Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Data from : Metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations

This dataset contains representative 1D 1H NMR spectra and data used in the manuscript " Metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations " .  The present study used Nuclear Magnetic Resonance-based metabolic footprinting to characterize the secreted cellular metabolite levels (exometabolomes) of Vero E6 cells in response to SARS-CoV-2 infection and to two candidate drugs (Remdesivir, RDV and Azithromycin, AZI).  Supplementary File 1.zip = Representative 1D 1H NMR profiles of examined VE6 esometabolomes,  Supplementary File 2.xlsx = Average Mean ± Standard Deviations of NMR relative quantified data (integrals, a.u.) from examined VE6 esometabolomes.  Supplementary File 3.csv  = p–values and associated False Discover Rate (FDR) derived from univariate ANOVA with Fischer's LDS post-hoc test  comparisons carried out on  NMR relative quantified data. List of Supplementary Files derived from Metabolite Set Enrichment Analysis (MSEA) :  Supplementary File 4.csv = Tabular Results from MSEA performed on VE6+ VE6- comparison.Supplementary File 5.csv = Tabular Results from MSEA performed on VE6+ RDV vs.  VE6+ comparison.Supplementary File 6.csv = Tabular Results from MSEA performed on VE6+ AZI vs.  VE6+ comparison.Supplementary File 7.csv = Tabular Results from MSEA performed on VE6+ R+A vs.  VE6+ comparison.

opencc-by-4.0Mar 2024View details →

ESI-CorA: SARS-CoV-2-Abwassersurveillance

Das Vorhaben "Emergency Support Instrument - Nachweis von SARS-CoV-2 im Abwasser" (ESI-CorA) lief von November 2021 bis März 2023. Zwanzig Kläranlagenstandorte wurden in Deutschland ausgewählt, die im Februar 2022 gestaffelt mit der Überwachung von SARS-CoV-2 im Abwasser begonnen haben. Das zentrale Ziel in ESI-CorA war die Vorbereitung und Durchführung der bundesweiten Pilotphase zur Überwachung von SARS-CoV-2 und seiner Varianten im Abwasser. Unter anderem wurde das Verfahren zur Normalisierung der Rohdaten, der angewandten PCR-Analytik und der Berechnung der Trenddynamiken untersucht. Weitere Informationen sind im <a href="https://www.ptka.kit.edu/img/Projektblatt_ESI-CorA.pdf">ESI-CorA Projektblatt</a> des Karlsruher Instituts für Technologie (KIT) zu finden.

opencc-by-4.0Dec 2023View details →

Fat oxidation rates and cardiorespiratory responses during exercise in different subject populations with post-acute sequelae of SARS-CoV-2 infection: a comparison with normative percentile values

INTRODUCTION: Post-acute sequelae of SARS-CoV-2 infection (PASC) presents a spectrum of symptoms following acute COVID-19, with exercise intolerance being a prevalent manifestation likely linked to disrupted oxygen metabolism and mitochondrial function. This study aims to assess maximal fat oxidation (MFO) and exercise intensity at MFO (FATmax) in distinct PASC subject groups and compare these findings with normative data. METHODS: Eight male subjects with PASC were involved in this study. The participants were divided in two groups: “endurance-trained” subjects (V̇O2max > 55 ml/min/kg) and “recreationally-active” subjects (V̇O2max < 55 ml/min/kg). Each subject performed a graded exercise test until maximal oxygen consumption (V̇O2max) to measure fat oxidation. Subsequently, MFO was assessed and FATmax calculated as the ratio between V̇O2 at MFO and V̇O2max. RESULTS: The MFO and FATmax of “endurance-trained” subjects were 0.85, 0.89, 0.71 and 0.42, and 68%, 69%, 64% and 53%, respectively. Three out of four subjects showed both MFO and FATmax values placed over the 80th percentile of normative data. The MFO and FATmax of “recreationally-active” subjects were 0.34, 0.27, 0.35 and 0.38, and 47%, 39%, 43% and 41%, respectively. All MFO and FATmax values of those subjects placed below the 20th percentile or between the 20th and 40th percentile. DISCUSSION: Significant differences in MFO and FATmax values between 'endurance-trained' and “recreationally-active” subjects suggest that specific endurance training, rather than simply an active lifestyle, may provide protective effects against alterations in mitochondrial function during exercise in subjects with PASC.

