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468 results for “Spikes”
Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies
<p>Participants submitted saliva using the OME-505 collection device (OMNIgene Oral, Ottawa, Canada) every two weeks from vaccination through six months post-dose 3 to detect breakthrough SARS-CoV-2 infections. Viral RNA was extracted using the NucliSENS easyMag automated extraction system from 200ul of saliva in stabilizing solution and eluted in a total volume of 50ul. First strand cDNA synthesis was performed from 5ul of eluted RNA using SuperScript IV VILO Master Mix (Thermo Fisher). Positive specimens were then sequenced. Multiplex tiled amplicon libraries were prepared using the Midnight panel and Rapid barcoding kit RBK-004 (Oxford Nanopore technologies) using previously published protocol. Twelve sample pooled libraries were sequenced on a GridION X5 nanopore sequencer using Flongle adapters. After sequencing, raw data were processed using interARTIC to generate consensus sequences and variant calls. SARS-CoV-2<strong> </strong>lineages were determined using these consensus sequences and the NextClade and Pangolin platforms.</p>
Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms (supplemental material: brain wave loops movies)
<p>This is a collection of videos supplementing the paper "Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms"</p> <p>Examples of wave trajectories and emergent persistent loop patterns for the spherical shell cortex model with<br> varying amounts of tensor anisotropy and inhomogeneous shell layer thickness.</p> <p><br> Examples of brain wave trajectories and emergent persistent loop patterns for cortical fold geometry with different<br> approaches used for estimation of inhomogeneity and anisotropy. Among those examples are several simple cases with variable inhomogeneity and fixed anisotropy (similar to the above spherical shell cortex model) as well as with more complex estimates of anisotropy based on multiple diffusion gradients MRI (dMRI) acquisitions.</p>
All atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme
<p><strong>The dataset contains a total of 40 snapshots of the four trajectories (10 snapshots each system = two per replica x 5 replicas/system):</strong></p> <ol> <li>SARS-CoV-2002 spike protein without ACE2</li> <li>SARS-CoV-2 spike protein without ACE2</li> <li>SARS-CoV-2002 spike protein with ACE2</li> <li>SARS-CoV-2 spike protein with ACE2</li> </ol> <p>Molecular dynamics simulation trajectories (320ns each) have been performed using the Amber ff14SB force field running with the Amber18 package at the the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Under the following simulation methodology:</p> <p><em>All-atom simulations were carried out with Amber18 (<a href="https://slack-redir.net/link?url=http%3A%2F%2Fambermd.org">ambermd.org</a>), and system components (protein, ions, water) were modeled with the included FF14SB and TIP3P parameter sets. Energy minimization used CPU pmemd, while later simulation stages used GPU pmemd. CoV2 and CoV1 systems with one RBD up (with/without ACE2) were solvated in 12 angstrom water shells. Cysteine residues identified in the initial models as having a disulfide bond (DB) were bonded using tLeap. All simulations used 0.150 M NaCl. Hydrogen mass repartitioning was applied only to the protein to enable a 4 fs timestep (<a href="https://slack-redir.net/link?url=https%3A%2F%2Fpubs.acs.org%2Fdoi%2Fabs%2F10.1021%2Fct5010406">https://pubs.acs.org/doi/abs/10.1021/ct5010406</a>). The SHAKE algorithm was applied to hydrogens, and a real-space cutoff of 8 angstroms was used. Periodic boundary conditions were applied and PME was used for long-range electrostatics. Minimization was by steepest descent (2000 steps) followed by conjugate gradient (3000 steps). Heating used two stages: (1) NVT heating from 0 K to 100 K (50 ps), and (2) NPT heating from 100 K to 300 K (100 ps). Restraints of 10 kcal mol<sup>-1</sup> angstrom<sup>-2</sup> were applied during minimization and heating to C-alpha atoms. During 6 ns of equilibration at 300 K C-alpha restraints were gradually reduced from 10 kcal mol<sup>-1</sup> angstrom<sup>-2</sup> to 0.1 kcal mol<sup>-1</sup> angstrom<sup>-2</sup>. Finally, restraints were released and 320 ns unrestrained production simulations were carried out for CoV2 and CoV1 systems. Production simulations began from the final equilibrated snapshots, and five copies of each system were simulated. As unrestrained systems can freely rotate we monitored simulations for any close contacts and found that in one copy of the CoV1 simulation without ACE2 and one RBD up that a few contacts close to 8 angstrom occur near the end of the 320 ns between the RBD and a different subdomain of the spike complex in a periodic image. However this did not influence analyzed structural properties which is verified by comparing results across simulations. The Monte Carlo barostat was used to maintain pressure (1 atm), and the Langevin thermostat was used to maintain 300 K temperature (collision frequency 1 ps<sup>-1</sup>), as implemented in Amber18. In aggregate, nearly 7 microseconds of simulation of systems ranging from 396,147 to 879,100 atoms was carried out for this work.