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1,204 results for “Enzyme”
Data from "Allostery and evolution: a molecular journey throught the structural and dynamical landscape of an enzyme super family."
<p>This data accompanies the paper entitled Allostery and evolution: a molecular journey throught the structural and dynamical landscape of an enzyme super family.</p> <p>The zip archive contains: </p> <p>1- Starting configurations of the proteins after equilibration in PDB format and trajectories of unrestrained molecular dynamics simulations with the positions of the proteins every 100 ps in XTC gromacs format are provided for all systems. </p> <p>2- The free energy profiles and histograms are provided for all umbrella sampling simulations and the scripts used to run it with gromacs.</p>
Metabolic enzymes moonlight as selective autophagy receptors to protect plants against viral-induced cellular damage
<p>The dataset contains all the original raw files for the following study:</p> <p><strong>Metabolic enzymes moonlight as selective autophagy receptors </strong><strong>to protect plants against viral-induced cellular damage</strong></p> <p>Marion Clavel<sup>1,2,*</sup>, Anita Bianchi<sup>1</sup>, Roksolana Kobylinska<sup>1</sup>, Roan Groh<sup>1,3</sup>, Juncai Ma<sup>4</sup>, Ranjith K. Papareddy<sup>1</sup>, Nenad Grujic<sup>1</sup>, Lorenzo Picchianti<sup>1,3</sup>, Ethan Stewart<sup>5</sup>, Michael Schutzbier<sup>1</sup>, Karel Stejskal<sup>1</sup>, Juan Carlos de la Concepcion<sup>1</sup>, Victor Sanchez de Medina Hernandez<sup>1,3</sup>, Yoav Voichek<sup>1</sup>, Pieter Clauw<sup>1</sup><strong>, </strong>Joanna Gunis<sup>1</sup>, Gerhard Durnberger<sup>1</sup>, Jens Christian Muelders<sup>2</sup>, Annett Grimm<sup>2</sup>, Arthur Sedivy<sup>5</sup>, Mathieu Erhardt<sup>6</sup>, Victoria Vyboishchikov<sup>1</sup>, Peng Gao<sup>1</sup>, Esther Lechner<sup>6</sup>, Emilie Vantard<sup>6</sup>, Jakub Jez<sup>5</sup>, Elisabeth Roitinger<sup>1</sup>, Pascal Genschik<sup>6</sup>, Byung-Ho Kang<sup>4</sup>, Yasin Dagdas<sup>1,*</sup></p> <p><strong> </strong></p> <p><strong>Affiliations</strong></p> <p><sup>1</sup>Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenter, Vienna, Austria.</p> <p><sup>2</sup>Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany</p> <p><sup>3</sup>Vienna BioCenter PhD Program, Doctoral School of the University at Vienna and Medical University of Vienna, Vienna, Austria</p> <p><sup>4</sup>School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China</p> <p><sup>5</sup>Vienna Biocenter Core Facilities (VBCF), Vienna, Austria</p> <p><sup>6</sup>Institut de Biologie Moléculaire des Plantes, CNRS, Université de Strasbourg, 12, rue du Général Zimmer, 67084 Strasbourg, France</p> <p> </p> <p>*Correspondence: Marion Clavel (marion.clavel@mpimp-golm.mpg.de),</p> <p> Yasin Dagdas (yasin.dagdas@gmi.oeaw.ac.at)</p> <p><strong> </strong></p> <p><strong>Abstract</strong></p> <p>RNA viruses co-opt the host endomembrane system and organelles to build replication complexes for infection. How the host responds to these membrane perturbations is poorly understood. Here, we explore the autophagic response of <em>Arabidopsis thaliana</em> to three viruses that hijack different cellular compartments. Autophagy is significantly induced within systemically infected tissues, its disruption rendering plants highly sensitive to infection. Contrary to being an antiviral defense mechanism as previously suggested, quantitative analyses of the viral loads established autophagy as a tolerance pathway. Further analysis of one of these viruses, the Turnip Crinkle Virus (TCV) that hijack mitochondria, showed that despite perturbing mitochondrial integrity, TCV does not trigger a typical mitophagy response. Instead, TCV and Turnip yellow mosaic virus (TYMV) infection activates a distinct selective autophagy mechanism, where oligomeric metabolic enzymes moonlight as selective autophagy receptors and degrade key executors of defense and cell death such as EDS1. Altogether, our study reveals an autophagy-regulated metabolic rheostat that gauges cellular integrity during viral infection and degrades cell death executors to avoid catastrophic amplification of immune signaling.</p> <p> </p> <p>One archive corresponds to one main or supplemental figure.</p>
Figure 6 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential
Figure 6. Comparative %age inhibition of AChE by Au NPs, crud extract and allanzanthane.
