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Project files provided as supporting information to the manuscript "Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with the oxRNA model"
<pre>README file to the project files provided as supporting information to the manuscript "Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with the oxRNA model" <br>October 8, 2024<br> Authors: Giovanni Mattiotti, Manuel Micheloni, Lorenzo Petrolli, Lorenzo Rovigatti, Luca Tubiana, Samuela Pasquali, Raffaello Potestio</pre> <p>==================================</p> <p>Trajectories and obsvervables relative to the scientific paper entitled:<br>"Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with oxRNA"</p> <p>Authors:<br>Giovanni Mattiotti, Manuel Micheloni, Lorenzo Petrolli, Luca Tubiana, Samuela Pasquali, Raffaello Potestio</p> <p>Brief description of the content of this folder:</p> <p><br>|_ oxDNA-CCMV_force.zip: the modified version of the oxDNA software we used to make the simulations with the CCMV-like spherical potential described in the paper<br>|<br>|_ CCMV_RNA2_oxRNA_simulations<br> |_ FOLDING (files of the "freely-folding" simulations)<br> | |_ FIRST_LAST_FRAMES (frames generated by oxDNA software)<br> | | |_ 0.15M (first and last frames of the runs at 0.15M)<br> | | | |_ REP1 (files relative to REP1 / REPI)<br> | | | | |_ first.dat<br> | | | | |_ last.dat <br> | | | |<br> | | | |_ REP2 (files relative to REP2 / REPII)<br> | | | | |_ first.dat<br> | | | | |_ last.dat <br> | | | |<br> | | | |_ REP3 (files relative to REP3 / REPIII)<br> | | | |_ first.dat<br> | | | |_ last.dat <br> | | |<br> | | |_ 0.50M (first and last frames of the runs at 0.50M)<br> | | | |_ [same structure as for 0.15M]<br> | | |<br> | | |_ 0.50M (first and last frames of the runs at 0.50M, T293K)<br> | | | |_ [same structure as for 0.15M]<br> | | |<br> | | |_ 0.50M (first and last frames of the runs at 0.50M, T293K, harmonic constraint to the ends)<br> | | |_ [same structure as for 0.15M]<br> | |<br> | |<br> | |_ HB_FILES (base pairs files generated by oxDNA software)<br> | | |_ hb_015M_I.dat (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.15M salt concentration)<br> | | |_ hb_015M_II.dat (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.15M salt concentration)<br> | | |_ hb_015M_III.dat (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.15M salt concentration)<br> | | |_ hb_050M_I.dat (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration)<br> | | |_ hb_050M_II.dat (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration)<br> | | |_ hb_050M_III.dat (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration)<br> | | |_ hb_050M_293K_I.dat (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration, T 293K)<br> | | |_ hb_050M_293K_II.dat (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration, T 293K)<br> | | |_ hb_050M_293K_III.dat (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration, T 293K)<br> | | |_ hb_050M_293K_harm_I.dat (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)<br> | | |_ hb_050M_293K_harm_II.dat (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)<br> | | |_ hb_050M_293K_harm_III.dat (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)<br> | |<br> | |_ example_input.ox (example of oxDNA input file used to launch simulations and dump observables and trajectories)<br> | |<br> | |_ example_input_harm.ox (example of oxDNA input file used to launch simulations with harmonic constraint to the ends and dump observables and trajectories)<br> | |<br> | |_ harmonic_ends.dat (constraint force file used to run simulations with harmonic constraint to the ends)<br> |<br> |_ PACKED<br> |_ SIM_VTHEO (files of the simulations with the analytic-based external potential, as described in the paper...)