Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
29,897
datasets available to search
ShareScore release 0.9.0
Dataset results
29,897 results for “activism”
ATAC-seq dataset: Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors
<p>The dataset encompasses raw sequencing reads, identified peaks, and regions of differential accessibility derived from ATAC-seq experiments conducted under various immune activation conditions. For additional technical details regarding data collection, please refer to the published source at https://doi.org/10.1093/jxb/erab373.</p>
Fig. 4 in Daily and seasonal activity patterns of a felid assemblage in a forest-grassland mosaic in southern Brazil
Fig. 4. Overlap of daily activities between the pairs of the four felid species found in the Papagaios-de-Altitude Private Protected Area in Urupema, Santa Catarina, southern Brazil. The area colored in gray indicates the overlapping of daily activity in each species pair. The parallel lines in each graph indicate the mean time of sunrise (yellow) and sunset (blue) in the study region. Below each graph is the coefficient of overlap for each pair of species, and the confidence interval.
Fig. 3 in Daily and seasonal activity patterns of a felid assemblage in a forest-grassland mosaic in southern Brazil
Fig. 3. Circular histograms with distribution and frequency of the seasonal activity of Leopardus guttulus, L. pardalis, L. wiedii and Puma concolor in Papagaios-de-Altitude Private Protected Area, Santa Catarina, Brazil. The arrows on each graph indicate the direction of the mean angle (µ). Each graph also exhibits the values of P and mean vector length (r).
Fig. 2 in Daily and seasonal activity patterns of a felid assemblage in a forest-grassland mosaic in southern Brazil
Fig. 2. Circular histograms with distribution and frequency of daily activity of Leopardus guttulus, L. pardalis, L. wiedii and Puma concolor in Papagaiosde-Altitude Private Protected Area, Santa Catarina, Brazil. The black arrows on each graph indicate the direction of the mean angle (µ). Each graph also exhibits the values of P and mean vector length (r).
Fig. 1 in Daily and seasonal activity patterns of a felid assemblage in a forest-grassland mosaic in southern Brazil
Fig. 1. Study area: Papagaios-de-Altitude Private Protected Area in Urupema, Santa Catarina, Brazil. On the top-right panel, colors indicate the type of soil-use in the area and its limits. The black dots in the map indicate the location of the camera traps.
Fig. 3 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 3. Temporal overlap of Leopardus guttulus (Hensel, 1872) activity during the different seasons: autumn/winter and spring/summer; the gray area represents the overlap between the activity observed in the two periods of the year and the vertical lines represent sunrise and sunset in each period (autumn/winter: 06h 45min sunrise and 18h 05min sunset; spring/summer: 06h 08min sunrise and 19h 33min sunset).
Fig. 4 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 4. Circular graph showing the distribution of Leopardus guttulus (Hensel, 1872) records in BRLJL, Rio Grande do Sul, Brazil, throughout the 12 months sampled. The black lines represent the concentration of records.
Fig. 2 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 2. Circular graphs showing the daily activity of Leopardus guttulus (Hensel, 1872) at the BRLJL, Rio Grande do Sul, Brazil, based on all records obtained for the species (n=25, all seasons), on records obtained during spring/summer (n=15), and on records from autumn/winter (n=10). The arrow on each circular graphs indicates the direction of the angular mean.
Fig. 1 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 1. Location of the study area in South America and the State of Rio Grande do Sul (left panel), with the indication of the geographic range of Leopardus guttulus (Hensel, 1872) (grey area) in Brazil, Paraguay and Argentina (above), and the Biological Reserve Lami JosÉ Lutzenberger – BRLJL in the municipality of Porto Alegre, Rio Grande do Sul, Brazil (below). On the right panel, a detailed map of the study area showing the limits of the BRLJL (black line), the vegetation types occurring in the area, the grid sQuares of 1 x 1 km (grey lines) designed to delimitate the zones where sampling stations (camera stations) were installed (black triangles).