Figure 5 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential
Figure 5. UV-Visible spectra of green synthesized gold nanoparticles.
Figure 7 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential
Figure 7. Comparative %age inhibition of BChE by Au NPs, crude extract and Allanzanthane.
Figure 2 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential
Figure 2. TEM images of green synthesized Au NPs at different magnifications.
Figure 1 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential
Figure 1. SEM images of green synthesized of Au NPs at different magnifications.
Figure 3 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential
Figure 3. FTIR spectrum of green synthesized gold nanoparticles.
Figure 1 in Recent progress in magnetic nanoparticles and mesoporous materials for enzyme immobilization: an update
Figure 1. Mesoporous material-functioned MNPs.
Enriching productive mutational paths accelerates enzyme evolution
<p>Quantum mechanical calculations of the Kemp elimination catalyzed by HG3.R5: cluster model transition structures (full QM) in MOL2 format), and enzyme-substrate complex and transition structures (hybrid QM/MM) in PDB format.</p>
The process of HDAC11 Assay Development: enzyme stability
<p>Before performing the kinetic study for calculating the Km for HDAC11, it is important to know the duration of the stability of the protein under the assay conditions. This is being analyzed here.</p> <p> </p> <p><strong>Note: </strong>1. In the assay buffer, BSA conc. is 0.5 mg/ml (instead of 0.5%).</p> <p> 2. In the 7.5 ul developer solution, 40 uM of TSA (Trichostatin A) is also included.</p>
Enzymes from the BRENDA database annotated with organism growth temperatures
<p>Experimental as well as predicted organism growth temperatures were used to annotate enzymes from the BRENDA database (doi: 10.1093/nar/gky1048, https://www.brenda-enzymes.org) version 2018.1. The growth temperature annotation can be used as an estimate of the enzymes catalytic optima.</p> <p>The "enzyme_to_growth_temp_mapping.tsv" file is a tab-separated file with the data headers: ec, uniprot_id, domain, organism, source and growth_temp. The ec column lists enzyme classes. The uniprot_id lists UniProt identifiers. The domain column lists the domain of life (superkingdom), either Archaea, Bacteria, or Eukaryota. The organism column lists organism names, with strain designations removed and formatted to lowercase characters with an underscore _ separating the name parts. The source column lists whether experimental growth temperatures or predicted ones were used for the annotation. The growth_temp column lists, in degrees centigrade, the organism growth temperature.</p> <p>The "all_enzyme_sequences.fasta" file follows the standard FASTA format and contains the protein sequences for all annotated enzymes. UniProt identifiers is used as a header for each of the sequences.</p> <p> </p>
Research data supporting "Rolling Circle Transcription-Amplified Hierarchically Structured Organic-Inorganic Hybrid RNA Flowers for Enzyme Immobilization""
<p>Raw research data supporting the publication:</p> <p>Wang Y. et al., 2019, ACS Applied Materials and Interfaces, DOI: 10.1021/acsami.9b04663</p>
HPTLC Data of "Metal Ion Cofactors Modulate Integral Enzyme Activity By Varying Differential Membrane Curvature Stress"
<p>Lipid hydrolysis by the integral membrane protein OmpLA (outer membrane phospholipase). The enzymatic degradation of the proteoliposomes was determined by TLC. After lipid extraction against organic solvent (2:1 vol/vol chloroform/methanol) based on the Folch extraction method, the samples were spotted on a silica plate (Sigma-Aldrich, Steinheim, Germany) with the automatic TLC sampler 4 (CAMAG, Muttenz, Switzerland). The mobile phase in the developing chamber was a solvent mixture composed of 32.5:12.5:2 vol/vol/vol CHCl_3/MeOH/H_2O. After drying, the plate was immersed in a developing bath (5.08 g MnCl_2 dissolved in 480 ml H_2O, 480 ml EtOH and 32 ml H_2SO_4), which is sensitive to double bonds, and dried for 15 min at 120°C. To quantify the lipid concentrations the plate was scanned with the TLC scanner 3 (CAMAG, Muttenz, Switzerland) and further analyzed with WinCats software.</p> <p>Lipids:</p> <ul> <li>1-palmitoyl-2-oleoyl-<em>sn</em>-glycero-3-phosphocholine (POPC)</li> <li>1-palmitoyl-2-oleoyl-<em>sn</em>-glycero-3-phosphoethanolamine (POPE)</li> <li>1-palmitoyl-2-oleoyl-<em>sn</em>-glycero-3-phosphoglycerol (POPG)</li> </ul>
