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393 results for “Molecular dynamics simulations”
Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8
<p>Trajectory, topology and index files for TLR7 (apo), TLR7-R, TLR7-H, TLR7-G, TLR8 (apo), TLR8-R, TLR8-H, TLR8-G systems. </p>
Figures S1–S10 from: Shoman ME, Abd El-Hafeez AA, Khobrani M, Assiri AA, Al Thagfan SS, Othman EM, Ibrahim ARN (2022) Molecular docking and dynamic simulations study for repurposing of multitarget coumarins against SARS-CoV-2 main protease, papain-like protease and RNA-dependent RNA polymerase. Pharmacia 69(1): 211-226. https://doi.org/10.3897/pharmacia.69.e77021
Molecular docking and Dynamic simulations study for repurposing of multitarget Coumarins against SARS-CoV-2 main protease, papain like protease and RNA-Dependent RNA polymerase.
Molecular Dynamics simulation dataset for : Hypoxia increases the methylated histones to prevent histone clipping and redistribution of heterochromatin during Raf-induced senescence.
<p>Files are Initial structures of Cathepsin L(CTSL) with histone H3 peptides (naive and K23me3) and their Molecular Dynamics simulation trajectories. AutoDock Vina and Charmm were used in H3 peptide docking. </p> <p>Wild type CTSL was modeled with apo-Cathepsin L C25A mutant(PDB ID : 3K24) using Charmm-GUI. Trimethylated histone H3 peptide was generated with PyMol. H3 peptide Molecular Dynamics simulations were performed with GPU-accelerated OpenMM.</p>
All-atom accelerated molecular dynamics (aMD) simulations of Filamin-A (FLNa) actin-binding Domain, immunoglobulin-like Domains 3, 4, 5, 21 and 24 to investagate the impact of known missense mutations associated with periventricular nodular heterotopia in the liveborn males
<p>Data includes all of the wild-type and mutant trajectories of accelerated all-atom molecular dynamics (aMD) simulations of Filamin-A (FLNa, the product of <em>FLNA</em> gene located on chromosome X). Wild-type proteins are from the PDB structures with IDs: 4M9P, 3HOP, 3CNK. The mutations, including R484Q that we discovered in a Turkish family, were formerly found in the liveborn males with <em>FLNA</em>-associated periventricular nodular heterotopia (PNH), who survived with the only copy of mutated <em>FLNA</em>. To understand how these mutations lead to the PNH and simultaneously allow their survival, we performed these MD simulations for the wild-type and mutant systems.</p> <p>Systems were prepared in Visual Molecular Dynamics (VMD 1.9.3) by placing them in a TIP3P water box with approximately 20 Å thickness from the protein surface and neutralizing the system by adding counter ions in the form of NaCl. Of note, only protein parts were kept for the submission to reduce the size of files. Nanoscale Molecular Dynamics (NAMD 2.13-CUDA) was used to perform MD simulations with CHARMM36m force field. For pressure and temperature controls, Nosé-Hoover Langevin barostat and Langevin thermostat were used. ShakeH algorithm of NAMD was applied for water molecule constraints. 12 Å cut-off distance was used for van der Waals interactions. Switching function starts at 10 Å and reaches zero at 14 Å. Integration time-step was 2 fs. To compute the long-range Coulomb interactions, the particle-mash Ewald method was used. After a 10000-step minimization with conjugate gradient algorithm and an equilibration for 1 ns at 298 K under NVT ensemble, production simulations were run along 100 ns. Only the production simulations were supplied in this dataset. Further details are available in the regarding configuration files.</p> <p>Resulting analysis files and scripts are included with the carbon alpha-containing dcd files of the simulations.</p> <p>This dataset is not used directly for any study, but they are preliminary results for the usage of aMD to understand rare disease mechanisms.</p> <p>Related publications:</p> <pre>Zenodo repo of classical MD for these variants: https://doi.org/10.5281/zenodo.4483108</pre> <p>Journal article based on classical MD:</p> <p>Gerlevik U, Saygı C, Cangül H, Kutlu A, Çaralan EF, Topçu Y, et al. (2022) Computational analysis of missense filamin-A variants, including the novel p.Arg484Gln variant of two brothers with periventricular nodular heterotopia. PLoS ONE 17(5): e0265400. https://doi.org/10.1371/journal.pone.0265400</p>
CG molecular dynamics simulations. Supporting data for "Improving Martini 3 for Disordered and Multidomain Proteins".
