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Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p><strong>00_Caver.tar.gz </strong>- Contains CAVER (https://caver.cz/fil/download/manual/caver_userguide.pdf) input and output files used for identification of transport pathways in LinB86(PDB ID: 5LKA). <br>final_clustering<br>├── tunnel_custers # contains caver output files for individual tunnels clusters <br>│ ├── ...<br>├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters <br>│ ├── ...</p> <p><strong>01_CaverDock_Tunnels_Profile.tar.gz</strong> - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained. <br># Each folder (tun_cluster_p1a, tun_cluster_p1b, tun_cluster_p2, tun_cluster_p3) contains input raw files used for CaverDock calculations for individual snapshots of the respective tunnel clusters named as stripped_system*. The details of those files are:<br>- <em>calculations/*/caverdock.conf</em> : The config file input for caverdock calculation. <br>-<em> calculations/*/DBE.pdbqt </em>: Input file for the substrate DBE.<br>- <em>calculations/*/stripped_system*.pdbqt </em>: Input file for the Protein/Receptor<br>- c<em>alculations/*/stripped_system*.dsd </em>: Tunnel discretization file. Notes: <br>- <em>calculations/*/stripped_system*.pdb </em>: PDB file for the tunnel. </p> <p><strong>02_Minimization_and_Equilibration.tar.gz</strong> - Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations.</p> <p><strong>03_HTMD_Bulk</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. <br><strong>04_HTMD_Cavity</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes.<br><strong>05_HTMD_Cavity_Bulk</strong> -<strong> </strong>separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. <br><strong>06_HTMD_Tunnels</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p><strong>07_MD-Analysis.tar.gz</strong> - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K. <br># Each folder contains input raw files used to calculate epochs convergence, distances, RMSD, RMSF, kinetics, percentage, sample proportions and tunnel lengths. The details of those files are:<br>- <em>Epoch_Convergence/epochs_dist_counts.csv</em> : Contains the counts of DBE distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å) for the 30 epochs. <br>- <em>Distances/*/dist_s_r*.csv</em> : Contains .csv file for the frames wise for all studied schemes. The analysis were performed using the cpptraj program (https://amber-md.github.io/cpptraj/CPPTRAJ.xhtml). The following columns are present:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM <br>- <em>RMSD_RMSF/*/*.csv</em> : Contains .csv files with RMSD and RMSF from the protein residues. For RMSF 1st row are residue number (1-295) and 2nd row are RMSF. For RMSD, 1st column are frame no. (0.1ns) and 2nd column are RMSD values respectively. The calcualtion were performed using pytraj (https://amber-md.github.io/pytraj/latest/index.html) program. Example input: pytraj.rmsd(traj, mask='1-295@CA') Example input: pytraj.rmsf(traj, mask=':1-295', options='byres')<br>- <em>COM_RMSF/.xlsx</em> : Contains the center of mass (COM) distances calculated using the bottleneck residues for catalytic residues (N38, D108, W109), p1a (D147, F151, and V173), p1b (D147, W177, and L248), p2 (L211 and L248), and p3 (L143, F151, and I213)<br>- <em>Kinetics/kinetics.txt</em> : Contains .csv file with kinetic information from studied scheme: Cavity, Cavity&Bulk and Tunnels for all the three replicates and calculated average kon, koff, koff/kon rates.<br>- <em>Percentages/.csv</em> : Contains csv files for the percentages of DBE localization and distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å).<br>- <em>Tunnels_lengths/.csv</em> : Contains <em>tunnel_lengths.csv</em>, <em>Summary of tunnel lengths.xlsx</em> files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in <em>tunnel_lengths.csv</em> and summary of respective tunnel clusters generated from Caver output (for more details please check https://www.caver.cz/fil/download/manual/caver_userguide.pdf with keywork "summary.txt") in the <em>Summary of tunnel lengths.xlsx</em> file. <br>- <em>Sample_proportions/.csv</em> : Contains <em>sample_proportions.csv</em> file with proportions or fraction individual metastable states while performing the transition pathway analysis. For more details please check https://software.acellera.com/htmd/htmd.kinetics.html<br> or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path<br> - <em>*.py</em> : Python scripts used to build and analysis Markov state models with use case and distances of ligand.