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393 results for “Molecular dynamics simulations”

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zenodo36/100

Molecular dynamics simulation trajectory of a cationic lipid bilayer: 6/94 mol% DMTAP/DMPC in 1.0 M NaCl

System: Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 6 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 94 mol-%) lipids in 1.0 M NaCl solution. Number of DMPC: 120. Number of DMTAP: 8. Number of Na+-ions: 89. Number of Cl--ions: 97. Number of waters: 4921. Lipid model: 'Berger' united-atom [Biophys. J. 72 2002 (1997)] with DMTAP modification by Gurtovenko et al. [Biophys. J. 86 3461 (2004)]. Water model: SPC [In Intermolecular Forces, ed. Pullman. 331 (1981)]. Simulation engine: Gromacs 3.x [www.gromacs.org] Trajectory length: 230 ns. Previously equilibrated for: 12 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with lipids and water coupled separately at T = 323 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with xy and z coupled separately at p = 1.0 bar. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Phys. 103 8577 (1995)], real-space cutoff at 1.0 nm. Van der Waals: Truncated at 1.0 nm. Constraints: Covalent bond lengths in lipids using LINCS [J. Comput. Chem. 18 1463 (1997)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: [1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". J. Phys. Chem. B 113 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.

opencc-by-4.0May 2016View details →

zenodo36/100

Molecular dynamics simulation trajectory of a cationic lipid bilayer: 75/25 mol% DMTAP/DMPC in 1.0 M NaCl

System: Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 75 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 25 mol-%) lipids in 1.0 M NaCl solution. Number of DMPC: 32. Number of DMTAP: 96. Number of Na+-ions: 96. Number of Cl--ions: 192. Number of waters: 5304. Lipid model: 'Berger' united-atom [Biophys. J. 72 2002 (1997)] with DMTAP modification by Gurtovenko et al. [Biophys. J. 86 3461 (2004)]. Water model: SPC [In Intermolecular Forces, ed. Pullman. 331 (1981)]. Simulation engine: Gromacs 3.x [www.gromacs.org] Trajectory length: 110 ns. Previously equilibrated for: 11 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with lipids and water coupled separately at T = 323 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with xy and z coupled separately at p = 1.0 bar. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Phys. 103 8577 (1995)], real-space cutoff at 1.0 nm. Van der Waals: Truncated at 1.0 nm. Constraints: Covalent bond lengths in lipids using LINCS [J. Comput. Chem. 18 1463 (1997)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: [1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". J. Phys. Chem. B 113 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.

opencc-by-4.0May 2016View details →

zenodo36/100

Molecular dynamics simulation trajectory of a cationic lipid bilayer: 50/50 mol% DMTAP/DMPC

System: Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 50 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 50 mol-%) lipids in water. Number of DMPC: 64. Number of DMTAP: 64. Number of Cl--ions: 64. Number of waters: 5336. Lipid model: 'Berger' united-atom [Biophys. J. 72 2002 (1997)] with DMTAP modification by Gurtovenko et al. [Biophys. J. 86 3461 (2004)]. Water model: SPC [In Intermolecular Forces, ed. Pullman. 331 (1981)]. Simulation engine: Gromacs 3.x [www.gromacs.org] Trajectory length: 149 ns. Previously equilibrated for: 41 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with lipids and water coupled separately at T = 323 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with xy and z coupled separately at p = 1.0 bar. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Phys. 103 8577 (1995)], real-space cutoff at 1.0 nm. Van der Waals: Truncated at 1.0 nm. Constraints: Covalent bond lengths in lipids using LINCS [J. Comput. Chem. 18 1463 (1997)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: [1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". J. Phys. Chem. B 113 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.

opencc-by-4.0May 2016View details →

zenodo36/100

Molecular dynamics simulation trajectory of a cationic lipid bilayer: 50/50 mol% DMTAP/DMPC in 0.1 M NaCl