<br> | |_ 0.15M (... with a salt concentration of 0.15M)<br> | | |_ REP1 (Replica with id 1, or I)<br> | | | |_ MD_scripts (files required by oxDNA software to launch the simulation for this replica)<br> | | | | |_ input_0.15M_REP1.ox (input file of oxDNA software to launch the simulation)<br> | | | | |_ force_0.15M_REP1.dat (additional file required for the application of an external force in the simulation, containing the relative parameters)<br> | | | |<br> | | | |_ input_structure (structure files in oxDNA format, one of which is used as starting configuration for the specific replica)<br> | | | | |_ PackingRNA2_0.15M_REP1.dat (last structure of simulation with time-dependent external packaging force)<br> | | | | |_ PackingRNA2_0.15M_REP2.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | | |_ PackingRNA2_0.15M_REP3.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | | |_ PackingRNA2_0.5M_REP1.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | | |_ PackingRNA2_0.5M_REP2.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | | |_ PackingRNA2_0.5M_REP3.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | |<br> | | | |_ data (output data generated by the simulation)<br> | | | |_ 1.E (files of the observables dumped during simulation)<br> | | | | |_ E_RNA2_0.15M_REP1.dat (energies of the system per each time frame: time frame, potential energy U, kinetic energy, total energy)<br> | | | | |_ P_RNA2_0.15M_REP1.dat (internal pressure of the system: time frame, total pressure, stress tensor components xx, yy, zz, xy, xz, yz)<br> | | | | |_ F_RNA2_0.15M_REP1.dat (components of the external force acting on each nucleotide, ordered according to the topology, per each frame)<br> | | | | |_ HB_RNA2_0.15M_REP1.dat (base pairs annotated, per each time frame dumped)<br> | | | |<br> | | | |_ 3.restart (restart/last configuration file dumped during simulation)<br> | | | |_ last_conf_RNA2_0.15M_Yukawa_REP1.dat (last frame dumped in the simulation, in oxDNA format)<br> | | |<br> | | |<br> | | |_ REP2 (Replica with id 2, or II)<br> | | | |_ [same content as REP1, but relative to replica 2 or II]<br> | | |<br> | | |_ REP3 (Replica with id 3, or III)<br> | | |_ [same content as REP1, but relative to replica 3 or III]<br> | |<br> | |_ 0.5M (... with a salt concentration of 0.5M)<br> | | |_ [same content as 0.15M, but for simulations done at 0.5M of salt concentration]<br> | |<br> | |_ 0.5M_293K_harm (... with a salt concentration of 0.5M, Temperature 293K and harmonic constraint to the ends)<br> | |_ [same content as 0.15M, but for simulations done at 0.5M of salt concentration]<br> |<br> |_ SIM_VCCMV (files of the simulations with the structure-based external potential, as described in the paper)<br> |_ [same content as SIM_VTHEO, but for simulations done with the structure-based external potential]</p> <p><br>For any additional file or data not present here, that was used to produce results in the paper, please ask to: giovanni.mattiotti@inserm.fr, manuel.micheloni@unitn.it, lorenzo.petrolli@unitn.it</p>
All-atom molecular dynamics simulations for Targeting Human Prostaglandin Reductase 1 with Licochalcone A: Insights from Molecular Dynamics and Covalent Docking Studies
<p>The dataset comprises simulations of the PTGR1 protein under four different conditions: in its apo (unbound) form, bound to the cofactor NADH, and in complex with both covalently and non-covalently bound licochalcone A. Each simulation was conducted using the ff19SB force field and the OPC water model, with water molecules excluded from the trajectories.</p> <p>For the apo form, NADH-bound, and non-covalently bound licochalcone A conditions, each trajectory consists of 5000 snapshots, representing a total of 500 nanoseconds of simulation. However, the trajectory for the no covalently bound licochalcone A condition includes only 1000 frames, corresponding to 150 nanoseconds. This discrepancy in frame count and simulation length across conditions is important to consider when comparing dynamics and structural behavior within the dataset.