Morphing libraries, QSAR models, and compounds predicted to be active on the Glucocorticoid receptor (GR)
<p>This repository contains datasets and files related to the computational drug discovery project of the chemical space exploration of the Glucocorticoid receptor. The accompanying Python code is freely available in the GitHub repository (<a title="https://github.com/Iagea/GRML_analyses" href="https://github.com/Iagea/GRML_analyses" target="_blank" rel="noreferrer noopener">https://github.com/Iagea/GRML_analyses</a>).</p> <p><strong>Morphing Libraries:</strong></p> <ul> <li><strong>GRML_library.csv:</strong> The GRML library is the collection of 999,015 virtual compounds generated by Molpher [1-2] starting from GR ligands with unique Bemis-Murcko scaffolds collected from the ChEMBL17 and IMG libraries.</li> <li><strong>RML_library.csv:</strong> The RML library is the collection of 1,346,310 virtual compounds generated by Molpher starting from compounds with unique Bemis-Murcko scaffolds randomly selected from the ZINC database.</li> </ul> <p><strong>IMG library:</strong></p> <ul> <li><strong>IMG_non_proprietary.csv</strong>: The non-proprietary IMG library subset containing 12,956 compounds and their corresponding B-scores from the primary screen.</li> </ul> <p><strong>Molpher inputs:</strong></p> <ul> <li><strong>GR_inputs.csv</strong>: The GR inputs are the ligands used to create the GRML library, 204 compounds from ChEMBL17 (95 compounds) and the non-proprietary dataset from IMG (109 compounds).</li> <li><strong>Random_inputs.csv</strong>: The random inputs are 249 random ZINC compounds used to create the Random library.</li> </ul> <p><strong>Model's training sets:</strong></p> <ul> <li><strong>Model33_training_set.csv</strong>: Random forest classification model training set, it includes 865 compounds; known GR actives and inactives from ChEMBL33 (738 compounds) and non-proprietary active ligands from the IMG library (127 compounds).</li> <li><strong>Model17_training_set.csv</strong>: Random forest classification model training set, it includes 601 compounds; known GR actives and inactives from ChEMBL17 (474 compounds) and non-proprietary active ligands from the IMG library (127 compounds).</li> <li><strong>RFR_training_set.csv</strong>: Random forest regression model training set, it includes 89 compounds; known GR actives and inactives from ChEMBL33 that fit into the GR pharmacophore with the four features we describe in our paper.</li> </ul> <p><strong>Models:</strong></p> <ul> <li><strong>Model33.pkl:</strong> Python pickle file containing the trained Random forest classification models used along with Mondrian cross-conformal prediction to classify GR actives/inactives. This model was trained with ChEMBL33 and IMG libraries.</li> <li><strong>Model17.pkl</strong>: Python pickle file containing the trained Random forest classification models used along with Mondrian cross-conformal prediction to classify GR actives/inactives. This model was trained with ChEMBL17 and IMG libraries.</li> <li><strong>RFR_models.pkl</strong><em>: </em>Python pickle file containing the 100 trained random forest regression models used to rank the proposed active morphs. These models were trained with the RFR_training_set.csv.</li> </ul> <p><strong>Active predicted morphs:</strong></p> <ul> <li><strong>all_morphs_actives</strong><em><strong>_</strong></em><strong>predicted.xlsx:</strong> An Excel spreadsheet containing two sheets. 1) All 22,524 GRML active predicted morphs. 2) All 4,341 RML active predicted morphs. The QED, NIBR Severity Score, and Molskill Score are given for each morph.</li> </ul> <p><strong>Proposed GR active ligands:</strong></p> <ul> <li><strong>designed_ligands.xlsx</strong>: An Excel spreadsheet containing two sheets. 1) All 54 designed GR ligands with their QED, NIBR severity score, MolSkill score, consensus ranking from the 100 RFR models, and the result of the manual annotation and remarks, if available. 2) The structure of the 54 ligands based on their manual annotation and presence or not in ChEMBL33 database.</li> </ul> <p>Researchers and professionals in the field of drug discovery and cheminformatics may find these resources useful for further analysis and investigations.</p> <p><strong>Bibliography</strong></p> <p>[1] Hoksza, D., Škoda, P., Voršilák, M. <em>et al.</em> Molpher: a software framework for systematic chemical space exploration. <em>J Cheminform</em> <strong>6</strong>, 7 (2014). https://doi.org/10.1186/1758-2946-6-7</p> <p>[2] <a href="https://github.com/lich-uct/molpher-lib">https://github.com/lich-uct/molpher-lib</a></p>
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini. in Nodularia vladivostokensis (Bivalvia: Unionidae) from Razdolnaya River (Primorye, Russia)
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini.
