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356 results for “In silico”
Dataset for article: Antimicrobial peptide induced colloidal transformations in bacteria-mimetic vesicles: Combining in silico tools and experimental methods
<p><strong>Dataset for publication:</strong></p><p>Antimicrobial peptide induced colloidal transformations in bacteria-mimetic vesicles: Combining in silico tools and experimental methods<br><i>Rafael V.M. Freire, Yeny Pillco-Valencia, Gabriel C.A. da Hora, Madeleine Ramstedt, Linda Sandblad, Thereza A. Soares, Stefan Salentinig</i><br>Journal of Colloid and Interface Science Volume 596, 15 August 2021, Pages 352-363 https://doi.org/10.1016/j.jcis.2021.03.060</p><p>Setup and conditions for the experiments are described in the experimental section of the published (open access) manuscript.</p><p>Data description in README.txt file.</p>
The Right Atrium Affects In Silico Arrhythmia Vulnerability in Both Atria
<h1>The Right Atrium Affects In Silico Arrhythmia Vulnerability in Both Atria</h1> <div> </div> <div><strong>Authors:</strong> Patricia Martínez Díaz, Jorge Sánchez, Nikola Fitzen, Ursula Ravens, Olaf Dössel, Axel Loewe</div> <div>patricia.martinez@kit.edu / publications@ibt.kit.edu</div> <div><a href="https://doi.org/10.1016/j.hrthm.2024.01.047">doi:10.1016/j.hrthm.2024.01.047</a></div> <div> </div> <div>This dataset contains 8 biatrial meshes and 8 monoatrial (left-only) meshes, derived from MRI and CT segmentations with annotations and fibers, ready for simulations in the cardiac electrophysiology simulator <a href="https://doi.org/10.1016/j.cmpb.2021.106223">openCARP</a>. We also provide the code to reproduce a total of 576 reentries by reading the selected parameters.par and state.roe files. The meshes were generated using <a href="https://github.com/KIT-IBT/AugmentA">AugmentA code</a> and the simulated reentries were induced following the <a href="https://doi.org/10.3389/fphys.2021.656411">PEERP protocol</a> by Azzolin et al. A carputils bundle containing the <a href="https://doi.org/10.35097/1830">openCARP experiment</a>, along with all associated parameters, is publicly available. The original cardiac segmentations are part of Krueger M. et al. Personalization of atrial anatomy and electrophysiology as a basis for clinical modeling of radio-frequency ablation of atrial fibrillation <a href="https://doi.org/10.1109/tmi.2012.2201948">doi:10.1109/TMI.2012.2201948</a></div> <div> </div> <h2>Folder structure</h2> <div>The code is located in the `src` folder, the meshes in the `data` folder and the reentries in the `results` folder. </div> <div>```</div> <div>KIT_2/</div> <div>|-- src/</div> <div> |-- run.py</div> <div> |-- induceReentry.py</div> <div> |-- getStimPoints.py</div> <div> |-- element_tag.csv</div> <div> |-- al_mk_H.par</div> <div> |-- requirements.txt</div> <div> |-- reproduceReentry.py</div> <div>|-- data/</div> <div> |-- meshes/</div> <div> |-- P1/ </div> <div> |-- monoatrial/ </div> <div> |-- biatrial/</div> <div>.</div> <div>.</div> <div>.</div> <div> |-- P8 </div> <div> |-- monoatrial/ </div> <div> |-- biatrial/</div> <div>|-- results/</div> <div>|-- MESH_SCENARIO_STATE_CHAMBER/ (e.g P1_bi_M_LA) </div> <div>|-- point_X_beat_Y</div> <div>| </div> <div>|-- README.md</div> <div>```</div> <div> </div> <div>`src`: contains the source files needed to run PEERP protocol </div> <div> <ul> <li>`run.py` This is the main function to run the pacing protocol (not needed to run if reentries are only reproduced, check reproduceReentry.py)</li> <li>`induceReentry.py` Contains a list of pacing protocols. The PEERP protocol is included here</li> <li>`getStimPoints.py` Extract the stimulation points</li> <li>`element_tag.csv` Region tag numbering</li> <li>`al_mk_H.par` Par file with ionic scaling factors for three states; H:Healthy, M:Mild, S:Severe</li> <li>`requirements.txt` Packages to create the virtual enviroment. (This was my output of ```pip3 list> requirements.txt```)</li> <li>`reproduceReentry.py` Reentries can be reproduced given a selected folder where the .par and .roe files are stored.</li> </ul> </div> <div> </div> <div>`data`: contains the `meshes` folder with the bilayer meshes in openCARP (.elem, .lon and .pts) and .vtk format. Synthetic fibrotic distributions are included in the the .regele files.</div> <div> <ul> <li>`meshes/P1/monoatrial/LA_stim_points.txt` stimulation points for the PEERP protocol</li> <li>`meshes/P1/monoatrial/LA_bilayer_with_fiber_elems_not_conductive_M.regele` Element ids corresponding to synthetic fibrotic distribution with respect to remodeling states M and S. H state was modelled without fibrosis</li> </ul> </div> <div> </div> <h2>Reproduce the reentries </h2> <div>You can generate the .igb file of a specific reentry by selecting the corresponding folder in the results directory. An example is given to reproduce the reentry in P1_bi_M_LA/point_0_beat_2/reproduce_reentry.igb. Select the folder `--par_file_directory`and set `--tend` to define the duration of the simulation in miliseconds.