dryad40/100

PI3Kg inhibition circumvents inflammation and mortality in SARS-CoV-2 and other infections

Virulent infectious agents such as SARS-CoV-2 and Methicillin Resistant Staphylococcus Aureus (MRSA) induce tissue damage that recruits neutrophils and monocyte/macrophages that promote T cell exhaustion, fibrosis, vascular leak, epithelial cell depletion, and fatal organ damage. Neutrophils and macrophages recruited to pathogen infected lungs, including SARS-CoV-2 infected lungs, express phosphatidylinositol 3-kinase gamma (PI3Kg), a signaling protein that coordinately controls granulocyte and monocyte trafficking to diseased tissues and immune suppressive, pro-fibrotic transcription in myeloid cells. PI3Kg deletion and inhibition with the clinical PI3Kg inhibitor eganelisib promoted survival in models of infectious diseases, including SARS-CoV-2 and MRSA, by suppressing inflammation, vascular leak, organ damage and cytokine storm. These results demonstrate essential roles for PI3Kg in inflammatory lung disease and support the potential use of PI3Kg inhibitors to suppress inflammation in severe infectious diseases.

opencc-zeroMar 2024View details →

opencc-by-4.0Mar 2024View details →

Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications

This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR

opencc-by-4.0Jul 2024View details →

Dataset for Diamond-coated quartz crystal microbalance sensors: Challenges in high yield production and enhanced detection of ethanol and sars-cov-2 proteins

The data set to paper:  Name:             Diamond-coated quartz crystal microbalance challenges in mass production and enhanced detection of ethanol and sars-cov-2 proteins Authors:        Tibor Izsák1*, Marian Varga1, Michal Kočí2,3, Ondrej Szabó2, Katarína Aubrechtová Dragounová2, Gabriel Vanko2, Miroslav Gál4, Jana Korčeková5, Michaela Hornychová 4, Alexandra Poturnayová5, Alexander Kromka2* Affiliations:        1 Department of Microelectronics and Sensors, Institute of Electrical Engineering, Slovak Academy of Sciences, Dúbravská Cesta 9, Bratislava, 841 04, Slovak Republic             2 Department of Semiconductors, Institute of Physics of the Czech Academy of Sciences, Cukrovarnicka 10/112, Prague 6 162 00, Czech Republic             3 Department of Microelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Prague 6, 166 27, Czech Republic             4 Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovak Republic             5 Center of Biosciences, Institute of Molecular Physiology and Genetics, Slovak Academy of Sciences, Bratislava, Slovak Republic             *corresponding author: tibor.izsak@savba.sk Data manager:         Kristýna Dostálová: dostalovak@fzu.cz Date of collection:    1. 5. 2023 - 31. 7. 2024 Description:        Figure 1: Photos of QCM substrates oriented horizontally or vertically on the substrate holder in the deposition chamber (left) and during the diamond CVD process with ignited plasma (right).             Figure 2: a) 3D model of the measurement setup and b) photograph of the open gas chamber with embedded QCM sample.             Figure 3: Photo of the a) measurement setup and b) disassembled flow cell with V-Dia-QCM. c) Side view photo of the assembled flow cell in the measurement setup.             Figure 4: a) SEM images revealing surface morphology and b) corresponding Raman spectra of Dia-QCM and Dia-Si substrates horizontally or vertically oriented on the substrate holder and corresponding optical photos. There is also the Raman spectrum of the bare QCM (Au-QCM) sample before the diamond deposition.             Figure 5: a) Raman spectra and b) SEM images depicting surface morphology of porous diamond film grown on Si (H-PorDia-Si) and QCM (H-PorDia-QCM) substrate. The inset in Fig. 5a represents the optical photo of diamond-coated QCM. Note: ‘H-’ in sample names means horizontally loaded samples.             Figure 6: The response delta fR of diamond-coated QCM sensors horizontally and vertically oriented, i.e., single-sided and double-sided diamond-coated QCMs, when applying periodic switching (at 3-minute intervals) of ethanol vapour (E) with various concentrations (from 10 ppm to 100 ppm) and synthetic air (Air).             Figure 7: a) First resonant frequency shift (delta fR) of individual QCM sensors and b) mean values of delta fR with corresponding error bars for each QCM sensor group dependent on ethanol concentration.             Figure 8: a) The changes of the resonant frequency, delta fR, after the addition of neutravidin (NA) dissolved in water, biotinylated 1C aptamers (1C APT) dissolved in PBS with MgCl2, and 50 pg/mL S-RBD protein in PBS. The addition of neutravidin, aptamers, proteins, and surface washings by water (H2O) or buffer (PBS) are highlighted by arrows. b) Zoom in on the highlighted area in Fig. 8a.             Figure 9: Decrease of the resonant frequency, fR, at various S-RBD protein concentrations. The comparison of the sensitivity of diamond and gold QCM surfaces on which S-RBD was determined is indicated in the graph legend.