<br>- <em>Generated_models/</em> : Contains <em>models_rep[].dat</em> files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below:<br>├── Cavity<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>├── Cavity_Bulk<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>└── Tunnels<br> ├── model_rep1.dat<br> ├── model_rep2.dat<br> └── model_rep3.dat </p> <p><br><strong>08_TransportTools.tar.gz</strong> - Contains TransportTools (TT) analysis output, log and summary files for Cavity, Cavity&Bulk and Tunnels schemes. For more details on the workflow of TT, please visit https://github.com/labbit-eu/transport_tools<br>results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>- <em>event.csv</em> file contains the aggregated summary of events inferred from the <em>4-filtered_events_statistics.txt</em> files of each results of respective schemes</p> <p><strong>09_MSM_states.tar.gz</strong> - Contains the Markov state models (MSM) output files for Cavity, Cavity&Bulk and Tunnels schemes and three replicates. The MSMs were generated using the pyEMMA program and HTMD framework, for further details please follow https://software.acellera.com/htmd/documentation.html. The directories looks as below: <br>├── Bulk<br>│ ├── rep1 # MSM states for replicate 1<br>│ ├── rep2 # MSM states for replicate 2<br>│ ├── rep3 # MSM states for replicate 3<br>├── Cavity<br>│ ├── rep1 <br>│ ├── rep2 <br>│ ├── rep3 <br>├── Cavity&Bulk<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3<br>├── Tunnels<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3</p> <p><br><strong>10_MSM_fingerprints.tar.gz</strong> - Contains the Markov state models (MSMs) distances generated from repository dir <strong>09_MSM_states</strong> consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package.<br>- <em>MSM_Distances/*/rep*/*.csv</em> : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM </p> <p><br><strong>11_ULS_clustering_and_transition_assignments.tar.gz</strong> - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files:<br>1. for the transition detection of DBE and the classification in Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and <br>2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown.<br># the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels:<br>├── average_tunnel_utilization_per_scheme.png<br>├── average_tunnel_utilization.png<br>├── Bulk<br>│ ├── Bulk_run_htmd_0_combined_df.csv<br>│ ├── Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Bulk_run_htmd_1_combined_df.csv<br>│ ├── Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Bulk_run_htmd_2_combined_df.csv<br>│ └── Bulk_run_htmd_2_transitions_counts.csv<br>├── Bulk&Cavity<br>│ ├── Cavity&Bulk_run_htmd_0_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_1_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_2_combined_df.csv<br>│ └── Cavity&Bulk_run_htmd_2_transitions_counts.csv<br>├── Cavity<br>│ ├── Cavity_run_htmd_0_combined_df.csv<br>│ ├── Cavity_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity_run_htmd_1_combined_df.csv<br>│ ├── Cavity_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity_run_htmd_2_combined_df.csv<br>│ └── Cavity_run_htmd_2_transitions_counts.csv<br>├── parse_distances_msm.py<br>├── schemes_comparison_piechart_per_scheme.png<br>└── Tunnels<br> ├── Tunnels_run_htmd_0_combined_df.csv<br> ├── Tunnels_run_htmd_0_transitions_counts.csv<br> ├── Tunnels_run_htmd_1_combined_df.csv<br> ├── Tunnels_run_htmd_1_transitions_counts.csv<br> ├── Tunnels_run_htmd_2_combined_df.csv<br> └── Tunnels_run_htmd_2_transitions_counts.csv</p> <p> </p>
05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. </p> <p># The forders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
Multifaceted Activity of Fabimycin: Insights from Molecular Dynamics Studies on Bacterial Membrane models