</em><br> For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu).</p> <p><strong>Regarding the contact map analysis scripts (contactMaps_Analysis.tar.gz), they contain the following workflow:</strong></p> <p>contactmap --> source files from contact_map executable<br> process_nc.sh --> convert raw data from all-atom simulation to numbered PDB files and get the contact maps<br> frequency.lua --> read a set of PDB files and output the frequency count for each contact<br> consensus.fasta --> align sequence of Covid19 and SARS from Chimera<br> consensus.lua --> read data previously generated and compute the frequency per residue, among other things.<br> consensus.sh --> input information to consensus.lua<br> consensus.gp --> gnuplot script to plot figures</p> <p>This dataset and the code is part of tripartite collaboration between:</p> <ul> <li>The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)</li> <li>Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).</li> <li>Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).</li> </ul>
Simple structural views of the SARS spike glycoprotein complex with human angiotensin-converting enzyme 2 (ACE2)
<p>A set of 5 screenshots of UnityMol running the first example system. Three screenshots are from a multi-user virtual reality session with 3 participants, two screenshots illustrate a custom menu to drive the example more efficiently and make it simple for the end user.</p>
Dataset: The SPIKE II experiment - Tracing the water balance
<p>This repository holds data collected during the “SPIKE II” tracer experiment. The experiment was carried out on a large vegetated lysimeter (2.5 m<sup>3</sup>) planted with two willow trees (clones) (<em>Salix viminalis</em>) within the EPFL campus (CH), in Switzerland. SPIKE II took place from May 10 to June 29 in 2018. This composite dataset contain stable isotopic composition (δ<sup>2</sup>H and δ<sup>18</sup>O) of more than 900 water samples of precipitation, soil water, bulk soil collected at different depths in the soil profile, xylem from willow, and leakage flow in the bottom of the lysimeter. The dataset comprises environmental conditions and water fluxes recorded during the experiment. This includes: meteorological conditions, soil moisture and tension, evapotranspiration in the lysimeters, and tree transpiration recorded at high resolution. Finally, the repository holds tree hydraulic and growth measurements and root traits.</p> <p>Specifically, this dataset contains six files:</p> <ul> <li>“METADATA_spikeII.txt” contains specific information about each recorded variable and data point collected throughout the experiment.</li> <li>“spike.hydrometric.II.csv” contains information about meteorological and soil conditions, evapotranspiration fluxes, and tree stem radius, including growth and tree water deficit.</li> <li>"spike.isotopes.II.csv” contains stable isotope data.</li> <li>“fineroots_spike.II.csv” contains root traits information.</li> <li>“events_chronology.csv” summarizes the main events that occurred during SPIKE II.</li> <li>“Figure1_SpikeII_Aerial_Image.PNG” illustrates the location and spatial display of the experiment at the EPFL campus.</li> </ul> <p>This data repository was used in the following SPIKE II publications:</p> <p>Nehemy, M. F., Benettin, P., Asadollahi, M., Pratt, D., Rinaldo, A., & McDonnell, J. J. (2021). Tree water deficit and dynamic source water partitioning. <em>Hydrological Processes</em>, <em>35</em>(1), e14004. doi:10.1002/hyp.14004</p> <p>Benettin, P., Nehemy, M. F., Cernusak, L. A., Kahmen, A., & McDonnell, J. J. (2021). On the use of leaf water to determine plant water source: A proof of concept. <em>Hydrological Processes</em>, <em>35</em>(3), e14073. doi:10.1002/hyp.14073</p> <p>Benettin, P., Nehemy, M. F., Asadollahi, M., Pratt, D., Bensimon, M., McDonnell, J. J., & Rinaldo, A. (2021). Tracing and closing the water balance in a vegetated lysimeter. <em>Water Resources Research</em>, 57, e2020WR029049. doi:org/10.1029/2020WR029049</p> <p>For any further inquiry, please contact Magali Nehemy or Paolo Benettin.</p> <p>We thank Kim Janzen for assistance with laser and mass spec analysis. We thank the Laboratory of Ecohydrology at EPFL (ECHO/IIE/ENAC/EPFL) for assistance throughout the experiment. We also thank Pierre Queloz and Scott Allen for precious help, Gabriel Cotte and Torsten Vennemann from University of Lausanne (CH) for the collection and analysis of atmospheric vapor samples. This research was supported by the American Geophysical – Horton Research Grant 2019 awarded to MFN, an NSERC CREATE in Water Security and an NSERC Discovery Grant to JJM, AR and PB thank ENAC school at EPFL for financial support and acknowledge the Swiss National Science Foundation grant number CRSII5\_186422.</p> <p> </p>