</p> <p><strong>PTGR1-NADPH.tar.xz</strong> - PTGR1 dimer in complex with NADPH<br><strong>PTGR1-apo.tar.xz</strong> - PTGR1 apo dimer<br><strong>PTGR1-monomer_NADPH.tar.xz</strong> - PTGR1 monomer in complex with NADPH<br><strong>PTGR1-LicA_covalent.tar.xz </strong>- PTGR1 dimer with covalently bound licochalcone A<br><strong>PTGR1-LicA_NO_covalent.tar.xz</strong> - PTGR1 dimer with covalently bound licochalcone A</p> <p> </p> <p>All folders contain:</p> <p><em>*.parm7</em> - dry topology in amber format</p> <p><em>*.nc</em> - dry trajectories in netcdf format</p> <p> </p> <p><strong>Plain molecular dynamics simulations.</strong> Two structures of human PTGR1 have been deposited in the PDB: one bound to NADPH and the raloxifene inhibitor (PDB ID 2Y05, 2.2 Å resolution), and another in apo form (PDB ID 1ZSV, 2.3 Å resolution). In the first structure, it is reported as a monomer, whereas in the second as a dimer. However, the protomers exhibit highly similar conformations in both structures (backbone RMSD ~0.5 Å). Experimental evidence, akin to PTGR1 orthologs and many other MDR enzymes, indicates that the functional form of human PTGR1 is a homodimer (Mesa et al., 2015). Thus, to construct the dimeric form of the coenzyme complex, we duplicated the NADPH-bound protomer from 2Y05 and aligned the two subunits with the dimer from 1ZSV, deleting the raloxifene molecule. In the resulting structure, no steric clashes between the protomers were observed. This structure was used as the starting point for the simulations. Additionally, the apo dimer was generated by removing the NADPH from both subunits, and the monomeric form in complex with NADPH was derived from the initial structure. </p> <p>The pmemd.cuda module of AMBER 22 was used to perform the MD simulations, employing the force field FF19SB and the OPC water model (Case et al., 2022; Izadi, Anandakrishnan, & Onufriev, 2014; Salomon-Ferrer, Götz, Poole Duncan and Le Grand, & Walker, 2013; Tian et al., 2020). NADPH parameters were taken from (Cummins, Ramnarayan, Singh, & Gready, 1991). The system was protonated at pH 7.4 with PDBfixer (Eastman et al., 2017a) and placed in a truncated octahedral box, initially spanning 12 Å further from the solute in each direction using the AMBER tLeap module. The overall charge of the system was neutralized by the addition of four sodium ions. ParmEd (Eastman et al., 2017b) was used to implement the hydrogen mass repartitioning scheme (Hopkins, Le Grand, Walker, & Roitberg, 2015). Local clashes and solvent orientation were corrected using the steepest descent algorithm for 5,000 cycles. During the initial NVT equilibration, the velocities gradually increased through five steps of 200 ps each. The temperature progression started at 150 K and was raised to 200 K, 250 K, 300 K, and finally, 310 K. Position restraints were applied to heavy atoms of the protein, with the restraining forces progressively decreasing at each step. The spring constants were set at 4, 5, 3, and 1 kcal/mol Å2, respectively, to allow for the gradual relaxation of the protein. The system was further equilibrated for 1 ns in the NPT ensemble with no restraints. For treating long-range electrostatic interactions, periodic boundary conditions and Ewald sums were used with a 9 Å cutoff for direct interactions (Darden, York, & Pedersen, 1993; Simmonett & Brooks, 2021). The same cutoff was used for Lennard-Jones interactions. The Langevin thermostat (Sindhikara, Kim, Voter, & Roitberg, 2009) with a collision frequency of 4 ps-1 and the Monte Carlo barostat(Åqvist, Wennerström, Nervall, Bjelic, & Brandsdal, 2004) with a pressure relaxation time of 2 ps were used to control temperatures and pressures, respectively. The SHAKE algorithm was used to fix any bond involving hydrogen atoms (Ryckaert, Ciccotti, & Berendsen, 1977), and a 4-fs time step integration was used. This protocol was taken from (Cofas-Vargas et al., 2022; Medrano‐Cerano et al., 2024) Unless otherwise stated, no other constraints were used. Five replicas of 500 ns each per system were produced.