<p>This dataset presents a comprehensive collection of input data for Molecular Dynamics (MD) simulations performed using the GROMACS simulation software. The included systems cover various membrane environments, each with distinctive properties. The systems consist of:</p> <ol> <li><strong>IM (Inner Membrane):</strong> Simulations involving the bacteral mimicking inner membrane environment.</li> <li><strong>IM_OM (Inner Membrane and Outer Membrane Complex):</strong> Complex systems encompassing both inner and outer bacterial membrane models.</li> <li><strong>OM_D (Double Symmetric Outer Membrane):</strong> Simulations featuring a symmetric outer membrane structure.</li> <li><strong>OM (Asymmetric Outer Membrane):</strong> Simulations with an asymmetric outer membrane configuration.</li> <li><strong>PC Membrane (Phosphatidylcholine Membrane):</strong> Simulations involving membranes composed of phosphatidylcholine.</li> </ol> <p>For each membrane type, the dataset provides three replicas. The dataset includes initial and final structures (.gro files), simulation parameter files (.mdp), index files (.ndx), and topology files (.itp and .top) applicable to all systems. </p>
Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'
<p>The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.</p>
Design and assembly of core/shell nanostructures as investigated by microfluidics and molecular dynamics simulation_dataset_DLS_TEM_MD
<p><span>He we like to publish data related to modified and non-modified MSN cores analysed using microfluidics platform against acetalated dextran (AcDEX)/spermine modified acetalated dextran (SpAcDEX) polymers. </span></p> <p><span>The data contains Dynamic light scattering (DLS) and TEM images which help us to to the demarcation of combinations which formed successful core/shell particles along with, <em>in-silico</em> modelling and molecular dynamics (MD) simulations data showing molecular interactions between the core particles and <a>the encapsulant </a></span><span><span></span></span><span>polymer. </span></p>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 02)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p> </p> <p>Please cite us if you find this data useful!</p>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 03)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p> </p> <p>Please cite us if you find this data useful!</p>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 01)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p>Please cite us if you find this data useful!</p>
Supporting molecular simulations data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3"
<p>Supplementary data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3: <a href="https://doi.org/10.1126/sciadv.abe3326">10.1126/sciadv.abe3326</a>"</p>
Molecular Dynamics Assessment of Fusion Relevant Elements Figures
<p>Figures for "Molecular Dynamics Assessment of Primary Damage Efficiency for Fusion Relevant Elements"</p>
Nicotinamide Adenine Dinucleotide Molecular Dynamics
<p>Nicotinamide Adenine Dinucleotide. Molecular dynamics of NAD molecule is solvated with water (transparent) in UCSF Chimera software for 22 nanoseconds.<br>By Victor Padilla Sanchez, PhD - Washington Metropolitan University, President.<br>Email: drvictorpadilla@aol.com<br>Website: https://www.drvictorpadillasanchez.com</p>
Supporting Data for "Thermal Transport Through CTAB- and MTAB-Functionalized Gold Interfaces using Molecular Dynamics Simulations"
<p>This gzipped tar archive contains initial configurations and parameters used for the simulations in the manuscript:</p> <p>"Thermal Transport Through CTAB- and MTAB-Functionalized Gold Interfaces using Molecular Dynamics Simulations", by Sydney A. Shavalier, and J. Daniel Gezelter</p> <p>A note on naming conventions. All simulations have filenames with with two numbers - one that signifies simulation replica (1-5), and another that signified which step of equilibration/RNEMD was being performed. For example, lowmtab111_3opt4 would signify the third simulation replica of a low coverage MTAB system and a (111) gold facet, which was on its fourth equilibration step after optimization.