Training dataset: Generation of a spectral library from HEK-Ecoli Spike-in mass spectrometry data
<p>The five raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in the following ratios (amount in µg):</p> <p>Sample HEK E.coli MS method<br> Sample1 2.5 0.00 DDA<br> Sample2 2.5 0.05 DDA<br> Sample3 2.5 0.15 DDA<br> Sample4 2.5 0.40 DDA<br> Sample5 2.5 0.80 DDA</p> <p>Additionally, iRT peptides were added and 1µg of each samples was measured with a Q-Exactive Plus mass spectrometer. Besides the five raw files, we uploaded two fasta files that serve as human and ecoli protein sequence databases, an transition list for the iRT peptides as well as an experimental design for the MaxQuant search.<br> Additionally, we uploaded the Galaxy MaxQuant training result files: protein groups, peptides, mqpar, msms, evidence and PTXQC.</p>
Training dataset: DIA data analysis of a HEK/Ecoli Spike-in dataset using OpenSwathWorkflow
<p>The eight raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in two different ratios and four replicates of each Spike/in ratio were measured:</p> <p>Sample HEK E.coli MS method<br> Sample1 2.5 0.15 DIA<br> Sample2 2.5 0.15 DIA<br> Sample3 2.5 0.15 DIA<br> Sample4 2.5 0.15 DIA<br> Sample5 2.5 0.80 DIA<br> Sample6 2.5 0.80 DIA<br> Sample7 2.5 0.80 DIA<br> Sample8 2.5 0.80 DIA</p> <p>Additionally, iRT peptides were added and 1µg of each samples was measured using data independent acquisition with a Q-Exactive Plus mass spectrometer. Briefly, a scan range from 400-1000 m/Z was first covered by an MS1 scan followed by 25 consecutive MS2 scans (each 24 m/z broad). In the next cycle another MS1 scan was acquired followd by 26 MS2 scans (also 24m/z broad) in which the window centers were shifted by 50% compared to the previous cycle of MS2 scans. The resulting raw files contain overlapping MS2 scans.</p> <p>Besides the eight raw files, we uploaded a spectral library, a transition list for the iRT peptides as well as an sample annotation file.<br> Additionally, we uploaded the Galaxy PyProphet score training result files: PyProphet score report and PyProphet score.</p>
Training dataset: Statistical analysis of a HEK/Ecoli Spike-in DIA dataset using MSstats
<p>The uploaded files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in two different ratios and four replicates of each Spike/in ratio were measured and analysed using OpenSwathWorkflow in Galaxy. Results were exported using PyProphet and can be used for the statistical analysis and detection of the two different Spike-in Ratios. The Spike-in ratios were the following:</p> <p>Sample HEK E.coli <br> Spike_in_1 2.5 0.15<br> Spike_in_2 2.5 0.80 </p> <p>Besides the two PyProphet export files, we uploaded a sample annotation file as well as a comparison matrix file.<br> Additionally, we uploaded the Galaxy MSstats training result files: MSstats_ComparisonResult_export_tabular and MSstats_ComparisonResult_msstats_input.</p>
Data for the article: "Molecular Modelling Reveals Eight Novel Druggable Binding Sites in SARS-CoV-2's Spike Protein" by Ilke Ugur and Antoine Marion
<p>This upload contains data related to the article<br> published as a preprint on ChemRxiv with DOI<br> https://doi.org/10.26434/chemrxiv.13292768</p> <p>"Molecular Modelling Reveals Eight Novel Druggable Binding Sites in SARS-CoV-2's Spike Protein"<br> by Ilke Ugur and Antoine Marion (2020)<br> Department of Chemistry, Middle East Technical University, Ankara, Turkey.</p> <p>For further information, please contact:<br> ilkeugur@metu.edu.tr ; amarion@metu.edu.tr</p> <p>The manuscript is currently under peer-review.</p> <p>Content:</p> <p>Library of molecules derived from DrugBank v 5.1.5:<br> - DrugBank_2020_5.1.5/ # All necessary files for the docking and refinement of the library of molecules.<br> -- DB_5.1.5_pH7.4_pdbqt/ ## PDBQT readily usable for docking with AutoDock Vina.<br> -- DB_5.1.5_pH7.4_mol2amber/ ## mol2 files containing assigned GAFF atom types and Gasteiger atomic charges.<br> -- DB_5.1.5_pH7.4_frcmod/ ## frcmod files containing missing molecular mechanics parameters<br> -- dbID_name.dat ## DrugBank ID to generic name dictionary</p> <p>Note: The files were prepared automatically via a series of operations handling openbabel and antechamber.<br> The protonation state of ionizable groups as well as Gasteiger atomic charges were assigned by openbabel for a pH of 7.4<br> mol2 and frcmod files can be used readily via the tleap module of AmberTools to produce topology files.