Faa1 membrane binding drives positive feedback in autophagosome biogenesis via fatty acid activation
<p>Autophagy serves as a stress response pathway by mediating the degradation of cellular material within lysosomes. In autophagy, this material is encapsulated in double-membrane vesicles termed autophagosomes, which form from precursors referred to as phagophores. Phagophores grow by lipid influx from the endoplasmic reticulum into Atg9-positive compartments and local lipid synthesis provides lipids for their expansion. How phagophore nucleation and expansion are coordinated with lipid synthesis is unclear. Here, we show that Faa1, an enzyme activating fatty acids, is recruited to Atg9 vesicles by directly binding to negatively charged membranes with a preference for phosphoinositides such as PI3P and PI4P. We define the membrane-binding surface of Faa1 and show that its direct interaction with the membrane is required for its recruitment to phagophores. Furthermore, the physiological localization of Faa1 is key for its efficient catalysis and promotes phagophore expansion. Our results suggest a positive feedback loop coupling phagophore nucleation and expansion to lipid synthesis.</p>
Real-bogus scores for active anomaly detection
<p>Data description for <a href="https://arxiv.org/abs/2409.10256">Semenikhin et al., 2024</a></p> <p>The dataset consists of the following files:</p> <p><strong>feature_snad4_r_100.dat</strong> contains light curve feature data for objects, where each object is represented by 54 feature values. These values are encoded as little-endian single-precision IEEE-754 floating-point numbers (32-bit floats). Feature names are listed in the plain text file <strong>feature_snad4_r_100.name</strong>, with one name per line.<br><strong>sid_snad4_r_100.dat</strong> contains ZTF DR object identifiers, encoded as little-endian 64-bit unsigned integers.</p> <p><strong>exp_feature_snad4_r_100.dat</strong> contains the same features as <strong>feature_snad4_r_100.dat</strong>, but with an additional column representing the real-bogus classifier prediction. Each object in this file corresponds to 55 features: the original 54 features plus 1 additional feature. Feature names for this file are provided in <strong>exp_feature_snad4_r_100.name</strong>.</p> <p>The files <strong>sid_snad4_r_100.dat</strong>, <strong>feature_snad4_r_100.dat</strong>, and <strong>exp_feature_snad4_r_100.dat</strong> share the same object order.</p> <p><br>Below is a sample Python script for accessing the data using NumPy:</p> <p><code>import numpy as np</code></p> <p><code># Load object IDs</code><br><code>oid = np.memmap('sid_snad4_r_100.dat', mode='c', dtype=np.uint64)</code></p> <p><code># Load features and reshape</code><br><code>feature = np.memmap('feature_snad4_r_100.dat', mode='c', dtype=np.float32).reshape(oid.shape[0], -1)</code></p> <p><code># Print dataset information</code><br><code>print(f'Number of objects: {len(oid)}')</code><br><code>print(f'Features shape: {feature.shape}')</code></p>
Dataset for "From CO2 to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-Alloyed GaInSn Catalyst"
<p>Experimental raw data for the article "<em>From CO<sub>2</sub> to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-alloyed GaInSn Catalyst</em>", published in <em>Journal of Physical Chemistry C </em>(2024). DOI:10.1021/acs.jpcc.4c05482.</p> <p>The data set is organized according to the publication's figures. XPS data given here is the raw data without binding energy calibration. For the manuscript, binding energies in a series of samples were calibrated by taking the strongest peak, where no chemical shift was to be expected, as a reference.</p>
Mechanochemical induced swelling-activation of a gastric-deployable 4D printed polypill inspired by natural hygromorphic actuators - Underlying data