</p> <p>The OpenMD simulation engine utilizes a number of file extensions that are present in this archive:</p> <p><strong>.omd</strong> : A combined MetaData and configuration file that is used to start a simulation <br><strong>.frc</strong> : a force field parameter file<br><strong>.eor</strong> : an 'end of run' or final configuration (same format as .omd)<br><strong>.stat</strong> : status file with instantaneous information about energies, temperatures, etc. These are generally large and have not been included, as they can be regenerated easily from the .omd file.<br><strong>.report </strong>: a post-simulation file containing thermodynamic averages from the .stat file<br><strong>.dump</strong> : a full trajectory file containing positions and velocities sampled at a 'sampleTime' specified in the .omd file. These are generally very large and have not been included, as they can be regenerated from the .omd file.<br><strong>.rnemd</strong> : Contains spatial information about temperatures, densities, etc. for simulations run under reverse non-equilibrium molecular dynamics</p> <p>Other data analyis or utility file extensions:</p> <p><strong> .pack</strong> : Files for creating systems with Packmol<br><strong> .z</strong> : Density \rho(z) for specific selected atom types<br><strong> .r </strong> : Density \rho(r) for specific selected atom types<br><strong> .p2z</strong> : Legendre Polynomial Correlation using z as reference axis<br><strong> .p2r</strong> : Legendre Polynomial Correlation using radial vector as reference axis<br> <strong>.chargez</strong> : Charge density as a function of z-axis<br> <strong>.charger</strong> : Charge density as a function of radius<br> <strong>.agr</strong> : Grace graphing package data<br><strong> .xyz </strong> : XYZ (Cartesian) coordinates for visualization<br> </p> <p>The archive is organized as follows:</p> <p> ./CTAB/111: Simulations of Au(111) functionalized with CTAB<br> final systems begin with "highctab"<br> RNEMD simulations are in ./CTAB/111/RNEMD<br> ./CTAB/110: Simulations of Au(110) functionalized with CTAB<br> final systems begin with "highctab"<br> RNEMD simulations are in ./CTAB/110/RNEMD<br> ./CTAB/100: Simulations of Au(100) functionalized with CTAB<br> final systems begin with "highctab"<br> RNEMD simulations are in ./CTAB/100/RNEMD<br> ./MTAB/111: Simulations of Au(111) functionalized with MTAB <br> final systems begin with "highmtab" or "lowmtab"<br> RNEMD simulations are in ./MTAB/111/RNEMD<br> ./MTAB/110: Simulations of Au(110) functionalized with MTAB<br> final systems begin with "highmtab" or "lowmtab"<br> RNEMD simulations are in ./MTAB/110/RNEMD<br> ./MTAB/100: Simulations of Au(100) functionalized with MTAB <br> final systems begin with "highmtab" or "lowmtab"<br> RNEMD simulations are in ./MTAB/100/RNEMD<br> ./MTAB/NP/R10: Simulations of Au Nanoparticles (r = 10 angstroms),<br> functionalized with MTAB<br> final systems begin with "highmtab" or "lowmtab"<br> RNEMD simulations are in ./MTAB/NP/R10/RNEMD</p> <p>Systems that were run with metal polarizability turned on have 'fq' as part of their filenames.</p>
Molecular dynamic simulations of WT PAR2
<p>MD simulations data for WT PAR2</p>
Molecular dynamic simulations of D62A PAR2
<p>MD simulations data for D62A PAR2</p>
Molecular dynamic simulations of I39L PAR2
<p>MD simulations data for I39L PAR2</p>
Molecular dynamic simulations of I39V PAR2
<p>MD simulations data for I39V PAR2</p>
How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study - Second part
<p>Second part of the data deposition for the paper "<strong>How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study</strong>", by Lemmens et al. Part one is availible via <a href="https://doi.org/10.5281/zenodo.13929049">10.5281/zenodo.13929049</a></p>
Insights into the DNA and RNA Interactions of Human Topoisomerase III Beta Using Molecular Dynamics Simulations
<p>hTOP3 simulations for both covalently and non-covalently bound DNA and RNA substrates. Simulation times = 300ns, with 1/ns per frame = 300 frames each.</p>
Molecular dynamics of solids at constant pressure and stress using anisotropic stochastic cell rescaling - dataset
<p>Supporting data related to manuscript "Molecular dynamics at constant pressure and stress using anisotropic stochastic cell rescaling"</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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