong>solute_only.ndx</strong></code><br>Contains an index group for solute molecules (peptides and/or heparin) only.</li> <li><code><strong>trial_A/</strong></code> <ul> <li><code><strong>md.tpr</strong></code> <br>Portable run file with which the current trajectory was generated. This file may be used to reproduce the trajectory as well.</li> <li><code><strong>solute_only.cluster_center.xtc </strong></code><br>A gromacs trajectory containing only the solute molecules (peptides and/or heparin), centered with the gromacs tool <em>gmx trjconv</em></li> <li><code><strong>solute_only.tpr </strong></code><br>A gromacs portable binary input file containing data for the solute molecules (peptides and/or heparin) only, generated with the <em>gmx convert-tpr</em> tool.<br>This file may be used during analysis of the solute_only.cluster_center.xtc trajectory.</li> </ul> </li> <li><code><strong>trial_B/</strong></code><br>contents same as <code><strong>trial_A/</strong></code></li> <li><strong><code>trial_C/</code><br></strong>contents same as <code><strong>trial_A/</strong></code></li> <li><strong><code>trial_D/</code><br></strong>contents same as <code><strong>trial_A/</strong></code></li> </ul> <h2><code>> psf_files_for_VMD_visualization.zip</code></h2> <p>This zip file contains thirteen .psf files, one per system, that can be used in accord with <code>solute_only.cluster_center.xtc </code>files to visualize trajectories on the Visual Molecular Dynamics (VMD) software.</p> <p><strong>Please seek out the associated publication for essential context on these trajectories. </strong></p> <p><strong>To access the source files with which these simulations were set-up, and a brief tutorial, see: </strong><a href="https://github.com/suhasgotla/heparin_amyloid_self-assembly">https://github.com/suhasgotla/heparin_amyloid_self-assembly</a></p>
All-atom molecular dynamics simulations of iRFP713/C15S/V254C/N136R
<p>The trajectories of all-atom MD simulations of monomeric and dimeric iRFP713/C15S/V254C/N136R with PCB (phycocyanobilin) and BV (biliverdin).</p> <p> </p> <p>Simulations have been performed using the CHARMM36 force field, running with the GROMACS 2022 package.</p>
Molecular dynamics trajectories of pYEEI:SH2 recognition, unbiased, at all-atom resolution.
<div> </div> <p>Set of 772 all-atom trajectories simulated from an unbound (apo) configuration of the human p56 -lck tyrosine kinase SH2 domain with its high-specificity phosphopeptide recognition substrate pYEEI (initial structure based on PDB:<a href="https://www.rcsb.org/structure/1LKK">1LKK</a> ). Approximately 24 trajectories spontaneously reach a bound state with ligand RMSD < 2 Â from the crystal. System building and run details are described in [1].</p> <p>A preliminary version of this dataset have been analyzed and discussed in [1] (approx 200 ns per trajectory were available and used in [1]). </p> <p>The trajectories provided here are extended to ~800 ns each, for a total of ~640 μs sampled time. The full dataset is analyzed in [2] with a SOM-based technique.</p> <div> <h2>Notes</h2> </div> <div> <ul> <li>These are all-atom simulations (with TIP3P water). Water molecules have been stripped off from these files (filtered).</li> <li>Not all trajectories have the same length. Some are cut short due to the distributed computing setup.</li> <li>Frame-to-frame interval is 1 ns.</li> </ul> </div> <h2>Acknowledgments</h2> <p>We thank the volunteers of the GPUGRID.net project for donating computing time.</p> <p> </p> <h2>References</h2> <p>[1] T. Giorgino, I. Buch, and G. De Fabritiis. <a href="https://pubs.acs.org/doi/10.1021/ct300003f">Visualizing the Induced Binding of SH2-Phosphopeptide</a>, J. Chem. Theory Comput. 2012, 8, 4, 1171-1175. doi:10.1021/ct300003f</p> <p>[2] Lara Callea, Camilla Caprai, Laura Bonati, Toni Giorgino, Stefano Motta. Self-Organizing Maps of Unbiased Ligand-Target Binding Pathways and Kinetics. J. Chem. Phys, 2024. https://doi.org/10.1063/5.0225183 </p> <p> </p> <div> </div> <div> <p> </p> </div>
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>
Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics
<p>Supplementary material to the article: Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer’s Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics,These data are available to researchers.</p>
Initial model and Molecular Dynamics trajectory of WGR domain of PARP2 with ZN ions