</div> <div>_HINT: We recommend keeping the folder structure so that the other parameters, such as: mesh, scenario, state and chamber, can be read from the --par_file_directory. Otherwise, the meshes and results directories need to be modified._</div> <div>```</div> <div>cd src/</div> <div>reproduceReentry.py --par_file_directory ../results/P1_bi_M_LA/point_0_beat_2 --tend 1500</div> <div> </div> <div>```</div> <div></div> <div> </div> <div> </div> <h2>Preparation before running the PEERP pacing protocol</h2> <div> </div> <div>Follow the next steps if you want to run the PEERP pacing protocol, either for the provided meshes or for your own meshes. To run the PEERP protocol in a controlled environment, it is recommended, before running the run.py, to create a virtual environment. Go to your terminal and type: </div> <div>```</div> <div>cd src/</div> <div>python3 -m venv ./myEnv</div> <div>source ./myEnv/bin/activate</div> <div>pip3 install -r requirements.txt</div> <div>```</div> <div> </div> <div>You need to add carputils to your `PATH`. You can run the code in the terminal or use and IDE to debug the code. </div> <div>Note: I am using PyCharm 2020.3. and in Settings --> Python interpreter --> show all and then in the (+) symbol, add the path to carputils there:</div> <div> </div> <div>Otherwise you can add this extra lines at the beginning of `run.py``:</div> <div>```</div> <p># Replace '/path/to/carputils' with the actual path to your carputils package</p> <p>carputils_path = '/path/to/carputils'</p> <p># Add the carputils path to sys.path</p> <div>sys.path.append(carputils_path)</div> <div>```</div> <h2>Run the PEERP protocol</h2> <div> </div> <div>The following example runs the PEERP from a single stimulation point. If you want to run PEERP over all the points, simply add the flag --run_all_points 1 </div> <div>```</div> <div>cd src/</div> <div>python3 run.py --giL 0.4166 --geL 1.458 --cv 0.8 --mesh monoatrial --protocol PEERP --pacing 122718 --stim_file LA_stim_points.txt --geometry LA_bilayer_with_fiber_um --cell_bcl 500 --model Courtemanche --ionic_prop_file al_mk_S.par --max_n_beats_PEERP 1 --overwrite-behaviour overwrite</div> <div>```</div> <div> </div> <h2>Running your own experiment and making your own changes</h2> <div>Extract the stimulation points on your mesh, where the PEERP protocol will be run: </div> <div>```</div> <div>python3 getStimPoints.py --mesh monoatrial --tolerance 20000 --stim_file LA_stim_points.txt --chamber LA</div> <div>```</div> <div> </div> <div>Tune conduction velocity (CV) and conductivites. The code expects the intracellular end extracellular longitudinal conductivity values as an input. We used `tuneCV` to fit CV=0.7m/s with dx=0.4mm and dt=20us</div> <div>If you want to adjust the values, run in the terminal:</div> <div>```</div> <div>tuneCV --resolution 400 --model Courtemanche --velocity 0.7 --converge True --sourceModel monodomain --surf True --dt 20</div> <div>```</div> <div>You can provide the location of the start of the activation by selecting the desired point ID:</div> <div> <ul> <li>Load the mesh in Paraview (or Meshalyzer)</li> <li>click on the ? symbol</li> <li>save the ID and change the `--pacing` argument </li> </ul> </div> <div> </div> <div>Call `run.py` with a new mesh. The protocol starts by prepacing the mesh and then using the last beat as initial condition tu run the PEERP.</div> <div>Be aware that for a monoatrial mesh you might need to give the new id for the location of the earliest activation. Change `12345` to your desired point ID.</div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol prepace --stim_file LA_stim_points.txt</div> <div>```</div> <div> </div> <div>Run the protocol with different electrical remodelling stage. You can change the .par file or select one file from the three provided: </div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol PEERP --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <div>```</div> <div> </div> <div>You can also try to run a biatrial example. The biatrial mesh is also provided. You need to extract the points on the RA surface using `getStimPoints.py`, to run the RA experiment: </div> <div>```</div> <div>cd src</div> <div>python3 getStimPoints.py --mesh biatrial --tolerance 20000 --stim_file RA_stim_points.txt --chamber RA</div> <div>```</div> <div>Then run PEERP twice, one per each chamber:</div> <div> </div> <div>```</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --pacing 12345 --stim_file LA_endo_2cm.txt --args.ionic_prop 'l_mk_M.par'</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <p> </p>
Kinodata-3D: an in silico kinase-ligand complex dataset for kinase-focused machine learning.