The official dataset of the paper "Sunflower Meal Valorization through Enzyme-Aided Fractionation and the Production of Emerging Prebiotics"
<p>This is the official repository of the paper "Sunflower Meal Valorization through Enzyme-Aided Fractionation and the Production of Emerging Prebiotics" (<a href="https://doi.org/10.3390/foods13162506">https://doi.org/10.3390/foods13162506</a>)</p> <p>DISCLAIMER</p> <p>The repository contains experimental data and is published for the sole purpose of giving additional background details on the respective publication "Sunflower Meal Valorization through Enzyme-Aided Fractionation and the Production of Emerging Prebiotics" ((<a href="https://doi.org/10.3390/foods13162506">https://doi.org/10.3390/foods13162506</a>).<strong> </strong>See the README.txt file for more details.</p>
Reactzyme: A Benchmark for Enzyme-Reaction Prediction
<h3>Official dataset of <span>Reactzyme -</span> <span>Reactzyme: A Benchmark for</span><span>Enzyme-Reaction Prediction.</span></h3> <p><span>Our study utilizes a comprehensive dataset compiled from the SwissProt and Rhea databases. SwissProt, a curated subset of the UniProt database, has been selected for its high-quality, human-derived functional annotations of protein sequences. This section of UniProt is particularly valuable for its expert-reviewed entries, which ensure reliable and accurate functional data, making it ideal for our analysis. Rhea is employed for its precise mapping from enzymes to specific catalyzed functions, offering detailed descriptions of biochemical reactions. </span></p> <p><span>The SwissProt and Rhea dataset are downloaded on January 8, 2024, and includes data entries up to this date, providing the most recent and comprehensive data available for our study. We selectively exclude water molecules and unspecific functional groups that could mask the true molecular structures. Conversely, we remove metal ions, gas molecules, and other small molecules because of their potential to bind to proteins, a characteristic that presents a valuable learning feature for our model. To this end, the total dataset comprises 178,463 positive enzyme-reaction pairs, including 178,327 unique enzymes and 7,726 unique reactions. </span></p>
Supplementary data and code to "Cellular location shapes quaternary structure of enzymes" by György Abrusán and Aleksej Zelezniak
<p>Scripts and high-level data to reproduce the figures and supplementary figures of "Cellular location shapes quaternary structure of enzymes" by György Abrusán and Aleksej Zelezniak. <em>Nature Communications</em> (2024) 15:8505.</p>
Impact of water models on structure and dynamics of enzyme tunnels
<ul> <li>1-initial_topologies_coordinates.tar.gz <ul> <li>primary input coordinates and parameter-topology files of all initial systems (LinBwt, LinB32, and Linb86 variants of haloalkane dehalogenase) in OPC and TIP3P water models</li> <li>prepared with the tleap module of AMBER18 package</li> <li>parm7 and crd formatted</li> </ul> </li> <li>2-cap_domain_gate_distances.tar.gz <ul> <li>datasets with minimum distance calculation between Asp146 and Leu176</li> <li>calculated by CPPTRAJ module of AMBER 18 for each performed simulation</li> <li>plain text formatted</li> </ul> </li> <li>3-protein_trajectories-linbwt.tar.gz <ul> <li>three replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of LinBwt in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-closed.