</p> <p><br> Receptor structures:<br> - receptors/ # PDB files for the four structures of the spike protein considered in this work<br> -- CS00ns.pdb ## Closed state after the remodelling of missing loops (PDB ID 6vxx)<br> -- OS00ns.pdb ## Open state after the remodelling of missing loops (PDB ID 6vyb)<br> -- CS25ns.pdb ## Closed state after 25 ns of molecular dynamics in explicit water<br> -- OS25ns.pdb ## Open state after 25 ns of molecular dynamics in explicit water</p> <p>Note: All structures are aligned to CS00ns.pdb and can be converted to pdbqt for docking with AutoDock Vina</p> <p><br> Docking grid centers:<br> - dockingCenters/ # XYZ files containing the coordinates of each docking grid center considered in this work</p> <p>Note: The coordinates are given in the same frame as that of the four structures of the receptor.</p> <p><br> Binding sites:<br> - bindingSites/ # XYZ files with the coordinates of the representative atomic centres<br> # of each binding site identified in this work (A-H).</p> <p>Note: These files can be used to get a clearer picture of the binding sites within the structures<br> of the spike protein shared in the receptors directory.</p> <p><br> Final modelling results:<br> - allData.txt # data for all molecules in the set (approved and investigational)<br> - appData.txt # data for approved molecules only<br> - data.xlsx # data for all molecules in the set (approved and investigational)<br> # as a formatted excel spreadsheet</p> <p>Note: The columns are delimited with semi-colons ";".<br> The files contain the results for the best pose of all approved molecules for which<br> molecular mechanics-based geometry optimization succeeded, regardless of their score.<br> For other molecules, the result of their best pose is reported only for those complexes<br> having MM interaction energy lower or equal to -22.00 kcal/mol.</p> <p><br> Visualization:<br> - bs.pse # pymol session representing the binding sites within the<br> # closed state structure of the spike protein (CS00ns)<br> - pt.pse # pymol session representing the docking grid centres within<br> # closed statestructure of the spike protein (CS00ns)</p> <p>Note: the PSE files should be compatible with version 7.0 of pymol and later</p>
All-atom Molecular Dynamics Simulations of SARS-CoV-2 Spike Receptor-binding Domain bound with ACE2
<p>Data includes all of the trajectories (1000) of classical all-atom molecular dynamics (MD) simulations of of SARS-CoV2 Spike Protein/ACE2 complex (PDB ID: 6M0J). In order to decrease the size of the file only protein rajectories were provided. Simulation has been performed with Desmond. Protein was placed in the cubic boxes with explicit TIP3P water models that have 10.0 Å thickness from surfaces of protein. The system is neutralized by adding counter ions, and salt solution of 0.15M NaCl was also used to adjust the concentration of the systems. The long-range electrostatic interactions were calculated by the particle mesh Ewald method. A cutoff radius of 9.0 Å was used for both van der Waals and Coulombic interactions. The temperature was set as 310K initially, and Nose–Hoover thermostat was used for adjustment. Martyna–Tobias–Klein protocol was employed to control the pressure, which was set at 1.01325 bar. The time-step was assigned as 2.0 fs. The default values were used for minimization and equilibration steps, and finally 100 ns production run was performed for the simulation.</p>
mletools: simulated spiking activity
<p>Supporting data for the article</p> <p>Zaytsev YV, Morrison A, Deger M: Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity. Journal of Computational Neuroscience (2015)</p> <p>DOI: 10.1007/s10827-015-0565-5</p> <p>The files in this upload contain spike data and ground truth connectivity matrices for the experiments presented in the article. We hereby place this data into the public domain.</p>
Spike-induced cytoarchitectonic changes in epileptic human cortex are reduced via MAP2K inhibition
<p>Interictal spikes are electroencephalographic discharges that occur at or near brain regions that produce epileptic seizures. While their role in generating seizures is not well understood, spikes have profound effects on cognition and behavior, depending on where and when they occur. We previously demonstrated that spiking areas of the human neocortex show sustained MAPK activation in superficial cortical layers I-III and are associated with microlesions in deeper cortical areas characterized by reduced neuronal nuclear protein (NeuN) staining and increased microglial infiltration. Based on these findings, we chose to investigate additional neuronal populations within microlesions, specifically inhibitory interneurons. Additionally, we hypothesized that spiking would be sufficient to induce similar cytoarchitectonic changes within the rat cortex and that