<div>Files:</div> <div>1. Initial model of PARP2 WGR domain (based on PDB entry: 6F5B) with two sites potentially capable of binding zinc ions: one formed by residues E97, C98, H160 and another - by residues H106, C109, E138</div> <div>2. PARP2 WGR domain with Zn ions bound after energy minimization.</div> <div>3. 400 ns MD trajectory of PARP2 WGR domain with Zn ions.</div>
Molecular Dynamics Simulation and Docking Studies Reveals Inhibition of NF-kB signaling as a Promising Therapeutic Drug Target for reduction in Cytokines Storms
<p><span>The complexes of the top identified molecules with NF-kB-kB site, as well as all the designed molecules used in the screening process. </span></p>
FAIRmat Tutorial 7: Molecular Dynamics Trajectories and Workflows in NOMAD
<p>The FAIRmat consortium is committed to extending the NOMAD infrastructure to a wide variety of materials science data. To support soft matter simulations (e.g., atomistic molecular dynamics simulations), a number of challenges arise, primarily due to the volume and variety of data. The FAIRmat team is working to overcome these challenges, and the NOMAD infrastructure is now equipped with new metadata, features, and tools specifically designed to ease the FAIR treatment of trajectory data and workflows. Parsers have been implemented for two of the most popular molecular dynamics codes (Gromacs and Lammps), with plans for quick expansion to additional codes within the next year. The NOMAD Metainfo now describes the system’s hierarchical structure (in terms of bond topology) through the concept of fixed chemical bonds defined within classical force fields. The NOMAD GUI provides a bespoke overview page for molecular dynamics data, which includes tools that ease visualization of the system topology and automatically displays structural, dynamic, and thermodynamic observables that can assist in a fast assessment of system equilibration. Additionally, a native workflow visualizer allows the user to connect individual simulation entries into complex workflows. Finally, the NOMAD Python module facilitates custom trajectory analysis, for instance in a Jupyter notebook, with functions that convert a NOMAD archive entry to an instance of the MDAnalysis data class.</p> <p>This tutorial invites both experienced and completely novice NOMAD users to learn about these new features for molecular dynamics trajectories. A brief introduction to the FAIRmat consortium and the NOMAD infrastructure will be given, followed by guided and interactive tutorials highlighting the various features described above</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Somatic hypermutation-mediated paratope flexibility improves the cross-reactivity of human malaria antibodies -- Molecular Dynamics dataset
<p>4493 Manuscript Data<br>====================</p> <p>author: Anton Hanke<br>size of uncompressed folder: ~19Gb.<br>DOI: 10.5281/zenodo.11470585</p> <p># Standard MD simulation data</p> <p>Standard Simulations were generated with gromacs 2021.5 using the charmm36m forcefield Juli 2021 release tarball (https://mackerell.umaryland.edu/download.php?filename=CHARMM_ff_params_files/charmm36-jul2021.ff.tgz).<br>Post processed (PBC) simulations are structured as follows:<br>Mature generally refers to the wildtype 4493 antibody.</p> <p>- simulations/standardMD<br> |<br> |- prod.mdp example production mdp file used to run all production simulations.<br> |<br> |- mature Mature simulation set. (folder and file naming the same in all simulation directories)<br> | |- {peptide}_{replicate}_prod.gro {peptide} = peptide; {replicate} = standard MD replicate<br> | |- {peptide}_{replicate}_prod.tpr<br> | `- {peptide}_{replicate}_prod_align_noPBC.xtc (10Frames/ns)<br> |<br> |- mature_rerun Additional set of replicates with the wildtype 4493.<br> |- matureCapped Set of simulations with termini capped peptides<br> |- mature_nanpv2 Set of simulations with NPDP similar positioning of NANP<br> |- wo_pep Set of simulations without peptides for germline and mature<br> `- germline Set of germline simulations.</p> <p><br># RAMD simulation data</p> <p>RAMD simulations were generated with gromacs_2020.5 patched with RAMDv2 modified to account for the connected multiple ligand groups.<br>(Source code provided as tar file ./sw/gromacs_ramd_patchv2.tar.gz)</p> <p>Not all trajectories contain an unbinding event (gromacs CUDA bug.). <br>These trajectories were not considered in the analysis of simulations. </p> <p>- simulations/ramd<br> |<br> |- prod.mdp Exemplary production mdp file with RAMD settings, these were used in all trajectories w/<br> | differing RAMD random seed.