<p>Underloying microfocus CT data of <em><strong>Mechanochemical induced swelling-activation of a gastric-deployable 4D printed polypill inspired by natural hygromorphic actuators</strong></em> by Konstantina Chachlioutaki, Nikolaos Papas, Zisis Chatzis, Orestis L. Katsamenis, Stephanie K. Robinson, Konstantinos Tsongas, Nikolaos Bouropoulos, Dimitrios G. Fatouros, Dimitrios Tzetzis, Christina Karavasili</p> <ul> <li>K. Chachlioutaki, Z. Chatzis, D.G. Fatouros, C. Karavasili<br>Laboratory of Pharmaceutical Technology, Department of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece</li> <li>N. Papas, D. Tzetzis, C. Karavasili<br>Digital Manufacturing and Materials Characterization Laboratory, School of Science and Technology, International Hellenic University, 57001 Thermi, Greece</li> <li>O.L. Katsamenis, S.K. Robinson<br>μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University road, Highfield campus, Southampton, SO17 1BJ, UK<br>Institute for Life Sciences, University of Southampton, University road, Highfield campus, Southampton, SO17 1BJ, UK</li> <li>K. Tsongas<br>Department of Industrial Engineering and Management, School of Engineering, International Hellenic University, 57001 Thessaloniki, Greece</li> <li>N. Bouropoulos<br>Department of Materials Science, University of Patras, 26504 Patras, Greece</li> </ul> <p> </p>
Apathy, motivation, and physical activity behavior: Material, data and R code
<p>This new release includes updates to the code and additional material following the peer review process conducted by Communications in Kinesiology.</p>
Portimine A toxin causes skin pathology through ZAKα-dependent NLRP1 inflammasome activation: LC-MS/MS raw data for Figure 1. C
<p>This dataset pertains to the LC-MS/MS analyses conducted as part of a study on microalgal toxins present in samples from Senegal, published in the paper entitled <em>"Portimine A toxin causes skin pathology through ZAK</em><em>α</em><em>-dependent NLRP1 inflammasome activation."</em> The data correspond to the quantification results of environmental samples presented in Figure 1C.</p> <p>The raw data were acquired using Analyst software (Applied Biosystems proprietary software). The materials and methods used to generate these data are detailed in the associated publication in <em>EMBO Molecular Medicine</em> (ISSN: 1757-4676, 2024).</p>
A legacy of submarine slope failure in seismic reflection data along the active Hikurangi Margin, Aotearoa New Zealand
<p><span>We present a database that documents mass transport deposits (MTDs) in 32 marine geophysical surveys, encompassing >38,000 line-km of 2D seismic profiles. We map and characterise 737 MTDs, showing variations in size, location and style of failure, which we attribute to changes in geomorphic setting from north to south. MTDs in the northern Hikurangi margin, characterised by a high taper wedge and seamount subduction, show a broad range in size, with the highest proportion of MTDs displaying blocky or intact internal architecture. The central margin, characterised by lower wedge taper, hosts the most MTDs (51%), albeit with the thinnest (on average) and clustering within interridge basins. The southern Hikurangi margin hosts widespread submarine canyons and the largest (on average) MTDs, based on area and thickness. We demonstrate the importance of seismic archives in providing new insights into MTD preservation and discuss the bias between seafloor geomorphology and subseafloor seismic data in quantifying MTD occurrence. Our findings support the interrogation of the varied and complex causes of submarine landslides along active margins generally, as well as regions prone to cascading geohazards and landslide-induced tsunami. </span></p>
Web browser useragent and activity tracking data
<p>600 000 000 web traffic records normalized into MySQL tables using TokuDB storage, complete with original web server response codes. Suitable for browser data and trend analysis as well as AI training of exploit and bot detection algorithms. The data had been collected from multiple Apache 2.x web servers across 8000+ domain names with special care for GDPR compliance.</p> <p> </p>
HDAC6 screening dataset using tau-based substrate in an enzymatic assay yields selective inhibitors and activators