<p><strong>Project Description</strong></p> <p>Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While the inclusion of 3D complex information is considered to be beneficial, structural ML for affinity prediction suffers from data scarcity. <br>We provide kinodata-3D, a dataset of <strong>~138 000</strong> docked complexes to enable more robust training of 3D-based ML models for kinase activity prediction (see <a href="https://github.com/volkamerlab/kinodata-3D-affinity-prediction">github.com/volkamerlab/kinodata-3D-affinity-prediction</a>).</p> <h2>Dataset</h2> <h3>1. Data</h3> <p>This data set consists of three-dimensional protein-ligand complexes that were generated using computational docking from the OpenEye toolkit. The modeled proteins cover the kinase family for which a fair amount of structural data, i.e. co-crystallized protein-ligand complexes in the PDB, enriched through KLIFS annotations, is available. This enables us to use template docking (OpenEye’s POSIT functionality) in which the ligand placement is guided according to a similar co-crystallized ligand pose. The kinase-ligand pairs to dock are sourced from binding assay data via the public ChEMBL archive, version 33. In particular, we use kinase activity data as curated through the <a href="https://github.com/openkinome/kinodata">OpenKinome kinodata</a> project. The final protein-ligand complexes are annotated with a predicted RMSD of the docked poses. The RMSD model is a simple neural network trained on a <a href="https://github.com/openkinome/kinase-docking-benchmark">kinase-docking benchmark</a> data set using ligand (fingerprint) similarity, docking score (ChemGauss 4), and Posit probability (see <a href="https://github.com/volkamerlab/kinodata-3D" target="_blank" rel="noopener">kinodata-3D repository</a>).</p> <p>The final data set contains in total <strong>138 286</strong> deduplicated kinase-ligand pairs, covering <strong>~98 000</strong> distinct compounds and ~<strong>271</strong> distinct kinase structures.</p> <h3>2. File structure</h3> <p>The archive <strong>kinodata_3d.zip </strong>uses the following file structure</p> <blockquote> <p>data/raw<br> | kinodata_docked_with_rmsd.sdf.gz<br> | pocket_sequences.csv<br> | mol2/pocket<br> | 1_pocket.mol2<br> | ...</p> </blockquote> <p>The file <strong>kinodata_docked_with_rmsd.sdf.gz</strong> contains the docked ligand poses and the information on the protein-ligand pair inherited from <em>kinodata</em>. The protein pockets located in <strong>mol2/pocket</strong> are stored according to the MOL2 file format.</p> <p>The pocket structures were sourced from KLIFS (<a href="https://klifs.net" target="_blank" rel="noopener">klifs.net)</a> and complete the poses in the aforementioned SDF file. The files are named <strong>{klifs_structure_id}_pocket.mol2</strong>. The structure ID is given in the SDF file along with the ligand poses.</p> <p>The file <strong>pocket_sequences.csv </strong>contains all KLIFS pocket sequences relevant to the kinodata-3D dataset.</p> <h3>3. Related code</h3> <p>The code used to create the poses can be found in the <a href="https://github.com/volkamerlab/kinodata-3D" target="_blank" rel="noopener">kinodata-3D repository</a>. The docking pipeline makes heavy use of the <a href="https://github.com/openkinome/kinoml" target="_blank" rel="noopener">kinoml</a> framework, which in turn uses <a href="https://www.eyesopen.com" target="_blank" rel="noopener">OpenEye's</a> Posit template docking implementation. The details of the original pipeline can also be found in the manuscript by <a href="https://www.biorxiv.org/content/10.1101/2023.09.11.557138v1">Schaller et al. (<strong>2023</strong>). Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery. <em>bioRxiv</em>.</a></p>
Distinct binding hotspots for natural and synthetic agonists of FFA4 from in silico approaches