AutoDock and CB-Dock data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands

AutoDock 4.2 and CB-Dock data for (NPA)6Zn3(H2O)2 with Mpro from SARS-CoV-2 from PDB Id: 6LU7.

opencc-by-4.0Apr 2021View details →

NMR data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands

1H, 13C, COSY, HMBC, and HSQC NMR data in fid format for (NPA)6Zn3(H2O)2 (NPA = 2-(phenylamino) benzoate) in DMSO-d6.

SARS-Cov-2 illumina sequencing training course

Dataset with two samples of SARS-Cov-2 sequenced with Illumina using Artic v3 amplicon enrichment protocol.

Predictions of the SARS-CoV-2 B.1.1.529 Variant Spike Protein Receptor Binding Domain Structure and Neutralizing Antibody Interactions

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.

Pre-vaccination and early B cell signatures of the antibody response to SARS-CoV-2 mRNA vaccine

The data presented in Code repository for Kardava, L., Rachmaninoff, N., Lau, W. W., Buckner, C. M., Trihemasava, K., Blazkova, J., ... & Moir, S. (2022). Early human B cell signatures of the primary antibody response to mRNA vaccination. Proceedings of the National Academy of Sciences, 119(28), e2204607119.<a href="https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119">https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119</a> are made available here. All code to reproduce the figures can be found here: https://github.com/niaid/COVID_Vaccine_Bcells <a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/all_subjects_cd19_positive_and_keys.zip">all_subjects_cd19_positive_and_keys.zip</a> contains a CSV file of all CD19+ cells with flowSOM clusters shown. Accompanying files allow for matching of timepoint and subject information. <a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/All_subjects_FCS_files_deidentified.zip">All_subjects_FCS_files_deidentified.zip</a> contains the raw fcs files and is organized by timepoint and subject.

opencc-by-4.0Dec 2021View details →

Uncovering cryptic pockets in the SARS-CoV-2 spike glycoprotein

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.

opencc-by-4.0Jan 2022View details →

Germinal centre-driven maturation of B cell response to SARS-CoV-2 mRNA vaccination

