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Figure 2 from: Voynikov Y, Gevrenova R, Zheleva-Dimitrova D, Balabanova V, Nikolova I, Marinov L, Benbassat I, Momekov G (2023) UHPLC-Orbitrap screening of oleraindoles in hydromethanolic extracts of Portulaca oleracea. Pharmacia 70(4): 1521-1527. https://doi.org/10.3897/pharmacia.70.e113577
Figure 2 MS2 spectra and fragmentation analysis of the three basic HCA-I conjugates in negative ionization mode. The characteristic difference of 149.048 Da, indicating a neutral loss of the 5,6-dihydroxyindole is indicated. The fragment ion corresponding to 5,6-dihydroxyindole is 148.04 m/z.
Figure 3 from: Voynikov Y, Gevrenova R, Zheleva-Dimitrova D, Balabanova V, Nikolova I, Marinov L, Benbassat I, Momekov G (2023) UHPLC-Orbitrap screening of oleraindoles in hydromethanolic extracts of Portulaca oleracea. Pharmacia 70(4): 1521-1527. https://doi.org/10.3897/pharmacia.70.e113577
Figure 3 Proposed fragmentation behavior and diagnostic fragment ions of the basic components of oleraindoles: 5,6-dihydroxyindole, and coumaroyl, caffeoyl, and feruloyl moieties. (A): negative ionization mode; (B): positive ionization mode.
Figure 1 from: Voynikov Y, Gevrenova R, Zheleva-Dimitrova D, Balabanova V, Nikolova I, Marinov L, Benbassat I, Momekov G (2023) UHPLC-Orbitrap screening of oleraindoles in hydromethanolic extracts of Portulaca oleracea. Pharmacia 70(4): 1521-1527. https://doi.org/10.3897/pharmacia.70.e113577
Figure 1 Workflow chart of the study. The hydromethanolic extract of purslane was subjected to UHPLC-HRMS with subsequent MS2 analysis. After the raw data files were transformed with MSconvert, the data filtering (DIF and DDF) were performed either with MS2Analyzer, MZmine and the in-house R script. The scans that fell within 1.5% retention time threshold and 15 ppm m/z treshold were grouped, as belonging to the same substance. Then, the obtained list of possible oleraindole structures were manually inspected with the Xcalibur software.
Supplementary material 2 from: Voynikov Y, Gevrenova R, Zheleva-Dimitrova D, Balabanova V, Nikolova I, Marinov L, Benbassat I, Momekov G (2023) UHPLC-Orbitrap screening of oleraindoles in hydromethanolic extracts of Portulaca oleracea. Pharmacia 70(4): 1521-1527. https://doi.org/10.3897/pharmacia.70.e113577
Scripts
Supplementary material 1 from: Voynikov Y, Gevrenova R, Zheleva-Dimitrova D, Balabanova V, Nikolova I, Marinov L, Benbassat I, Momekov G (2023) UHPLC-Orbitrap screening of oleraindoles in hydromethanolic extracts of Portulaca oleracea. Pharmacia 70(4): 1521-1527. https://doi.org/10.3897/pharmacia.70.e113577
MS/MS spectra of identified compounds
Figure 4 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 4 Number of HBs and RMSD between N1-H274Y-oseltamivir protein (PDB ID: 3CL0) with three flavones 428, 581, and 864 (Fig. A–C, respectively) during dynamic simulation time 500 ns.
Figure 5 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 5 The proposed semi-synthesis of compound 864. (i) Column chromatography (70% EtOH: 18.64 mg.g−1) (ii) ClCH2COCl, K2CO3, DCM, rt, 8–24h; (iii) K2CO3/KI, acetone, reflux, 25–30 h.
Figure 2 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 2 2D interaction diagrams and 3D molecular docking of N1-H274Y-oseltamivir protein (PDB ID: 3CL0) with four flavones 428, 581, 864, and 948 (A–D, respectively) and two flavonols 162 and 218, (E, F, respectively).
Figure 3 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 3 3D molecular docking of bifunctional N inhibitor (compound 864) binding into the sialic acid binding cavity and the 430-cavity through the unique arginine triad Arg118-Arg292-Arg371 of N1-H274Y-oseltamivir protein (PDB ID: 3CL0).
Figure 1 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 1 Procedure of molecular docking screening and molecular dynamics of flavones and flavonols from COCONUT database.
Supplementary material 1 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Supporting data
Figure 5 from: Mateev E, Irfan A, Mateeva A, Kondeva-Burdina M, Georgieva M, Zlatkov A (2024) In silico and in vitro screening of pyrrole-based Hydrazide-Hydrazones as novel acetylcholinesterase inhibitors. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e114120
Figure 5 Major intermolecular interactions between 12d and the active site of AChE (PDB: 1Q84). The interactions are provided in 2D (A) and 3D (B) forms. The AChE enzyme is depicted in grey while the active inhibitor – 12d, is presented as green sticks with its electrostatic potential.
Figure 4 from: Mateev E, Irfan A, Mateeva A, Kondeva-Burdina M, Georgieva M, Zlatkov A (2024) In silico and in vitro screening of pyrrole-based Hydrazide-Hydrazones as novel acetylcholinesterase inhibitors. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e114120
Figure 4 Inhibitory activity of the top ranked ligands against AChE (10 μM concentrations). * P < 0.1; *** P < 0.001 vs control (pure eeAChE). Data are presented as means from three independent experiments ± SD.