<p>Compressed trajectories files of natural and synthetic ligands (TUG-891, alpha-Linolenic acid, Linoleic acid and Oleic acid) as supporting information for a research article in Molecular Informatics exploring binding hotspots of FFA4.</p> <p>Each compressed trajectories comprise of a pdb file (xx_Rx.pdb) and its associated trajectory file (xx_Rx_TRJ.trr ) to be read using vmd.</p> <p> </p> <p> </p>
An 8-(Diazomethyl) Quinoline Derivatized Acyl-CoA In Silico Mass Spectral Library Reveals the Landscape of Acyl-CoA in the Aging Mouse Organs (Data Supplement)
<p>Data supplement for publication "An 8-(Diazomethyl) Quinoline Derivatized Acyl-CoA In Silico Mass Spectral Library Reveals the Landscape of Acyl-CoA in the Aging Mouse Organs (Data Supplement)" (2024)</p> <p>Jinhui Yu<sup>1†</sup>, Menghao Guo<sup>1,3†</sup>, Sha Li<sup>5</sup>, Jian Ni<sup>2</sup>, Yu-Qi Feng<sup>4,5</sup>*, Jun Ding<sup>1,2</sup>*</p> <p>1. CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, PR China.</p> <p>2. Renmin Hospital of Wuhan University, Wuhan University, 430072, Wuhan, P. R. China.</p> <p>3. College of Life Sciences, Wuhan University, Wuhan 430072, China.</p> <p>4. School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, China.</p> <p>5. Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, 430071, China.</p> <p> </p> <p>†The authors contribute equally to this work.</p> <p>* Corresponding author Email: <a href="mailto:dingjun@wbgcas.cn">dingjun@wbgcas.cn</a>, <a href="mailto:yqfeng@whu.edu.cn">yqfeng@whu.edu.cn</a></p> <p>----<br>Content:<br>1) Developement: XLS template sheet for development (can be used to adjust or create new library)<br>2) MSP library: spectra in NIST MSP format (use with MS-Dial or NIST MS Search)<br>3) NIST library: NIST23 compatible 8-DMQ-acyl-CoA library (use with NIST MS Search)<br>4) Reference spectra msp: 8-DMQ-acyl-CoA authentic MS/MS spectrum in NIST MSP format (generated by Thermo QE HF-X MS (HCD), for searching NIST MS-Search)</p> <p>Version 1.0<br>April 22 2024</p>
ISDB: In Silico Spectral Database (of Natural Products)
<h1><strong>ISDB: In Silico DataBase (of Natural Products)</strong></h1> <h2><strong>Background</strong></h2> <p>The ISDB repository contains in silico fragmented spectra of natural products, generated using <strong>cfm-predict 4</strong> <a href="https://doi.org/10.1021/acs.analchem.1c01465" target="_new" rel="noopener">(DOI: 10.1021/acs.analchem.1c01465)</a>.</p> <p>The initial database preparation and its application for dereplication were first described in:<br><strong>Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication</strong> <a href="https://doi.org/10.1021/acs.analchem.5b04804" target="_new" rel="noopener">(DOI: 10.1021/acs.analchem.5b04804)</a>.</p> <h2><strong>Content</strong></h2> <p>Previous versions of ISDB contained predicted spectra for compounds aggregated within the <strong>LOTUS Initiative</strong> <a href="https://doi.org/10.7554/eLife.70780" target="_new" rel="noopener">(DOI: 10.7554/eLife.70780)</a> and included only "merged" spectra.</p> <p>This version expands beyond taxon-restricted compounds and includes:</p> <ul> <li>Individual in silico spectra predictions (<strong>~1 million</strong>)</li> <li><strong>Substructure annotations</strong></li> <li>Data for compounds from <strong>Wikidata</strong> and <strong>LOTUS</strong></li> </ul> <h2><strong>File Structure</strong></h2> <h3><strong><code>.zip</code> Files</strong></h3> <p>Each <code>.zip</code> file contains:</p> <ul> <li>All original outputs from <strong>cfm-predict 4</strong></li> <li>Substructure annotations</li> </ul> <h3><strong>Wikidata vs LOTUS Files</strong></h3> <ul> <li><strong>Wikidata files</strong>: Contain spectra for nearly all compounds found in Wikidata, obtained using this query: <a href="https://w.wiki/Cvdo" target="_new" rel="noopener">https://w.wiki/Cvdo</a>.</li> <li><strong>LOTUS files</strong>: Contain spectra only for compounds with a "found in taxon" (<code>P703</code>) statement, obtained using this query: <a href="https://w.wiki/D35x" target="_new" rel="noopener">https://w.wiki/D35x</a>.</li> </ul> <h3><strong>Energy Levels: <code>energyAll</code> vs <code>energySum</code></strong></h3> <ul> <li><strong><code>energyAll</code> files</strong>: Include spectra for three individual energy levels plus a summed spectrum (4 spectra per polarity per compound).