inhibition of MAPK signaling, using a MAP2K inhibitor, would not only inhibit spike formation but also reduce these cytoarchitectonic changes and improve behavioral outcomes. To test these hypotheses, we analyzed tissue samples from 16 patients with intractable epilepsy who required cortical resections. We also utilized a tetanus toxin-induced animal model of interictal spiking, designed to produce spikes without seizures in male Sprague-Dawley rats. Rats were fitted with epidural electrodes, to permit EEG recording for the duration of the study, and automated algorithms were implemented to quantify spikes. After 6 months, animals were sacrificed to assess the effects of chronic spiking on cortical cytoarchitecture. Here, we show that microlesions may promote excitability due to a significant reduction of inhibitory neurons that could be responsible for promoting interictal spikes in superficial layers. Similarly, we found that the induction of epileptic spikes in the rat model produced analogous changes, including reduced NeuN, calbindin, and parvalbumin-positive neurons and increased microglia, suggesting that spikes are sufficient for inducing these cytoarchitectonic changes in humans. Finally, we implicated MAPK signaling as a driving force producing these pathological changes. Using CI-1040 to inhibit MAP2K, both acutely and after spikes developed, resulted in fewer interictal spikes, reduced microglial activation, and less inhibitory neuron loss. Treated animals had significantly fewer high-amplitude, short-duration spikes, which correlated with improved spatial memory performance on the Barnes maze. Together, our results provide evidence for a cytoarchitectonic pathogenesis underlying the epileptic cortex, which can be ameliorated through both early and delayed MAP2K inhibition. These findings highlight the potential role of CI-1040 as a pharmacological treatment that could prevent the development of epileptic activity and reduce cognitive impairment in both patients with epilepsy and those with non-epileptic spike-associated neurobehavioral disorders.</p>
Predictions of the SARS-CoV-2 B.1.1.529 Variant Spike Protein Receptor Binding Domain Structure and Neutralizing Antibody Interactions
<p>Using AlphaFold2 and HADDOCK, we have generated a predicted structure for the SARS-CoV-2 B.1.1.529 variant's Spike receptor binding domain and then predicted the binding interaction with neutralizing antibodies. This was performed to understand the potential structural changes in the receptor binding domain of B.1.1.529 and how this may affect vaccine efficacy through antibody interaction.</p>
Uncovering cryptic pockets in the SARS-CoV-2 spike glycoprotein
<p>The COVID-19 pandemic has prompted a rapid response in vaccine and drug development targeting SARS-CoV-2. Herein, we modelled a complete membrane-embedded SARS-CoV-2 spike (S) protein and used molecular dynamics (MD) simulations in the presence of benzene probes designed to enhance discovery of cryptic, potentially druggable pockets. This approach recapitulated lipid binding sites previously characterized by cryo-electron microscopy, and uncovered a novel cryptic pocket with promising druggable properties located underneath the 617-628 loop, which was shown to be involved in modulating the stability of cleaved S protein trimers a well as the formation of S protein multimers on the viral surface. A multi-conformational behaviour of this loop in simulations was validated using hydrogen-deuterium exchange mass spectrometry (HDX-MS) experiments, supportive of opening and closing dynamics. The pocket is the site of multiple mutations associated with increased transmissibility and severity of infection found in SARS-CoV-2 variants of concern including D614G. Collectively, this work highlights the utility of the benzene mapping approach in uncovering potential druggable sites on the surface of SARS-CoV-2 targets.</p> <p> </p>
Cortical oscillations support sampling-based computations in spiking neural networks
<p>This archive contains the scripts for generating the data and figures and the data of the publication: "Cortical oscillations support sampling-based computations in spiking neural networks".</p> <p>The different parts of the material are grouped into separate archives to enable modular usage and can be individually downloaded as required:</p> <ul> <li>The archive spike-based-tempering_scripts.tgz contains all the scripts to reproduce the simulation data and figures.</li> <li>The file software.img contains the third-party software needed to execute the simulations of the current-based experiments.</li> <li>The archive current-based_experiments_data includes the scripts and the simulation data for the current-based experiments.</li> </ul>
(VIDEOS) Glycosylation as a key for enhancing drug recognition into spike glycoprotein of SARS-CoV-2.