<p><strong>Structure and information of the data file</strong></p> <p>DATA SET; Contains the information to which data set this information belongs. There are four possibilities denoted 1 to 4. Data set1: Enzymatic assay of human HDAC6 with commercial peptide substrate. Data set2: Enzymatic assay of human HDAC6 with custom peptide substrate. Data set3: Hit confirmation of the active molecules of the enzymatic assay of human HDAC6 with custom peptide substrate. Data set4: Determination of IC50 values for inhibition of enzymatic assay of human HDAC6 with custom peptide substrate.</p> <p>INTERNAL NAME; An internal name which enables identification of the compound within data sets from Fraunhofer ITMP ScreeningPort.</p> <p>TYPE; Type of data. Either 'inhibition' for normalized inhibition values or 'IC50' for enzymatic IC50.</p> <p>RELATION; Relation between TYPE and VALUE, always '='.</p> <p>VALUE; Value of the normalized inhibition or the enzymatic IC50.</p> <p>UNITS; Unit of the value. Either '%' for the normalized inhibition or 'uM' for the enzymatic IC50.</p> <p>NAME; Trade name of the chemical compound.</p> <p>SMILES; The canonical Smile of the chemical compound.</p> <p> </p> <p><strong>A</strong><strong>bstract</strong></p> <p>Histone deacetylase 6 (HDAC6) and HDAC10 are unique among the other HDACs as they consist of two domains instead of one. Only in the case of HDAC6 both domains are active resulting in a number of unique deacetylase reactions. Interestingly, HDAC6 can regulate the microtubule network and plays a role in the degradation of misfolded and aggregated proteins. We therefore developed a substrate (Boc-Ile-Asp-(Dimethyl)Lys-(Ac)Lys-aminoluciferin) based on a critical acetylation site of misfolded human Tau, a hallmark of Alzheimer’s Disease. This substrate was used to screen a 5632 compound encompassing repurposing library at 10 µM in a coupled, luminescence based assay. The assay was miniaturised to 10 µL per enzymatic reaction. For comparison, a generic HDAC substrate (BOC-Gly-(Ac)Lys-aminoluciferin) was also used to screen the same library. Both substrates rely on a cascade of enzymatic reactions. First, HDAC6 deacetylates the substrate followed by cleavage of aminluciferin from the peptide by porcine Trypsin and conversion of the aminoluciferin using firefly Luciferase. Compounds with an activity of at least 75% inhibition against the custom human Tau based substrate were confirmed in triplicates at the screening concentration of 10 µM. Confirmed hits, activity of at least 75%, where analysed in 8 point or 15 point dose response curves, depending on their activity. The data presented here encompass both primary data sets including 5632 compounds as well as 249 values from hit confirmation screening against the hTau based substrate and 151 IC<sub>50</sub> values from confirmed hits.</p> <p> </p> <p><strong>Methods of data generation</strong></p> <p><strong>Enzymatic assay of human HDAC6 with commercial peptide substrate. </strong></p> <p>The assay using the commercial peptide substrate (BOC-Gly-(Ac)Lys-aminoluciferin) was obtained from Promega Inc.. In the beginning the assay buffer is thawed and the lyophilized substrate is dissolved according to the technical manual (Promega Inc.) to create the substrate reagent. HDAC6 (obtained from BPS Biosciences) is dissolved in assay buffer at 0.2 nM, which is twice the final assay concentration. Compounds and controls are added to the plates using acoustic dispensing to reach a final concentration of 10 µM in the assay followed by 5 µl enzyme solution per well. Plates are centrifuged shortly and incubated for 10 min at RT. Afterwards, 5 µL/well substrate solution are added to the wells, centrifuged shortly and incubated for 10 min prior detection of the luminescence signal on a multimode reader. Primary screening was done at one concentration (10 µM) in singlicates.