</p> <p>The topology and parameter files for a LicA molecule and for this inhibitor covalently bound to the sulfur atom of a cysteine residue were generated with Antechamber suite (J. Wang, Wang, Kollman, & Case, 2006), using the general Amber force field (GAFF2) for organic molecules (He, Man, Yang, Lee, & Wang, 2020). Atomic charges were derived using the AM1-BB method (Jakalian, Jack, & Bayly, 2002). The parameters are documented in Supplementary Tables SI-1 and SI-2. Trajectories for PTGR1 covalently and noncovalently bound to LicA were run using the same conditions as described above. All molecular structure representations were created using UCSF ChimeraX v1.8 (Meng et al., 2023; Pettersen et al., 2021).</p> <p> </p> <p><strong>Solvent-site identification and guided docking.</strong> Determination of solvent sites (SS) for ethanol and water molecules was conducted by employing the MDmix method. After removing both NADPH molecules from the enzyme dimer, the system was protonated at pH 7.4 with PDBfixer (Eastman et al., 2017a) and placed in a truncated octahedral box of water/ethanol 80/20% v/v, extending12 Å beyond the solute in each direction using the AMBER tLeap module. Five 20 ns replicas were run, using the same conditions described above, but applying Cartesian restrictions of 0.01 kcal/mol A2 over all heavy atoms. After the alignment of trajectories, density maps for probe atoms were generated by constructing a static mesh with cubic grids (0.5 Å edge length) over the entire simulation box. The occurrence of probe atoms within each grid were tracked across the trajectories. These density distributions were then converted into binding free energy using the Boltzmann relationship, comparing observed probe atom distributions against the expected bulk solvent distribution at 1.0 M. Solvent sites were then filtered by applying an energy threshold of 1 kcal/mol, as previously described (Alvarez-Garcia & Barril, 2014; Avila-Barrientos et al., 2022).</p> <p><strong>LicA docking. </strong>For covalent docking, LicA, bound through its Cb atom to the sulfur atom of C239, was docked onto the NADPH-binding site of human PTGR1 employing the covalent docking module of AutoDock4 v4.2.6 (Bianco, Forli, Goodsell, & Olson, 2016; Morris et al., 2009). The flexible side-chain methodology was used. In a subsequent non-covalent docking, solvent sites previously identified for ethanol and water were used as pharmacophoric element for rDock (Ruiz-Carmona et al., 2014). This docking involved defining the receptor system and generating a binding cavity using the NADPH as a reference molecule. During the non-covalent docking, a penalty score proportional to the square of the distance from each ligand conformation to a solvent site (SS) was applied when the separation exceeded 2 Å. The docking run included 100 simulations, generating a set of potential binding modes for LicA within the NADPH site. </p>
Molecular Dynamics simulation data of Cyt c at pH 7 at the water|TFT interface
<p>Programmed cell death via apoptosis is a natural defence against excessive cell division, crucial for foetal development to maintenance of homeostasis and elimination of precancerous and senescent cells. Here we demonstrate an electrified liquid bio-interface that replicates the molecular machinery of the inner mitochondrial membrane at the onset of apoptosis. By mimicking in vivo cytochrome c (Cyt c) interactions with cell membranes, our platform allows us to modulate the conformational plasticity of the protein by simply varying the electrochemical environment at an aqueous|organic interface. Remarkably, we observe interfacial electron transfer between an organic electron donor decamethylferrocene and O2, electrocatalysed by Cyt c. This interfacial reaction requires partial Cyt c unfolding, mimicking Cyt c in vivo peroxidase activity. As proof-of-concept, we use our electrified liquid bio-interface to identify drug molecules, such as bifonazole, that can potentially downregulate Cyt c and protect against uncontrolled neuronal cell death in neurodegenerative disorders.</p>