</li> <li><strong><code>energySum</code> files</strong>: Contain only the summed spectrum (1 spectrum per polarity per compound).</li> </ul> <h2><strong>Related Resources</strong></h2> <ul> <li><strong>Building scripts</strong>: <a href="https://github.com/mandelbrot-project/spectral_lib_builder" target="_new" rel="noopener">https://github.com/mandelbrot-project/spectral_lib_builder</a></li> <li><strong>Matching scripts</strong>: <a href="https://github.com/mandelbrot-project/spectral_lib_matcher" target="_new" rel="noopener">https://github.com/mandelbrot-project/spectral_lib_matcher</a></li> </ul>
Metadata of "Effect of chemical substitution on the surface charge of the photosynthetic Reaction Center from Rhodobactersphaeroides: an in-silico investigation"
<p>Metadata of "Effect of chemical substitution on the surface charge of the photosynthetic Reaction Center from Rhodobactersphaeroides: an in-silico investigation"</p>
Dataset 2 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution
<p><strong>Dataset</strong></p> <p>Molecular dynamics simulation trajectories of TTR peptide aggregation kinetics:</p> <ul> <li>multi-eGO-XXmM-Y: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration replicate Y.</li> </ul>
ARCHIMED-φ simulation files for the simulation of Design A from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal
<p>Input files for the simulation of Design A in ARCHIMED-φ from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal.</p> <p>See https://archimed-platform.github.io/archimed-phys-user-doc/ for more details on the model.</p> <p>Make a simulation by opening a terminal at the root of the folder and type: `java -jar .\archimed-phys.jar .\DesignA_MockUpA_seed1_MAP_72.yml`.</p>
In silico data-set used to train the PGNNIV
<p>The folder contains the data used to train PGNNIVs to unravel the go or grow behaviour of glioblastoma under 4 different parametric models. These data consist on the solutions (in time and space) of eleven simulations with different oxygen boundary conditions.</p> <p>Each folder is named as DATA_"ModelName", where "ModelName" can be: "Sigmoid", "ReLU", "MichaelisMenten" or "Heaviside". Inside each folder, the multidimensional arrays for input and output data for the network training can be found. These arrays have dimension [nExp,TimeStep,x,field], where:</p> <ul> <li>nExp = 11 and corresponds to the number of different configurations or experiments simulated.</li> <li>TimeStep = 1000 and correspond to the different temporal frames where the solution is given.</li> <li>x = 51 and corresponds to the different spatial points where the solution is given.</li> <li>field = 2 and correspond to the different solution fields (1: cells, 2: oxygen).</li> </ul>
Identification of potential modulators of IFITM3 by in-silico modeling and virtual screening
<p>Modeled structure of IFITM3 and Desmond MD trajectory files for IFITM3 and IFITM3-ligand complexes. Please see README file.</p>
Dataset for "In silico Positional Analogue Scanning with Amber GPU-TI"
<p>This repository contains the full data set and analysis scripts to reproduce all results for the manuscript "<a href="https://pubs.acs.org/doi/10.1021/acs.jcim.2c00860"><strong>In silico Positional Analogue Scanning with Amber GPU-TI</strong>, <em>J. Chem. Inf. Model.</em> 2022, 62, 18, 4448–4459</a>".</p> <p><a href="https://doi.org/10.1021/acs.jcim.2c00860">https://doi.org/10.1021/acs.jcim.2c00860</a></p> <p><br> The repository contains the following data:</p> <ul> <li><strong>20_PDB_66_MOL2_input_coordinates_for_PAS.tar.gz (7.7MB)</strong> <ul> <li>input structures of proteins (pdb format), ligands (mol2 format), experimental data and GPU-TI maps for all the 20 scans including Br-Scan, Cl-Scan, F-Scan, HO-Scan, MeO-Scan, Me-Scan, N-Scan.</li> </ul> </li> <li><strong>AMBER18.GPUTI.scripts.tar.gz (597.2 M)</strong> <ul> <li>Scripts and example of CDK8 TI output for GPU TI ddG calculation and cycle closure correlation.