These are the processed BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., Nature, 2022</a>. The raw sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA777934">PRJNA777934</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>) and <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>).   Code Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2022">found on GitHub</a>.   Metadata File: WU368_kim_et_al_nature_2022_meta.tsv Notes: <ul> <li>Sample breakdown by `sequence_type` (132 total) <ul> <li>73 bulk BCR sequencing samples (`bulk`) <ul> <li>57 new</li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a></li> <li>11 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a>.</li> </ul> </li> <li>56 10x Genomics single-cell VDJ + 5' gene expression samples (`tgx`)</li> <li>3 samples from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (`mab`) corresponding to a total of 37 S-binding mAbs previously reported. These are not the same as the 2099 recombinant mAbs generated in this study (see below).</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Timepoints were referenced in weeks in the manuscript, as shown in the `timepoint_ms` column.</li> <li>`bio_rep` and `tech_rep` = biological replicate and technical replicate respectively.</li> </ul> Abbreviations: <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul>   Information on the 2099 recombinant mAbs generated in this study File: WU368_kim_et_al_nature_2022_mabs.tsv Notes on columns: <ul> <li>`h_sequence_id` and `l_sequence_id`: Sequence IDs of the heavy and light chains respectively.</li> <li>`elisa`: ELISA results for binding to SARS-CoV-2 S (`TRUE` = positive).</li> </ul>   Processed BCR data - heavy chains File: WU368_kim_et_al_nature_2022_bcr_heavy.tsv Analysis was based on heavy chain-based clonal inference. Notes on columns: The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login">iReceptor</a> use these. Non-standard columns are noted below. <ul> </ul> <ul> <li>`cell_id`: Only sequences from single-cell samples and the 37 mAbs from Turner & O'Halloran et al., Nature, 2021 have cell IDs following the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <li>`v_call_genotyped`: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a>.</li> <li>`germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequence were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</li> <li>`isotype`: IGH[ADEGM].</li> <li>`cdr3`: CDR3 nucleotide sequence.</li> <li>`cdr3_length`: CDR3 nucleotide sequence length.</li> <li>`cdr3_aa`: CDR3 amino acid sequence.</li> <li>`collapse_count`: Number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a>.</li> <li>`donor`, `timepoint`, `tissue`, `sorting`, `seq_type`: Propagated as is from the metadata file. <ul> <li>In `seq_type`, `tgx` corresponds to 10x Genomics data; `mab` corresponds specifically to the 37 S-binding mAbs from Turner & O'Halloran et al., Nature, 2021.</li> </ul> </li> <li>`timepoint_2`: Same as `timepoint`, except that `d28+d35` and `d201+d208` were treated as `d28` (week 4) and `d201` (week 29) respectively as described in Materials & Methods.</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.18` from WU368_kim_et_al_nature_2022_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment. <ul> <li>ABC = activated B cell. LNPC = lymph node plasma cell. RMB = resting memory B cell.</li> <li>Minor differences in terminology <ul> <li>The manuscript refers to the memory compartment as MBCs, whereas the terminology used in the data is RMB. As described in Materials & Methods, analysis involving the memory compartment used specifically d201 bulk-sequenced memory sorts from blood. To get these sequences, subset `s_pos_clone`, `seq_type`, `compartment`, and `timepoint_2` to, respectively, `TRUE`, `bulk`, `RMB`, and `d201`. </li> <li>The manuscript uses the term BMPC (bone marrow plasma cell), whereas the data uses the term LLPC.</li> </ul> </li> </ul> </li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA or one of the S-binding mAbs from Turner & O'Halloran et al., Nature, 2021.</li> <li>`expressed_id`: mAb IDs for the 2099 recombinant mAbs generated in this study (mapped from `mab_id` from WU368_kim_et_al_nature_2022_mabs.tsv) and the 37 mAbs from Turner & O'Halloran et al., Nature, 2021. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive. `NA` if not tested.</li> <li>`nuc_RS_19_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a>.</li> <li>`nuc_denom_19_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".").</li> <li>`nuc_RS_freq_19_312`: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312).</li> </ul>   Processed BCR data - light chains File: WU368_kim_et_al_nature_2022_bcr_light.tsv Light chains were not used for heavy chain-based clonal inference or analysis.   Processed transcriptomics data Files: <ul> <li>WU368_kim_et_al_nature_2022_gex_all_cells.h5ad (clustering all cells)</li> <li>WU368_kim_et_al_nature_2022_gex_b_cells.h5ad (re-clustering only the B cells)</li> </ul> Notes: <ul> <li>The `h5ad` files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195673">GEO under BioProject PRJNA777934</a>.