Supplementary material 1 from: Mateev E, Irfan A, Mateeva A, Kondeva-Burdina M, Georgieva M, Zlatkov A (2024) In silico and in vitro screening of pyrrole-based Hydrazide-Hydrazones as novel acetylcholinesterase inhibitors. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e114120
Docking scores and MM/GBSA recalculation of the applied dataset
Figure 3 from: Mateev E, Irfan A, Mateeva A, Kondeva-Burdina M, Georgieva M, Zlatkov A (2024) In silico and in vitro screening of pyrrole-based Hydrazide-Hydrazones as novel acetylcholinesterase inhibitors. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e114120
Figure 3 Superimposed native conformation of TZ4 and the re-docking conformations acquired with Glide (A) and GOLD 5.3 (B).
Unveiling CNS Cell Morphology with Deep Learning: A Gateway to Anti-Inflammatory Compound Screening
<p>Understanding the connection between cellular structure and observable traits is vital for comprehending cell behavior, especially in neuropathological conditions. Current limitations exist in applying advanced image analysis, like deep learning, to central nervous system cells due to challenges such as the need for extensive labeled data, difficulty in detecting subtle changes, and the presence of batch effects. The study focuses on overcoming these issues in the context of neuroinflammation, utilizing a deep learning approach to effectively analyze morphological changes in neuronal and microglial cells. This innovative method aids in studying neuroinflammation, facilitating the screening of potential therapeutic compounds and bridging gaps in neuropathological research and pharmaceutical development.</p>
Human Myeloma Cell Line (HMCL) NCATS MIPE 4.0 Drug Screen Dataset
<h3>Abstract</h3> <p>Multiple myeloma, a hematopoietic malignancy of terminally differentiated B cells, is the second most common hematological malignancy after leukemia. While patients have benefited from numerous advances in treatment in recent years resulting in significant increases to average survival time following diagnosis, myeloma remains incurable and relapse is common. To help identify novel therapeutic agents with efficacy against the disease and to search for biomarkers associated with differential response to treatment, a large-scale pharmacological screen was performed with 1,912 small molecule compounds tested at 11 doses for 47 human myeloma cell lines (HMCL). Raw and processed versions of the drug screen dataset are provided, as well as supportive information including drug and cell line metadata and high-level characterization of the most salient features of each. The dataset is publicly available at Zenodo and the workflow code used for data processing and generation of supporting figures and tables can be found at https://github.com/khughitt/hmcl-drug-screen-pipeline.</p> <h3>Overview</h3> <p>The dataset shared here contains the raw and processed versions of a high throughput small molecule screening dataset, as as supporting tables and figures.</p> <p>The computational pipeline used to generate the dataset is available at: <a href="https://github.com/khughitt/hmcl-drug-screen-pipeline">https://github.com/khughitt/hmcl-drug-screen-pipeline</a></p> <h3>Data organization</h3> <p>The .zip file available on Zenodo contains the complete output from the computational pipeline linked to above.</p> <p>Data and figures are organized into subfolders relating to the various pipeline steps.</p> <p>Files & folders:</p> <table> <tbody> <tr> <td><strong>drugs/</strong></td> <td>Drug curves, cell line x drug AC-50 matrix, and drug metadata tables.</td> </tr> <tr> <td><strong>manuscript/</strong></td> <td>Figures and tables generated by the pipeline for the manuscript, including the harmonized cell line metadata table ("table1.tsv")</td> </tr> <tr> <td><strong>mutations/</strong></td> <td> <p>Cell line mutation predictions with identifiers harmonized to match those used in the drug screen (source: <a href="https://www.keatslab.org/data-repository">Keats Lab</a>)</p> </td> </tr> <tr> <td><strong>plates/</strong></td> <td> <p>Drug screen viability measurements at different levels of processing, arranged with one column per plate ("raw.tsv", "raw_filtered.tsv", "normed.tsv", "background_adjusted.tsv"), plate-level metadata ("metadata.tsv"), the compued background plate ("background.tsv"), and plate images used for QA.</p> </td> </tr> <tr> <td><strong>raw/</strong></td> <td>Raw version of the drug screen dataset, including the data for cell lines which were filtered out in the pipeline due to quality purposes</td> </tr> <tr> <td><strong>datapackage.yml</strong></td> <td> <p>Metadata for the provided data resources including sha256 checksums, human-readible descriptions, column types, etc. (see: <a href="datapackage.org">https://datapackage.org/</a>)</p> </td> </tr> </tbody> </table> <p> </p>
mouse on a disk in front of screen
SciDraw upload
Supplementary Information: On the Role of Dielectric Screening in Calculating Excited States of Solvated Azobenzene: A Benchmark Study Comparing Quantum Embedding and Polarizable Continuum Model for Representing the Solvent
<p>Link to Gitlab repo: https://gitlab.com/jezsmartinez/azobencene_ep/-/tree/main</p>
Fourier transform infrared (FTIR) spectroscopy as a screening tool for osteosarcopenia in community-dwelling older women
<p>Dataset generated by MATLAB to be analyzed and identify osteosarcopenic women. </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.