System: Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 50 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 50 mol-%) lipids in 0.1 M NaCl solution. Number of DMPC: 64. Number of DMTAP: 64. Number of Na+-ions: 10. Number of Cl--ions: 74. Number of waters: 5316. Lipid model: 'Berger' united-atom [Biophys. J. 72 2002 (1997)] with DMTAP modification by Gurtovenko et al. [Biophys. J. 86 3461 (2004)]. Water model: SPC [In Intermolecular Forces, ed. Pullman. 331 (1981)]. Simulation engine: Gromacs 3.x [www.gromacs.org] Trajectory length: 190 ns. Previously equilibrated for: 21 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with lipids and water coupled separately at T = 323 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with xy and z coupled separately at p = 1.0 bar. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Phys. 103 8577 (1995)], real-space cutoff at 1.0 nm. Van der Waals: Truncated at 1.0 nm. Constraints: Covalent bond lengths in lipids using LINCS [J. Comput. Chem. 18 1463 (1997)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: [1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". J. Phys. Chem. B 113 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.

opencc-by-4.0May 2016View details →

zenodo36/100

Molecular dynamics simulation trajectory of a cationic lipid bilayer: 75/25 mol% DMTAP/DMPC in 0.1 M NaCl

System: Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 75 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 25 mol-%) lipids in 0.1 M NaCl solution. Number of DMPC: 32. Number of DMTAP: 96. Number of Na+-ions: 10. Number of Cl--ions: 106. Number of waters: 5476. Lipid model: 'Berger' united-atom [Biophys. J. 72 2002 (1997)] with DMTAP modification by Gurtovenko et al. [Biophys. J. 86 3461 (2004)]. Water model: SPC [In Intermolecular Forces, ed. Pullman. 331 (1981)]. Simulation engine: Gromacs 3.x [www.gromacs.org] Trajectory length: 110 ns. Previously equilibrated for: 11 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with lipids and water coupled separately at T = 323 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with xy and z coupled separately at p = 1.0 bar. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Phys. 103 8577 (1995)], real-space cutoff at 1.0 nm. Van der Waals: Truncated at 1.0 nm. Constraints: Covalent bond lengths in lipids using LINCS [J. Comput. Chem. 18 1463 (1997)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: [1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". J. Phys. Chem. B 113 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.

opencc-by-4.0May 2016View details →

zenodo36/100

Molecular dynamics simulation trajectory of a cationic lipid bilayer: 75/25 mol% DMTAP/DMPC in 0.5 M NaCl

System: Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 75 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 25 mol-%) lipids in 0.5 M NaCl solution. Number of DMPC: 32. Number of DMTAP: 96. Number of Na+-ions: 49. Number of Cl--ions: 145. Number of waters: 5398. Lipid model: 'Berger' united-atom [Biophys. J. 72 2002 (1997)] with DMTAP modification by Gurtovenko et al. [Biophys. J. 86 3461 (2004)]. Water model: SPC [In Intermolecular Forces, ed. Pullman. 331 (1981)]. Simulation engine: Gromacs 3.x [www.gromacs.org] Trajectory length: 110 ns. Previously equilibrated for: 11 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with lipids and water coupled separately at T = 323 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with xy and z coupled separately at p = 1.0 bar. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Phys. 103 8577 (1995)], real-space cutoff at 1.0 nm. Van der Waals: Truncated at 1.0 nm. Constraints: Covalent bond lengths in lipids using LINCS [J. Comput. Chem. 18 1463 (1997)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: [1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". J. Phys. Chem. B 113 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.

opencc-by-4.0May 2016View details →

zenodo36/100

Molecular dynamics simulation trajectory of a cationic lipid bilayer: 75/25 mol% DMTAP/DMPC

System: Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 75 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 25 mol-%) lipids in water. Number of DMPC: 32. Number of DMTAP: 96. Number of Cl--ions: 96. Number of waters: 5496. Lipid model: 'Berger' united-atom [Biophys. J. 72 2002 (1997)] with DMTAP modification by Gurtovenko et al. [Biophys. J. 86 3461 (2004)]. Water model: SPC [In Intermolecular Forces, ed. Pullman. 331 (1981)]. Simulation engine: Gromacs 3.x [www.gromacs.org] Trajectory length: 109 ns. Previously equilibrated for: 31 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with lipids and water coupled separately at T = 323 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984)] with xy and z coupled separately at p = 1.0 bar. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Phys. 103 8577 (1995)], real-space cutoff at 1.0 nm. Van der Waals: Truncated at 1.0 nm. Constraints: Covalent bond lengths in lipids using LINCS [J. Comput. Chem. 18 1463 (1997)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: [1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". J. Phys. Chem. B 113 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.