<pre>Coarse-grained molecular dynamics simulations with Martini 3 with varying rescaling of protein-water interactions. Supporting data for "Improving Martini 3 for Disordered and Multidomain Proteins".</pre>
Simulation input files and analysis scripts for "Optimal bond-constraint topology for molecular dynamics simulations of cholesterol"
<p>Simulation input files and analysis scripts for "Optimal bond-constraint topology for molecular dynamics simulations of cholesterol".</p> <p>See ... for details.</p>
Ligand Field Molecular Dynamics Simulation of Pt(II)-Phenanthroline Binding to N-Terminal Fragment of Amyloid-beta Peptide
<p>DL_POLY Classic input and output files for 10 MD simulations: 5 of free peptide, 5 with Pt(phen)</p>
Molecular dynamics simulations of a4b2 nAChR receptor with epibatidine
<p>Molecular dynamics simulation trajectories of a4b2 nAChR receptor widetype and mutations with epibatidine</p>
Input files for molecular dynamics simulations of holo CaM tagged with Alexa Fluor 488 and Texas Red dyes using Amber20
<p>Here we share the input files for molecular dynamics simulations of holo CaM tagged with Alexa Fluor 488 and Texas Red dyes using Amber20, as well as the TrESP charges used in TrESP-MMPol electronic coupling calculations of FRET properties. Links to the TrADA tool used to derive TrESP charges and the Trespcoup software used to compute electronic couplings for FRET are indicated below:</p> <div> <div> <div> <p>Cupellini, L., Jurinovich, S., & Mennucci, B. (2024). TraDA - Transition Density Analyzer. Zenodo. https://doi.org/10.5281/zenodo.10966411</p> <p>Cupellini, L., Lipparini, F., & Cignoni, E. (2024). trespcoup - Software to compute TrEsp couplings. Zenodo. https://doi.org/10.5281/zenodo.10966391</p> </div> </div> </div>
MDD-Molecular Dynamics Dataset: Collection of protein-ligand complex simulations
<p>Dataset is part of the paper: https://chemrxiv.org/engage/chemrxiv/article-details/664c73f6418a5379b0de8152.</p> <p>This dataset consists of molecular dynamics (MD) simulations of 862 unique protein-ligand complexes, covering a wide range of protein families and diverse chemical classes of ligands. It is derived from publicly available repositories and represents the largest single source of MD simulations to date.</p> <p>All protein-ligand complexes included in the dataset were prepared following a standardized protocol. Missing atoms in the protein structures were added using the PDBFixer tool. The protein targets were parameterized using the AMBER99SB-ILDN force field, while ligands were parameterized with the ANTECHAMBER module within the ACPYPE tool. Ligand partial charges were determined to match the quantum-mechanically generated electrostatic potential via the Restrained Electrostatic Potential (RESP) method, and the remaining parameters were set using the GAFF2 force field. The molecular dynamics simulations were performed using GROMACS. The simulations were configured in a cubic simulation box with periodic boundary conditions and employed a TIP3P water model within an electrostatically neutral environment. The simulation protocol included an initial minimization cycle, followed by temperature equilibration in the NVT ensemble and pressure equilibration in the NPT ensemble. Production simulations were conducted over a period of 200 ns, with a timestep of 100 ps.</p> <p>Constructing a large, representative set of MD simulations poses challenges due to the high computational costs and complexities associated with preparing molecular systems. Moreover, given the limited number of suitable training examples (complexes) and the large volume of MD data from each simulation, careful filtering and feature selection are crucial. This dataset is valuable for exploring how molecular dynamics simulation data can be integrated with protein-ligand binding affinity prediction tasks, an essential component of in silico drug discovery pipelines. MD simulations, in particular, offer a dynamic view by illustrating the temporal interactions within protein-ligand complexes, potentially providing additional insights for affinity and specificity estimates.</p>