<br> |<br> |- mature_2.625kcalmolA_4.0_3.0<br> | |- {peptide}_{replicate}_prod_{startFrame}.gro {peptide} = peptide; {replicate} = standard MD replicate; {startFrame} = Frame in standard MD used to start simulation.<br> | |- {peptide}_{replicate}_prod_{startFrame}.tpr<br> | |- {peptide}_{replicate}_prod_{startFrame}.ndx<br> | |- {peptide}_{replicate}_prod_{startFrame}_align_noPBC.xtc (100Frames/ns) Files omited due to size -- available on request.<br> | `- {peptide}_{replicate}_prod_{startFrame}_lastframe.pdb Last frame of the processed RAMD trajectory.<br> `- gl_2.625kcalmolA_4.0_3.0</p> <p><br># Analysis</p> <p>- analysis<br> |<br> |- entropie Quasi harmonic entropy estimation.<br> | |- inp Concatenated & Bootstrapped, coarse-grained and aligned trajectories of all systems<br> | |- out CPPTRAJ runs to calculate QHE on the bootstrapped trajectories<br> | |- run_complex.sh Script running analysis.<br> | |- ana.py Script to calculte average and std of QHE for each system. (generates *.out *.tsv *.png)<br> | |- cg.py Script used to bootstrap, coarse-grain align and build average structure with.<br> | `- delta_entropies.ods Excel file used to calculate Tab 1. in Main text of paper from entropies.out.<br> |<br> |- mmpbsa MMPBSA calculations (MM + SolvEnergy) with gmx_MMPBSA<br> | |- inp Input trajectories and topologies processed for MMPBSA<br> | |- out/gmx_mmpbsa Output directories in which gmx_MMPBSA was run.<br> | | ` *.dat Output files containing calculated energy terms from gmx_MMPBSA.<br> | |- mmpbsa.in MMPBSA input file used to run analysis.<br> | |- plot_results.py Python script to plot correlation of MMPBSA output with experimental data<br> | |- pca_eig_extr.py Script to reduce simulations to regions of high probability density within trajectory (not used in the present analysis)<br> | |- slurm-91315023.out Log file of the analysis run<br> | `- run_mmpbsa.sh Shell script to run the MMPBSA analysis (generates input and output file trees).<br> |<br> `- ramd RAMD analysation.<br> |- run.sh Shell script to run the analysis<br> |- run_ramd_ana.py Python script called by `run.sh` to run the analysis using `ramdAnalysis.py`<br> |- contact_clusters.py Python script to generate plots based on output of the analysis.<br> |- ramdAnalysis.py Python module containing analysis classes called/used within `run_ramd_ana.py`<br> | Based on tauRAMD & Fingerprint analysis by Dr. Daria Khokh (https://doi.org/10.1021%2Facs.jctc.8b00230; https://doi.org/10.1063%2F5.0019088)<br> |- abrun.* Log files from the present run<br> |- *.svg; *.png Analysis output files.<br> |- tramd_patchv2/ Output PDB structures from the analysis (excluded due to size, available on request)<br> `- representatives.pse Pymol session of cluster representatives along unbinding for germline and wildtype with contact probabilities within the<br> cluster mapped as b-factor.</p> <p># Figures</p> <p>- figure_pdbs PDB files (and pymol sessions) used to generate figures in the papers main text.</p>
Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study.
<p>This repository contains the representative structures of the 20 clusters obtained, which constitute the “MD-adapted structure ensemble”: i.e., sets of atomic coordinates that capture the flexibility and the pre-miR20b (<a href="https://zenodo.org/api/files/ee12021f-4398-465a-9ff6-ddb7be32765f/ensemble_MD_2n7x.pdb?versionId=310a80f6-aa64-445d-8641-45faf9f1ac03">ensemble_MD_2n7x.pdb</a>) and Rbfox/pre-miR20b (<a href="https://zenodo.org/api/files/ee12021f-4398-465a-9ff6-ddb7be32765f/ensemble_MD_2n82.pdb?versionId=82d7afcb-8a92-4daa-8150-789dbd7b2474">ensemble_MD_2n82.pdb</a>) conformers suggested by MD simulations while still retaining the highest possible level of agreement with the primary NMR data.</p>
Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study.-PART 8
<p>Simulations of the miR20b RNA with the Case vdW modification to amber force field and OPC water molecules.</p>
Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations
<p>Dataset and supplementary files of the research: Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations (https://doi.org/10.1101/121681)</p> <p>- Supporting Information</p> <p>- Input: Parameters and initial structures</p> <p>- Output: Trajectories, Docking poses</p> <p>Gromacs (multi-core with CUDA) was used for the simulations.</p> <p>Autodock Vina, FlexAid and rDock were used for molecular docking.</p>
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