opencc-by-4.0May 2016View details →

zenodo36/100

Simulation snapshots of complex I molecular dynamics simulation

These are snapshots from the molecular dynamics simulations of complex I (setups I and II). These can be loaded in the VMD software for visualization, and 'segname' keyword can be used to distinguish the different parts of the protein.

opencc-by-4.0Feb 2017View details →

dryad36/100

Molecular Dynamics Simulations and associated data for: Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases

Catalytic signaling outputs of protein kinases are dynamically regulated by an array of structural mechanisms, including allosteric interactions mediated by intrinsically disordered segments flanking the conserved catalytic domain. The Doublecortin Like Kinases (DCLKs) are a family of microtubule-associated proteins characterized by a flexible C-terminal autoregulatory 'tail' segment that varies in length across the various human DCLK isoforms. However, the mechanism whereby these isoform-specific variations contribute to unique modes of autoregulation is not well understood. Here, we employ a combination of statistical sequence analysis, molecular dynamics simulations and in vitro mutational analysis to define hallmarks of DCLK family evolutionary divergence, including analysis of splice variants within the DCLK1 sub-family, which arise through alternative codon usage and serve to 'supercharge' the inhibitory potential of the DCLK1 C-tail. We identify co-conserved motifs that readily distinguish DCLKs from all other Calcium Calmodulin Kinases (CAMKs), and a 'Swiss-army' assembly of distinct motifs that tether the C-terminal tail to conserved ATP and substrate-binding regions of the catalytic domain to generate a scaffold for auto-regulation through C-tail dynamics. Consistently, deletions and mutations that alter C-terminal tail length or interfere with co-conserved interactions within the catalytic domain alter intrinsic protein stability, nucleotide/inhibitor-binding, and catalytic activity, suggesting isoform-specific regulation of activity through alternative splicing. Our studies provide a detailed framework for investigating kinome–wide regulation of catalytic output through cis-regulatory events mediated by intrinsically disordered segments, opening new avenues for the design of mechanistically-divergent DCLK1 modulators, stabilizers or degraders.

opencc-zeroOct 2023View details →

zenodo36/100

Rate-enhancing PETase mutations determined through DFT/MM molecular dynamics simulations†

Raw data for classical MD simulations ran with Gromacs 2018.3 for the two mutants Asp83Asn and Asp89Asn.Raw data for quantum mechanics/molecular mechanics simulations ran with CP2K 6.1 for the two mutants Asp83Asn and Asp89Asn.Distance and free energy analysis from the QM/MM MD simulations  for the wild-type and the two mutants Asp83Asn and Asp89Asn.

opencc-by-4.0Nov 2023View details →

zenodo36/100

The steered discrete molecular dynamics simulation data of amyloids with EC1 and EC12 cadherin dimer

The steered discrete molecular dynamics (sDMD) simulation parameters are provided. Binding frequency of amyloids with EC1 and EC1-2 cadherin dimer. Trajectories of sDMD simulations of EC1 cadherin dimer with Abeta species.

opencc-by-4.0Jul 2023View details →

zenodo36/100

Simulations for "Molecular Dynamics-Based Identification of Binding Pathways and Two Distinct High-Affinity Sites for Succinate in the Succinate Receptor 1 SUCNR1/GPR91"

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2023View details →

dryad36/100

Data from: programming co-assembled peptide nanofiber morphology via anionic amino acid type: insights from molecular dynamics simulations

Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecular architecture. Here we use molecular dynamics simulations to quantify the effect of anionic amino acid type on co-assembly dynamics and nanofiber structure in binary CATCH(+/-) peptide systems. CATCH peptide sequences follow a general pattern: CQCFCFCFCQC, where all C's are either a positively charged or a negatively charged amino acid. Specifically, we investigate the effect of substituting aspartic acid residues for the glutamic acid residues in the established CATCH(6E-) molecule, while keeping CATCH(6K+) unchanged. Our results show that structures consisting of CATCH(6K+) and CATCH(6D-) form flatter β-sheets, have stronger interactions between charged residues on opposing β-sheet faces, and have slower co-assembly kinetics than structures consisting of CATCH(6K+) and CATCH(6E-). Knowledge of the effect of sidechain type on assembly dynamics and fibrillar structure can help guide the development of advanced biomaterials and grant insight into sequence-to-structure relationships.

opencc-zeroNov 2023View details →

zenodo36/100

Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate – Absorption Spectra and Global Analysis Results, Molecular Dynamics Simulations

Time-resolved absorption and molecular dynamics trajectory datasets associated with: Rose, J. B.; Gascón, J. A.; Sutter, M.; Sheppard, D. I.; Kerfeld, C. A.; Beck, W. F. Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate. Phys. Chem. Chem. Phys. 2023, DOI: 10.1039/d3cp04484j.

opencc-by-4.0Dec 2023View details →

zenodo36/100

Molecular dynamics simulation of CFTR with inhibitor CFTRinh-172

Molecular dynamics simulation trajectory, parameter files for the systems of human CFTR protein with inhibitor CFTRinh-172. CFTRinh-172 was tested with two different poses.

opencc-by-4.0Dec 2023View details →

zenodo36/100

All-atom molecular dynamics simulations of incomplete ATP synthase rotor rings with unusually high stoichiometry predicted by the AlphaFold2-based method

The trajectories of all-atom MD simulations of AlphaFold2 4, 11, 16 or 18-mer structures of the subunit c from Candidatus Kryptonium thompsoni (CKt_Nmer_lipid_mix_CHM36m_303K_500ns) and Thalassoglobus polymorphus (Tp_Nmer_lipid_mix_CHM36m_303K_500ns), and AlphaFold2 11-mer structure of the subunit c from Spinacia oleracea (So_11mer-c20_POPC_CHM36m_303K_300ns) in a lipid bilayer. Simulations have been performed using the CHARMM36m force field, running with the GROMACS 2022 package.

opencc-by-4.0Feb 2024View details →

zenodo36/100

Molecular dynamic simulations of histamine-bound H4R

MD simulations data for Histamine-bound H4R

opencc-by-4.0Mar 2024View details →

zenodo36/100

Molecular dynamics simulation of MFSD1 in apo, His-Ala, Lys-Ala and Leu-Ala bound

The MFSD1 structures were placed in a heterogenous bilayer composed of POPE (20%), 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC, 30%), Cholesterol (30%), and N-Palmitoyl-sphingomyelin (SPM, 20%) using CHARMM-GUI scripts and all simulations were performed using GROMACS 2021.3.  Substrates were fitted into the binding site based on non-protein density observed in the outward-open Cryo-EM structure of GLMP-MFSD1+HisAla.  Here, inital structures and 500 ns simulations for each replicate (Rep1-3) with the respective ligands and its starting conformation (Conf1 or Conf2) are given in PDB-format.  The following ligands were used for the molecular dynamics simulations: <ul> <li>LA - Leucyl-alanine dipeptide: both termini are charged</li> <li>KA - Lysyl-alanine dipeptide: both termini are charged, side chain of lysine is positively charged</li> <li>H0A - Histidyl-alanine dipeptide: both termini are charged, side chain of histidine is neutral</li> <li>HA - Histidyl-alanine dipeptide: both termini are charged, side chain of histidine is positively charged</li> </ul>

opencc-by-4.0Dec 2023View details →

zenodo36/100

03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  # The forders are organized as: 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. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

opencc-zeroApr 2024View details →

zenodo36/100

06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  # The folders are organized as: 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. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

opencc-zeroApr 2024View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record