<ul> <li>S1_movie_1. Movie of MD1 trajectory showing the Interaction of ligand TCMDC-124223 (roto-translate phenomenon) on RBD in absence of glycosylations within 50ns of simulation.</li> <li>S2_movie_2. Movie of MD4 trajectory showing the Interaction of ligand TCMDC-133766 (induced fit phenomenon) on the cryptic pocket of NTD in presence of glycosylations within 50ns of simulation.</li> <li>S3_movie_3. Movie of MD2 trajectory showing the Interaction of ligand TCMDC-124223 on RBD in presence of glycosylations within 50ns of simulation.</li> <li>S4_movie_4. Movie of MD3 trajectory showing the Interaction of ligand TCMDC-133766 on the cryptic pocket of NTD in absence of glycosylations within 50ns of simulation.</li> <li>S1_movie_1_v2. Movie of MD1 trajectory showing the Interaction of ligand TCMDC-124223 (roto-translate phenomenon) on RBD in absence of glycosylations within 300ns of simulation.</li> <li>S2_movie_2_v2. Movie of MD4 trajectory showing the Interaction of ligand TCMDC-133766 (induced fit phenomenon) on the cryptic pocket of NTD in presence of glycosylations within 300ns of simulation.</li> <li>S3_movie_3_v2. Movie of MD2 trajectory showing the Interaction of ligand TCMDC-124223 on RBD in presence of glycosylations within 300ns of simulation.</li> <li>S4_movie_4_v2. Movie of MD3 trajectory showing the Interaction of ligand TCMDC-133766 on the cryptic pocket of NTD in absence of glycosylations within 300ns of simulation.</li> </ul>
Simulation of Receptor Binding Domain of SARS-CoV-2 spike protein (WT and variants) in complex with neutralizing antibodies.
<p>This repository contains the molecular dynamics trajectories of the SARS-CoV-2 Spike RBD bound to BD23 and B38 monoclonal antibodies. The simulations for the RBD only systems are also provided. The trajectories are available for the WT spike protein as well as for four different variants (alpha, beta, kappa and delta). The simulations of the RBD only system are propagated for 300 ns and for the RBD-Antibody complex for 500 ns. The trajectories are saved at 100 ps interval. The Steered MD simulation trajectories (WT_RBD_B38_SMD_1.dcd etc.) and collective variables files are also included (WT_RBD_B38_SMD_1.colvars.traj etc.). There are 5 SMD trajectories for each RBD antibody pair. The details of the simulation can be obtained from the preprint: https://doi.org/10.1101/2021.08.13.456317</p>
Coronavirus Spike Protein Sequences from NCBI Virus
<p>For the study of taxonomic classification of coronaviruses across all genera (Alpha-, Beta-, Gamma-, and Deltacoronavirus), we can use spike protein sequences downloaded from the NCBI Virus website, \url{https://www.ncbi.nlm.nih.gov/labs/virus/vssi/}. The spike protein sequences used in this study were downloaded on November 21 and 27, 2021 using search terms such as ``spike'', ``S1 protein'', ``S2 protein'', and ``S protein'', in order to download as many spike protein sequences as possible.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 0.25% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 0.25% contaminant spike-in.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 1% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 1% contaminant spike-in.</p>
ScienceDex guides
Understand access before you commit
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.