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-closed.tar.gz <ul> <li> two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>4-basic_analyses.tar.gz <ul> <li>datasets on RMSF, RMSD, RoG, and RDF from the CPPTRAJ module of AMBER 18</li> <li>for selected replicas 3x LinBwt, 2x LinB32-closed, 2x LinB32-open, 2x LinB86-closed and 2x LinB86-open</li> <li>plain text and PDB formatted</li> </ul> </li> <li>5-caver_analyses.tar.gz <ul> <li>results of tunnel analyses for two replicas of open & closed state each for LinB32 & LinB86 in OPC and TIP3P, and three replicas of LinBWT in OPC and TIP3P</li> <li>generated by CAVER 3.0 using "Divide-and-conquer approach" (MethodsX, 10, 2023, 101968)</li> <li>comprising csv and pdb formatted: tunnel_profiles.csv and bottlenecks.csv, stripped_system.10001.pdb, v_origins.pdb</li> <li>For this and following analyses, the names of the trajectories were modified as follows: <ul> <li>linbwt_opc1_2 = md1_opc_linbwt; linbwt_opc2_2 = md2_opc_linbwt; linbwt_opc3_2 = md3_opc_linbwt;</li> <li>linbwt_tip3p1_2 = md1_tip3p_linbwt; linbwt_tip3p2_2 = md2_tip3p_linbwt; linbwt_tip3p3_2 = md3_tip3p_linbwt;</li> <li>linb32-closed_opc1_2 = md1_closed_opc_linb32; linb32-closed_opc2_2 = md2_closed_opc_linb32;</li> <li>linb32-open_opc1_2 = md1_open_opc_linb32; linb32-open_opc2_2 = md2_open_opc_linb32;</li> <li>linb32-closed_tip3p1_2 = md1_closed_tip3p_linb32; linb32-closed_tip3p2_2 = md2_closed_tip3p_linb32;</li> <li>linb32-open_tip3p1_2 = md1_open_tip3p_linb32; linb32-open_tip3p2_2 = md2_open_tip3p_linb32;</li> <li>linb86-closed_opc1_2 = md1_closed_opc_linb86; linb86-closed_opc2_2 = md2_closed_opc_linb86;</li> <li>linb86-open_opc1_2 = md1_open_opc_linb86; linb86-open_opc2_2 = md2_open_opc_linb86;</li> <li>linb86-closed_tip3p1_2 = md1_closed_tip3p_linb86; linb86-closed_tip3p2_2 = md2_closed_tip3p_linb86;</li> <li>linb86-open_tip3p1_2 = md1_open_tip3p_linb86; linb86-open_tip3p2_2 = md2_open_tip3p_linb86.</li> </ul> </li> </ul> </li> <li>6-transport_tools_analyses.tar.gz <ul> <li>results of comparative analyses for all simulations generated in 5-caver_analyses.tar.gz</li> <li>generated by TransportTools 0.9.3</li> <li>comprising csv, pdb, plain text and py formatted: configuration file (config_TT.ini), tunnel_profiles (data folder) for all filtered tunnels and bottlenecks (data folder) for all filtered tunnels, statistics (statistics folder) and visualization (visualization folder)<br> </li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from " Allostery can convert binding free energies into concerted domain motions in enzymes"
<p>Data from " Allostery can convert binding free energies into concerted domain motions in enzymes"</p> <p> </p> <p>Electrophysiology data corresponding to the main text figures and supporting information figures. One representative set was chosen for each triplicate and included in this data set. For details are found in the ‘read me explanation.txt’</p> <p>PDB used for this paper can be found at; 4ake [http://doi.org/10.2210/pdb4AKE/pdb] and 1ake [http://doi.org/10.2210/pdb1AKE/pdb]</p> <p>Full uncropped scans of any cropped gel/blot images are provided.</p> <p>The zip folder contains the MATLAB code package HMM inference, specifically tailored to nanopore ionic current flow data, as analyzed in the publication and can also be found at: https://github.com/yulanvanoppen/nanopore-HMM</p> <pre><br> </pre>
A model-based approach to characterize enzyme-mediated response to antibiotic treatments: towards a model-guided classification
<p>This dataset, taken together with the scripts at <a href="https://gitlab.inria.fr/Public/InBio/esbl-escape">https://gitlab.inria.fr/Public/InBio/esbl-escape</a>, allows one to reproduce the analyses and figures of the article "A model-based approach to characterize enzyme-mediated response to antibiotic treatments: towards a model-guided classification".</p>
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