</li> </ul> </li> <li><strong>Supporting_Information_Tables_dG_ddG_small_big_change.xlsx (60kB)</strong></li> <li>AMBER18_input_fort files, parameter and topology files, first 500 ps equilibrated restart files, amber TI input files for each TI pair calculations (total 12.9 GB) <ul> <li><strong>AMBER-GPUTI_PAS_input.01.N-Scan.CDK8.tar.gz (736.2MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.02.N-Scan.Tankyrase.tar.gz (266.1 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.03.N-Scan.HCV_NS5B_gt1b.tar.gz (722.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.04.F-Scan.ox1r_antagonist.tar.gz (549.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.05.F-Scan.ox2r_antagonist.tar.gz (768.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.06.F-Scan.KAT6A.tar.gz (407.9 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.07.F-Scan.PDE1B.tar.gz (470.7 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.08.F-Scan.Akt1_kinase.tar.gz (390.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.09.Cl-Scan.PPAR_Gama.tar.gz (412.9 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.10.Cl-Scan.erk12.tar.gz (352.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.11.Cl-Scan.KAT6A.tar.gz (408.2 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.12.Br-Scan.PRMT4.tar.gz (522.1 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.13.Me-Scan.BD1_scaffold_thiophene.tar.gz (187.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.14.Me-Scan.BD1_scaffold_furan.tar.gz (187.3 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.15.Me-Scan.HIV-1.tar.gz (740.2 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.16.Me-Scan.PPAR_Gama.tar.gz (412.8 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.17.Me-Scan.avb6.tar.gz (2.2 GB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.18.MeO-Scan.KAT6A.tar.gz (409.3 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.19.MeO-Scan.ox2r_agonist.tar.gz (1.1 GB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.20.HO-Scan.ox2r_agonist.tar.gz (1.1 GB)</strong><br> </li> </ul> </li> <li><strong>Data Structures inside each files:</strong> <ul> <li><strong>directory_tree.20_PDB_66_MOL2_input_coordinates_for_PAS.txt (5 kB)</strong></li> <li><strong>directory_tree.AMBER18.GPUTI.scripts.txt (91 kB)</strong></li> <li><strong>directory_tree.AMBER18_input_for_PAS_GPUTI_simulations.txt (1.5 MB)</strong></li> </ul> </li> </ul>
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
<p><strong>Supplementary Data</strong> </p> <ol> <li><strong>Supplementary Data 1</strong> contains the input (non-watertight) surface meshes of the block (shown in Figure 2a) reconstructed within the context of the EPFL-KAUST collaboration, and the corresponding output (watertight) meshes generated by Ultraliser.</li> <li><strong>Supplementary Data 2 </strong>contains a set of 20 non-watertight meshes that were randomly selected from the block shown in <strong>Supplementary Figure S54</strong> and another set of the their watertight counterparts.</li> <li><strong>Supplementary Data 3</strong> contains a set of 25 neuronal morphologies with different morphological types and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 4</strong> contains a set of 25 synthetic astroglial morphologies 15 and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 5</strong> contains the vascular morphology (shown in <strong>Supplementary Fig. S83</strong>) and a corresponding multi-partitioned watertight mesh.</li> <li><strong>Supplementary Data 6</strong> contains the datasets used for the comparative analysis shown in <strong>Supplementary Section 13</strong>.<br> <br> Neuronal, astrocytic and vascular morphologies are stored in SWC, H5 and VMV file formats respectively. The file structures of the SWC and VMV formats are publicly available online. The H5 files of the complete astrocyte cells can be made available from corresponding authors upon request. All the surface meshes are stored in Wavefront OBJ files. Additional STL meshes are generated to be used for TetGen to create corresponding tetrahedral meshes. All the input and generated data files are publicly available on Zenodo (10.5281/zenodo.7105941).