Data set of the article "Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by supervised molecular dynamics simulation"
<p>Data set of the article "Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by supervised molecular dynamics simulation"</p>
Molecular Dynamics Dataset - Dynamics of GLP-1R peptide agonist engagement are correlated with kinetics of G protein activation
<p>Molecular dynamics simulations of the glucagon-like peptide receptor (GLP-1R) in complex with Gs protein and four different agonist peptides: GLP-1, exendin-4, oxyntomodulin, and exendin-P5. </p> <p>For each system, the .xtc file is the result of the merging of 4 replicas of 500 ns for a total of 2 microseconds (8 microseconds considering all four GLP-1R complexes). The original MD trajectories have been stripped of water, ions and phosphatidylcholine atoms before the upload. The CHARMM36 force field was used.</p>
Collision-induced dissociation of protonated uracil water clusters probed by molecular dynamics simulations
<p>This dataset contains the input and output files associated to the work of "Collision-induced dissociation of protonated uracil water clusters probed by molecular dynamics simulations"</p> <p> </p> <p> </p>
Jensen_etal_2022_molecular_dynamics_simulation_data
<p>Full molecular dynamics simulation data set accompanying Jensen et al, 2022, containing (i) representative coordinates and measurements described therein, (ii) simulation trajectories, and (iii) GROMACS input files.</p>
Molecular dynamics simulations of the tripartite interface (Syt1_C2B—SNARE—Cpx Complex)
<p>Synaptic transmission is mediated by an orchestra of presynaptic proteins that precisely control and trigger fusion between synaptic vesicles and the neuron terminal at the active zone upon an action potential. Critical to this process are the neuronal SNAREs (Soluble N-ethylmaleimide sensitive factor Attachment protein REceptor), the Ca2+-sensor synaptotagmin, the activator/regulator complexin, and other factors. Here we present the data for molecular dynamics simulations of the tripartite interface bewteen these proteins. </p>
Molecular Dynamics simulation
<p>Molecular Dynamics of a protein + POPC membrane system (Beta2-adrenergic receptor, PDB ID:5D5A).</p> <p>Equilibration time: 20 ns.</p> <p>Simulation time: 100 ns.</p> <p>Software: OpenMM: 7.7.0</p> <p>Forcefield:amber14, tip3p</p>
Supplementary FIles for Cholesterol Biases the Conformational Landscape of the Chemokine Receptor CCR3: A MAS SSNMR-Filtered Molecular Dynamics Study
<p>This repository contains supplementary files for the initial submission of:</p> <p>Cholesterol Biases the Conformational Landscape of the Chemokine Receptor CCR3: A MAS SSNMR-Filtered Molecular Dynamics Study<br> Evan J. van Aalst, Corey J. McDonald, and Benjamin J. Wylie</p> <p>Files found in this repository include:<br> 1. Raw fids corresponding to the solid-state NMR spectra used in this work.<br> 2. The script, model structures, and predicted chemical shifts used in the COMPASS proof of concept.<br> 3. Input molecular dynamics files derived from CHARMM-GUI including all mdp files, initial model structure files, and the production script.<br> 4. Model structures per ns derived from MD trajectories with corresponding predicted chemical shift lists and associated experimental chemical shift lists.</p>
Input parameters for manuscript "Understanding drug skin permeation enhancers using Molecular Dynamics Simulation"
<p>Input parameters used for Gromacs simulations in publication with title: "Understanding drug skin permeation enhancers using Molecular Dynamics Simulation"</p>
