1 to 10 of 48 Results
Mar 20, 2026 - PN 3-11
Pluhackova, Kristyna; Hiller, Sebastian; Agustoni, Elia, 2026, "Supplementary Data to: "Activation mechanism of the full-length histidine kinase LvrB from pathogenic Leptospira"", https://doi.org/10.18419/DARUS-5664, DaRUS, V2
MD data (author K. Pluhackova): Initial coordinate and simulation input files and a coordinate files of the final outputs as well as of the simulation trajectories (protein only) for all-atom MD simulations of LvrB performed using CHARMM36m/TIP4p in GROMACS2023. (pdb, xtc) Simulation data on phosphorylated aspartic acid (residue APP) with parameter... |
Mar 10, 2026
AI software tools for materials development |
Feb 10, 2026 - PN 3-11
Korn, Viktoria Helena; Pluhackova, Kristyna, 2026, "Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"", https://doi.org/10.18419/DARUS-5682, DaRUS, V1
GROMACS simulation files, input and final structures for CHARMM36m MD simulations including our refined parameters for phosphorylated serine in 3 different protonation states. The directory charmm36-jul22mod.ff contains our refined parameters. |
Feb 3, 2026 - PN 3-11
Pluhackova, Kristyna, 2026, "Supplementary data to "Roles of ligand, phosphorylation, and membrane in assembling human β2-adrenergic receptor-β-arrestin complexes"", https://doi.org/10.18419/DARUS-5513, DaRUS, V1
GROMACS simulation files, initial and final snapshots (all atoms) as well as simulation trajectories (proteins, ligands, membrane) of (i) beta-arrestin 2 (barr2) in inactive and active form interacting with a membrane with or without PIP2 lipids, (ii) beta2-adrenergic receptor activated by adrenaline and either phosphorylated on S355 and S356 or no... |
Dec 22, 2025 - PN 3-5
Sriram, Siddharth, 2025, "Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset", https://doi.org/10.18419/DARUS-5623, DaRUS, V1
This repository is intended to host the supplementary material (dataset and scripts) associated with the article “Optimization of magnetoelectric composites using convolutional neural networks”. The HDF5 file 'Microstructure_data.h5' contains the dataset of microstructural images and their effective properties of interest, computed using the finite... |
Nov 28, 2025
Stärk, Philipp; Schlaich, Alexander, 2025, "Supplementary Data for: Phase Diagram and Criticality of the Modified Primitive Electrolyte Model in Bulk and Inert and Conducting Confinement", https://doi.org/10.18419/DARUS-5037, DaRUS, V1, UNF:6:2rxz9p/7+PzWplE3Nv/71A== [fileUNF]
Supplementary Data for: Phase Diagram and Criticality of the Modified Primitive Electrolyte Model in Bulk and Inert and Conducting Confinement This data respository contains software code for an implementation of the Wang-Landau (WL) method in LAMMPS, code to "stitch" together free energy profiles from WL runs of sub-spaces of the phase space, simu... |
Nov 25, 2025
Stärk, Philipp; Stooß, Henrik; Loche, Philip; Bonthuis, Douwe Jan; Netz, Roland; Schlaich, Alexander, 2025, "Supplementary Data for: Static Dielectric Profiles and Coarse-graining Approaches for Water in Graphene Slit Pores: The Crucial Influence of Boundary Conditions in Simulations", https://doi.org/10.18419/DARUS-4317, DaRUS, V1
This repository contains the input scripts and raw data to replicate the results of the main publication. |
Sep 9, 2025 - PN 3-11
Pluhackova, Kristyna, 2025, "Supplementary Data to: "Cysteine-mediated structural stabilization of the tetrameric GlpF"", https://doi.org/10.18419/DARUS-5324, DaRUS, V1
Simulation files, molecular structures and trajectories tetrameric GlpF wild type and M4C mutant. in the latter, 4 cysteine residues in the transmembrane helix bundle of GlpF, in detail C11, C28, C80, and C99 are replaced by glycines. The data underlays our publication on the role of those 4 cysteine residues for stability of membrane inserted tetr... |
Apr 16, 2025 - PN 3-11
Pluhackova, Kristyna; Pfaendner, Christian; Unger, Benjamin; Korn, Viktoria Helena, 2025, "Data for: ART-SM: Boosting Fragment-Based Backmapping by Machine Learning", https://doi.org/10.18419/DARUS-4134, DaRUS, V1
The simulation files, molecule topologies, and analysis workflows required to generate the results of our paper 'ART-SM: Boosting Fragment-Based Backmapping by Machine Learning' published in J. Chem. Theory Comput.. In details: simulations.tar.gz: Contains the pdb (molecular structure), xtc (trajectory), mdp (MD parameters), itp (topology), and top... |
Jan 10, 2025
Bottom-up modeling of conducting porous materials via molecular simulation |