Alpha1-antitrypsin molecular dynamics simulations
<p>The zip archive contains molecular dynamics trajectories and related files organized into two main directories: ED for essential dynamics and MD for standard molecular dynamics. Each folder contains two simulations (ed1 and ed2 subfolders of ED; md1 and md2 subfolders for MD).</p> <p>Reference structures, corresponding to the beginning of each simulation are given both as GROMACS gro files and as pdb files. Trajectories are in the xtc GROMACS format, which can be loaded for visualization both in VMD and in Pymol.</p> <p> </p>
Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction
<p>This Zenodo repository provides comprehensive resources for the paper titled "Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction" published on <a href="https://academic.oup.com/bioinformatics/article/41/8/btaf429/8238154">Bioinformatics</a>. We created a dataset of 63,000 molecular dynamics simulations by performing 10 simulations of 10 ns on 6,300 complexes. Neural networks were developed to learn from this data in order to predict the binding affinities of protein-ligand complexes. The implementation of these neural networks are available on <a href="https://github.com/ICOA-SBC/MD_DL_BA" target="_blank" rel="noopener">github</a>. Our collection includes training/benchmark datasets, trained statistical models, and results on test sets (CSV & PDF files).</p> <p> </p> <p><strong>Training/benchmark datasets:</strong></p> <p>Training, validation and test sets are provided to train and evaluate the following neural networks:</p> <ul> <li>Pafnucy, Proli and Densenucy without MD data augmentation (dataset file names contain "initial")</li> <li>Pafnucy, Proli and Densenucy with MD data augmentation (dataset file names contain "MDDA")</li> <li>Pafnucy with/without MD data augmentation and Proli and Densenucy with MD data augmentation were also evaluated on the fep test set (test set file name contain "fep")</li> <li>Timenucy and Videonucy using spatiotemporal learning methods (dataset file names contain "4D")</li> <li>Pafnucy without MD data augmentation and on a reduced training set (dataset file names contain "reduced")</li> </ul> <p>For each training methodology (MD data augmentation and spatiotemporal learning), we provide the data for the whole complex, only the ligand or only the protein. Additionally for spatiotemporal learning, we provide the data with only the ligand using the tracking mode.</p> <p> </p> <p><strong>Statistical models:</strong></p> <p>We provide the models trained with Pafnucy, Proli, Densenucy, Timenucy and Videonucy. Each models were trained in 10 replicates. </p> <p>For Pafnucy, Proli, Densenucy, we provide the models trained with random and systematic rotations, as well as with or without MD data augmentation.</p> <p>For Proli, Densenucy, Timenucy and Videonucy, we provide the models trained on the whole complex, only the ligand or only the protein.</p> <p>For Pafnucy we also provide the models trained on the reduced set (5932 complexes).</p> <p> </p> <p><strong>Results on test sets (CSV & PDF files):</strong></p> <p>We provide the predictions on the PDBbind v.2016 core set.</p> <ul> <li>For spatiotemporal learning methods (Timenucy and Videonucy), there are predictions for only 83 complexes, as we did not perform simulations on the whole test set.</li> <li>For models trained with MD DA, predictions were carried on the crystallographic structures as well as on the frames extracted from the simulations performed on the test set (augmented test).</li> </ul> <p>Results on the FEP dataset are also provided for Pafnucy, Proli and Densenucy.</p> <p> </p> <p>The Raw MD data (~4.5 To) are stored, and can be visualized/downloaded, on the <a href="https://mdposit.mddbr.eu/#/browse?search=MDBind">MDDB</a>.</p> <p>This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-A0100712496 & 2022-AD011013521) and CRIANN (Grant 2021002).</p>