Yu_et_al_2023_molecular_dynamics_simulation_data
<p>The molecular dynamics simulation data set accompanying Yu et al, 2023, containing (i) representative coordinates and measurements described therein, (ii) selected simulation trajectories, and (iii) LAMMPS input files.</p>
Atomistic Picture of Opening-Closing Dynamics of DNA Holliday Junction Obtained by Molecular Simulations: Simulations Topology, Coordinate, Parameters, Input and Output files
<p>The simulation data for the article: Atomistic Picture of Opening-Closing Dynamics of DNA Holliday Junction Obtained by Molecular Simulations.</p> <p>ck_metad.tar.gz: Includes the topology files, coordinates files and gromacs parameter input file (.mdp) used for WT-MetaD-HREX simulations with different c(K+), which are newly added runs for resubmission. The corresponding script files and Plumed files are in GitHub.</p> <p>eq_mini.tar.gz: Includes the parameter files required for the equilibration and minimization protocol.</p> <p>standard_md.tar.gz: Includes the topology files and coordinate files for all systems built in the article. Also include the hbfix parameters file required on the MD run, and the MD script file.</p> <p>metad.tar.gz: Includes the topology files, coordinates files and gromacs parameter input file (.mdp) used for WT-MetaD-HREX simulations. The corresponding script files and Plumed files are in GitHub.</p> <p>metad*fe*.tar.gz: Plumed HILLS files and metad.bias data used for drawing the free energy landscapes.</p> <p>ions.tar.gz: Data used for Figure.3 in the manuscript</p> <p>si_data.tar.gz: All data used for SI figures.</p>
Data set for graphene/GO polymer molecular dynamics simulation
<p>Lammps input files and log files for molecular dynamics simulations of graphene and graphene-oxide nano ribbons for paper "Molecular dynamics reveals the origin of the enhancement of polymer properties by graphene". Log files include stress-strain behaviour during uniaxial strain. </p> <p> </p>
Charge Transport in Water-NaCl Electrolytes with Molecular Dynamics Simulations
<p>LAMMPS input-file, and log-files used in "Charge Transport in Water-NaCl Electrolytes with Molecular Dynamics Simulations"</p> <p> </p> <p>DOI: 10.1021/acs.jpcb.2c08047</p>
Surface tension coefficient of acqueous glycerol from Molecular Dynamics simulations
<p>The dataset contains the configuration files and the results of molecular simulations of aqueous glycerol liquid slabs. The aim of the simulations is to determine the surface tension of the interface between aqueous glycerol and its vapour. Simulations are performed with Gromacs 2021. The water model is SPC/E, while the force field for Glycerol is extrapolated from OPLS-AA according to the work by Jahn et al. (<a href="https://doi.org/10.1021/jp5059098">doi.org/10.1021/jp5059098</a>). Surface tension is computed using Gromacs analysis tool by running</p> <pre><code>gmx energy -f ener.edr</code></pre> <p>and selecting the #Surf*SurfTen term, which returns the surface tension (bar*nm) times the number of surfaces (2, due to the system's periodicity). The surface tension is computed from the difference between interface-perpendicular and interface-parallel components of the pressure tensor (see Gromacs documentation or Frenkel and Smit <em>Understanding Molecular Simulations </em>second edition p.472).</p> <p>The nomenclature of the zip files indicates the mass fraction of glycerol, ranging from 0% (Glycerol000) to 100% (Glycerol100).</p>
Molecular Dynamics simulations of acqueous glycerol spreading on a silica-like surface