</li> </ol> <p><strong>Data Sources</strong> </p> <ol> <li>Cellular and subcellular NGV meshes segmented from the volume shown in Figure 2 are provided by the collaborating co-authors affiliated with KAUST.</li> <li>Neuronal meshes shown in Figure 3, Supplementary Figures S55 - S75 and Supplementary Figures S85 are publicly available from the MICrONS program.</li> <li>Neuronal morphologies shown in Figure 4, Supplementary Figures S80 - S81 and Supplementary Figure S86 are publicly available from NeuroMorpho.Org.</li> <li>Astrocytic morphologies (Figure 5 and Supplementary Figure S82) are provided by Eleftherios Zisis.</li> <li>Vascular morphologies (rat’s cerebral microvasculature) shown in Figure 6 and Supplementary Figures S83 - S84 are courtesy of Bruno Weber, University of Zürich (UZH).</li> <li>The vascular morphology of the arterial arborizations shown in Supplementary Figure S88 is available from the Brain Vasculature (BraVa) database (cng.gmu.edu/brava).</li> </ol>
In-silico PCR results for PSSC primer sets exhibiting >50% amplification rate
<p>using in-silico PCR, 2,161 genomes from<em> </em>the <em>Pseudomonas syringae</em> species complex were amplified using 16 established PCR primer sets, allowing one mismatch per primer. Each file here contains amplicons generated from a single primer set, with each amplicon sequence named with the GenBank accession number for the associated genome.</p>
Ir and NMR for article Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (in vitro) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors
<p>this is IR and NMR data for article titled <strong>Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (<em>in vitro</em>) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors </strong></p> <p> </p>
Figure 3 in Analysis of the toxicological and pharmacokinetic profile of Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside - Tiliroside: in silico, in vitro and ex vivo assay
Figure 3. Photomicrography of exfoliated oral mucosa cells with: (A) karyorrhexis; (B) karyolysis; (C) micronucleus; (D) binucleation; and (E) macronucleus. Magnification X1000.
Figure 2 in Analysis of the toxicological and pharmacokinetic profile of Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside - Tiliroside: in silico, in vitro and ex vivo assay
Figure 2. Cytotoxic effect of tiliroside (H. velutina) against RBC; (C-) Negative control (erythrocytes 0.5%), (C+) Positive control (1% Triton X-100). P <0.05 (*), P <0.01(**) and P <0.001 (***) versus positive control.
Overview of available toxicity data for calystegines - results of the in silico genotoxicity predictions
<p>Results of the<em> in silico</em> genotoxicity predictions complementing the EFSA scientific report on calystegines: https://doi.org/10.2903/j.efsa.2019.5574</p>
Fig. 4 in Response to enantiomers of (Z3Z9)-6,7-epoxy-octadecadiene, sex pheromone component of Ectropis obliqua Prout (Lepidoptera: Geometridae): electroantennagram test, field trapping, and in silico study
Fig. 4. Pan trap catches of male Ectropis obliqua Prout baited with binary blends of racemic and enantiomers of Z3Z9-6,7-epo-18:Hy with triene in XianNing County, China. (A) Z3Z9-6S,7R-epoxy-18:H (6 μg) + Z3Z6Z9-18:H (4 μg); (B) Z3Z9-6R,7S-epoxy-18:H (6 μg) + Z3Z6Z9-18:H (4 μg); (C) blank lure (control). Data are mean ± SD (n = 8) of male E. obliqua trap catches in Xian-Ning County, China, May 2016 by 3 different combinations compared with water pan trap.
Fig. 1 in Response to enantiomers of (Z3Z9)-6,7-epoxy-octadecadiene, sex pheromone component of Ectropis obliqua Prout (Lepidoptera: Geometridae): electroantennagram test, field trapping, and in silico study
Fig. 1. Sex pheromone components of Ectropis obliqua Prout. (A) sex pheromone components; (B) enantiomers of Z3Z9-6,7-epo-18:Hy.
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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.