Molecular dynamics simulations of 20 complexes from the Protein-Protein Docking Benchmark
<p>We selected 20 complexes from the Protein-Protein Docking Benchmark 5.0 dataset based on structure resolution and parameterization difficulty. For each complex, we conducted a standard 1 µs-long molecular dynamics (MD) simulation in the NPT ensemble (at 1 atm and 300 K, following a 2 ns NVT equilibration) for the bound receptor, unbound receptor, bound ligand and unbound ligand. We set up all systems using Amber ff14SB<sup> </sup>and its recommended TIP3P water model, running MD simulations with Amber 16. For the 80 (single chain structure) MD, we sampled 500 frames for each simulation and computed the average prediction confidence.</p>
Molecular dynamics simulation trajectory data for "Permeability and ammonia selectivity in aquaporin TIP2;1: linking structure to function"
<p>Trajectories and input files corresponding to entries in Supplementary Table S1.</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 3
<p>Trajectories and input files for the simulations of the Rbfox·pre-miR20b complex.</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 2
<p>Trajectories and input files of the simulations of the free pre-miR20b.</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 7
<p>Simulations of the Rbfox*-miR20b and of the Rbfox-mir20b* complexes.</p>
Molecular dynamics simulations of lipid bilayers containing POPC and POPS with the lipid17 force field, NaCl and KCl salt concentrations
<p>Classical molecular dynamics simulations of various mixtures of POPC:POPS lipid bilayers in water solution at various NaCl, KCl and CaCl2 concentrations, with Na+ counterions (and K+ counterions when noted with "_KCl" suffix).</p> <p>Lipid17 force field parameters used for lipids, TIP3p water model and Dang ions.</p> <p>The file names report the number of additional cations.</p> <p>simulations performed with Gromacs 2018.0 (*.xtc files)</p> <p>simulation length 1000 ns = 1 microsecond</p> <p>temperature 298 K</p> <p>Gromacs simulation setting is in the file npt_lipid_bilayer.mdp</p>
Molecular dynamics simulations of lipid bilayers containing POPC and POPS with the lipid17 force field and ff99 ions
<p>Classical molecular dynamics simulations of various mixtures of POPC:POPS lipid bilayers in water solution at various NaCl, KCl and CaCl2 concentrations, with Na+ counterions (and K+ counterions when noted with "_KCl" suffix).</p> <p>Lipid17 force field parameters used for lipids, TIP3p water model and ff99 ions.</p> <p>The file names report the number of additional cations.</p> <p>simulations performed with Gromacs 2018.0 (*.xtc files)</p> <p>simulation length 1000 ns = 1 microsecond</p> <p>temperature 298 K</p> <p>Gromacs simulation setting is in the file npt_lipid_bilayer.mdp</p>
Molecular dynamics simulations of lipid bilayers containing POPC and POPS with the lipid17 force field, only counterions, and CaCl2 concentrations
<p>Classical molecular dynamics simulations of various mixtures of POPC:POPS lipid bilayers in water solution at various NaCl, KCl and CaCl2 concentrations, with Na+ counterions (and K+ counterions when noted with "_KCl" suffix).</p> <p>Lipid17 force field parameters used for lipids, TIP3p water model and Dang ions.</p> <p>The file names report the number of additional cations.</p> <p>simulations performed with Gromacs 2018.0 (*.xtc files)</p> <p>simulation length 1000 ns = 1 microsecond</p> <p>temperature 298 K</p> <p>Gromacs simulation setting is in the file npt_lipid_bilayer.mdp</p>
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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.
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.