<p>This dataset contains the results of Molecular Dynamics simulations of quasi-2D water-glycerol liquid droplets, spreading on silica-like surfaces. The goal of the simulations is to quantify the contact line friction coefficient by regressing over the dynamic contact angle and the contact line speed.</p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains configuration files and compressed output molecular trajectories. The contact angle and the contact line speed are computed from density maps binned on-the-fly using a customized Gromacs version (<a href="https://github.com/pjohansson/gromacs-flow-field">https://github.com/pjohansson/gromacs-flow-field</a>); frames are placed in a tarball ("spread-*p-r1.tar.gz").</p> <p>The zipped folder 'scripts.zip' contains a self-contained library of functions to read density maps and a Jupyter notebook with an example of density reading and plotting.</p> <p>Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p>
Molecular dynamics dataset of apo TMPRSS2
<p>This dataset contains all-atom molecular dynamics trajectories of TMPRSS2 (apo). The data was generated with openMM 7.4.0 with the CHARMM36 force field in the NPT ensemble at 310 K. Details about the molecular dynamics setup are given in Hempel, T.; Raich, L.; Olsson, S.; Azouz, N. P.; Klingler, A. M.; Hoffmann, M.; Pöhlmann, S.; Rothenberg, M. E.; Noé, F. Molecular Mechanism of Inhibiting the SARS-CoV-2 Cell Entry Facilitator TMPRSS2 with Camostat and Nafamostat. <em>Chem. Sci.</em> <strong>2021</strong>, 10.1039.D0SC05064D. <a href="https://doi.org/10.1039/D0SC05064D">https://doi.org/10.1039/D0SC05064D</a>.</p> <p>The data was downsampled to a timestep of 1 ns and consists of three parts:</p> <ul> <li> <p><strong>1TMP_crystal7MEQpruned: </strong>Seeded from crystal structure PBD-ID 7MEQ, only catalytic chain. 80 µs cumulative simulation time. </p> </li> <li> <p><strong>1TMP_crystal7MEQ: </strong>Seeded from crystal structure PBD-ID 7MEQ. Both chains. 77 µs cumulative simulation time. </p> </li> <li> <p><strong>1TMP_homology3W94: </strong>Seeded from a homology model [S. Rensi et al., 2020, chemRxiv, DOI: 10.26434/ chemrxiv.12009582.]. 534 µs cumulative simulation time. </p> </li> </ul>
Molecular dynamics dataset of barnase-barstar
<p>This dataset contains all-atom molecular dynamics trajectories of barnase with its inhibitor barstar. All details regarding the molecular dynamics setup are given in reference [1]. The dataset consists of three parts and comes with a time step of 1 ns.</p> <p><strong>amber-adaptive.tar: </strong>adaptive MD trajectories using Amber ff99SB. Cumulative length of about 0.3 ms.</p> <p><strong>amber-gpugrid,.tar: </strong>longer simulations computed on GPUgrid, using Amber ff99SB. Cumulative length of about 1.7 ms.</p> <p><strong>charmm-adaptive.tar: </strong>adaptive MD trajectories using CHARMM36. Cumulative length of about 0.5 ms.</p> <p>[1] Plattner, N.; Doerr, S.; Fabritiis, G. D.; Noé, F. Complete Protein–Protein Association Kinetics in Atomic Detail Revealed by Molecular Dynamics Simulations and Markov Modelling. <em>Nature Chemistry</em> <strong>2017</strong>, <em>9</em> (10), 1005. <a href="https://doi.org/10.1038/nchem.2785">https://doi.org/10.1038/nchem.2785</a>.</p>
Zr–O Ab Initio Training Data Created by Molecular Dynamics, Contour Exploration, and Dimer Searches
<p> These density functional theory calculations span a diverse set of structures in the Zr–O system which was used as machine-learned interatomic potential (MLIP) training data. This data set was used to benchmark different structural evolution methods (molecular dynamics, contour exploration, and dimer searches) for the quality and accuracy of MLIPs trained on them. The data is provided in the .traj format from ASE. Along with data set used in our publication, we provide a large set of extra unused data and a small Python script example for parsing the data set. The set contains 120,068 structures which contain a total of 3,154,158 atoms.</p> <p>For more details, please see our paper:<br> Michael J Waters and James M Rondinelli, <em>J. Phys.: Condens. Matter</em> <strong>34</strong> 385901(2022) (<a href="https://dx.doi.org/10.1088/1361-648X/ac7f73">https://dx.doi.org/10.1088/1361-648X/ac7f73</a>)</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.