opencc-by-4.0Mar 2022View details →

(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>

opencc-zeroMar 2022View details →

Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR

The datas correspond to article entitled: "Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR".  The file entitle GISAID have the fasta formats for 107259 genomes from the SARS-CoV-2 GISAID database from January to December 2020. Three documents in plane tex correspond: sequences.fasta contain the original data. sequences_clean.fasta. Corresponds to genomes sequences without nucleotides undeterminateds indicates with "N" in previous document. alignment_clean.fasta. Contain the genomes sequences cleaned alingment.   The file entitle GenBank  have the data set from 19317  genomes from the SARS-CoV-2 GenBank database  from January to October 2020 and the documets have the prrevious order.

Full Datasets for the "Molecular Mimicry Map of SARS-CoV-2" Web Application

A Full Dataset for the "Molecular Mimicry Map of SARS-CoV-2" Web Application This TAR archive file contains the minimally filtered list of potentially cross-reactive epitopes of SARS-CoV-2 and human proteins predicted by the CRESSP analysis pipeline (<a href="https://pypi.org/project/cressp/">https://pypi.org/project/cressp/</a>). Our web application, "Molecular Mimicry Map of SARS-CoV-2" (<a href="https://ahs2202.github.io/3M/">https://ahs2202.github.io/3M/</a>), only visualizes the high-scoring potential cross-reactive epitope pairs (40,000 epitope pairs maximum). However, for the researchers who want to access the complete list of potentially cross-reactive epitope pairs for further analysis, these full datasets can be used.

Extended Data Fig. 7 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells

Extended Data Fig. 7 | Analysis of the inter-subunitshydrogen bondsat the interfaceof RBDandhACE2. Frequencyofformationofhydrogenbondsat theinterface of RBDandhACE2 intheknob (A), base (B), andtipregions (C). Theanalysisisperformedfor 9 different MDsimulations:3 replicatesof the SARS-CoV-2 (shadesof green),BANAL-236 (shadesof red), and BANAL-52/103 (shadesofblue) RBD–hACE2 complexes.

Extended Data Fig. 3 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells

Extended Data Fig. 3 | Nucleotideandamino-acidalignmentsof thefurin cleavagesiteregion. Completenucleotideandamino-acidspikesequencesof representativebat SARS-CoV-2-likecoronavirusesweredownloadedfrom GenBankand GISAIDandaligned with MAFFT (G-INS-I parameter) (A & C). Alignmentsweremanuallyeditedas proposedby Zhou2 andLytras47 with CLC Main Workbench (Qiagen) (B & D). Alignmentsof thefurincleavageregionare presentedatthenucleotide (A & B) andtheamino-acid (C & D) level, respectively.

Extended Data Fig. 1 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells

Extended Data Fig. 1 | Spike identity matricesatthe genus level of representativesarbecoviruses. Amino-acid (lower) andnucleotide (upper) identitymatricesof Laotianandrepresentativehuman,bat, andpangolin sarbecoviruses.Spike N-terminal (NTD), Receptor-binding (RBD) and S2 nucleotideandamino-acidsequenceswerealigned with MAFFT, andidentity matriceswereconstructedusing CLCMain Workbench 21.0.4 (Qiagen). Matriceswerecoloredaccordingtotheidentityscale,from 25% (red) to 100% (green) of nucleotideoramino-acididentity.

Extended Data Fig. 2 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells

Extended Data Fig. 2 | Alignmentofthespike RBDdomain. Protein alignmentof the Receptor Binding Domain (RBD) of Laotianandrepresentative human, batandpangolinsarbecoviruses.Sequenceswerealignedwith MAFFT in G-iNS-Imode. Residuesinteractingwithhuman ACE2 receptorare highlighted ingrey.Thedomainusedforinteractionsmodeling,basedonthe X-raystructure 6M0J (residues T333 to G526), ishighlightedbyablackline.