</p> <p> </p> <p><strong>Enzymatic assay of human HDAC6 with custom peptide substrate. </strong></p> <p>The assay was designed based on a commercial HDAC6 assay available from Promega Inc. This luminescence assay works by an aminoluciferin coupled HDAC6 peptide substrate. Upon deacetylation of the peptidic substrate by HDAC6 (obtained from BPS Biosciences) Trypsin (obtained from Sigma-Aldrich) can cleave the aminoluciferine from the peptide which can be converted by Luciferase (obtained from AAT Bioquest) to the detected signal. First, a twofold concentrated enzyme solution was generated, consisting of 4 nM HDAC6 and 0.1% BSA in HEPES buffer (25 mM HEPES, 137 mM NaCl, 2.7 mM KCl and 1 mM MgCl2, pH 7.0). Second, a twofold peptide solution was generated containing 100 µM custom made peptide (Boc-Ile-Asp-(Dimethyl)Lys-(Ac)Lys-aminoluciferin) in HEPES buffer. Compounds and controls are added to the plates using acoustic dispensing to reach a final concentration of 10 µM in the assay followed by 5 µl enzyme solution per well. Plates are centrifuged shortly and 5 µL/well peptide solution are added to the wells, centrifuged shortly and incubated for 30 min at RT. Afterwards, 5 µL detection reagent (0.067 mg/mL Luciferase, 133.3 µM ATP, 0.133 mg/mL Trypsin in HEPES buffer) were added to each well. Plates were centrifuged shortly and measured on a multimode reader after 30 min incubation at RT in the dark. Primary screening was done at one concentration (10 µM) in singlicates.</p> <p> </p> <p><strong>Hit confirmation of the active molecules of the enzymatic assay of human HDAC6 with custom peptide substrate</strong></p> <p>The assay was designed based on a commercial HDAC6 assay available from Promega Inc. This luminescence assay works by an aminoluciferin coupled HDAC6 peptide substrate. Upon deacetylation of the peptidic substrate by HDAC6 (obtained from BPS Biosciences) Trypsin (obtained from Sigma-Aldrich) can cleave the aminoluciferine from the peptide which can be converted by Luciferase (obtained from AAT Bioquest) to the detected signal. First, a twofold concentrated enzyme solution was generated, consisting of 4 nM HDAC6 and 0.1% BSA in HEPES buffer (25 mM HEPES, 137 mM NaCl, 2.7 mM KCl and 1 mM MgCl2, pH 7.0). Second, a twofold peptide solution was generated containing 100 µM custom made peptide (Boc-Ile-Asp-(Dimethyl)Lys-(Ac)Lys-aminoluciferin) in HEPES buffer. Compounds and controls are added to the plates using acoustic dispensing to reach a final concentration of 10 µM in the assay followed by 5 µl enzyme solution per well. Plates are centrifuged shortly and 5 µL/well peptide solution are added to the wells, centrifuged shortly and incubated for 30 min at RT. Afterwards, 5 µL detection reagent (0.067 mg/mL Luciferase, 133.3 µM ATP, 0.133 mg/mL Trypsin in HEPES buffer) were added to each well. Plates were centrifuged shortly and measured on a multimode reader after 30 min incubation at RT in the dark. Hit confirmation was done at one concentration (10 µM) in triplicates.</p> <p> </p> <p><strong>Determination of IC50 values for inhibition of enzymatic assay of human HDAC6 with custom peptide substrate</strong></p> <p>The assay was designed based on a commercial HDAC6 assay available from Promega Inc. This luminescence assay works by an aminoluciferin coupled HDAC6 peptide substrate. Upon deacetylation of the peptidic substrate by HDAC6 (obtained from BPS Biosciences) Trypsin (obtained from Sigma-Aldrich) can cleave the aminoluciferine from the peptide which can be converted by Luciferase (obtained from AAT Bioquest) to the detected signal. First, a twofold concentrated enzyme solution was generated, consisting of 4 nM HDAC6 and 0.1% BSA in HEPES buffer (25 mM HEPES, 137 mM NaCl, 2.7 mM KCl and 1 mM MgCl2, pH 7.0). Second, a twofold peptide solution was generated containing 100 µM custom made peptide (Boc-Ile-Asp-(Dimethyl)Lys-(Ac)Lys-aminoluciferin) in HEPES buffer. Compounds and controls are added to the plates using acoustic dispensing to reach a final concentration of 10 µM in the assay followed by 5 µl enzyme solution per well. Plates are centrifuged shortly and 5 µL/well peptide solution are added to the wells, centrifuged shortly and incubated for 30 min at RT. Afterwards, 5 µL detection reagent (0.067 mg/mL Luciferase, 133.3 µM ATP, 0.133 mg/mL Trypsin in HEPES buffer) were added to each well. Plates were centrifuged shortly and measured on a multimode reader after 30 min incubation at RT in the dark. IC50 values were determined using 7 point dose response curves (DRCs) between 20 µM and 312 nM. In case inhibition values were not below 50% additional 7 point DRCs were measured, starting at 312 nm with a dilution factor of 2. All DRCs were recorded in triplicates.</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.