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5,942 results for “binding”
LOBSTER (Ligand Overlays from Binding SiTe Ensemble Representatives)
<p>LOBSTER ("Ligand Overlays from Binding SiTe Ensemble Representatives") is a dataset of ligand overlays designed to evaluate small molecule superposition tools.</p> <p><br>Based on all structures from the RCSB PDB, the dataset generation and filtering protocols are fully automated to avoid subjectivity in the selection of protein-ligand complexes and to gain the largest possible set of refined compounds. Affinity and activity data have been processed to select ligands with a high ligand efficiency.<br>Ligands were superimposed in their crystal pose by aligning the corresponding binding pockets to so-called ensembles. For poses generated in benchmark experiments, this offers an objective comparison to the superimposed ligand crystal poses. A clustering of ensembles created with the same protein-ligand complexes ensures the diversity of the LOBSTER set.<br>The 671 ligand ensembles comprise a total of 3212 unique ligands from 3521 different protein-ligand complexes. A total of 72 734 ligand pairs have been derived from the ensembles. Ten subsets were generated from the pairs according to the shape overlap of the pairs, quantified by the Shape Tversky Index.</p>
Impact of the dynamics of the catalytic arginine on nitrite and chlorite binding by dimeric chlorite dismutase
<ul> <li><strong>Data type</strong>: spectroscopic measurements (UV-visible, ECD, EPR), DSC measurements, X-ray crystallography datasets, kinetic measurements, Molecular Dynamics simulations and data analysis.</li> <li>Files are in <strong>spc, par, DTA, DSC, dsx, csv, mtz </strong>formats</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements in <strong>spc</strong>,<strong> par</strong>,<strong> DTA </strong>and<strong> DSC</strong> formats</li> <li>EPR spectroscopic simulation and analyses in <strong>m </strong>and<strong> mat</strong> format</li> <li>UV-vis spectroscopic measurements in <strong>csv</strong> format</li> <li>ECD measurements in <strong>dsx</strong> format</li> <li>Enzyme activity data in <strong>csv</strong> format</li> <li>DSC measurements in <strong>csv </strong>format</li> <li>X-ray data in <strong>mtz</strong> format</li> <li>MD simulations data are in<strong> xlsx</strong> format</li> </ul> </li> <li>The data are <strong>generated</strong> by: <ul> <li>UV−vis spectra were recorded using a Cary 60 UV−vis spectrophotometer (Agilent).</li> <li>Electronic circular dichroism spectroscopy was performed using Chirascan (Applied Photophysics, Leatherhead, U.K.).</li> <li>X-Band CW-EPR experiments were performed on A) a Bruker ESP300E spectrometer equipped with a liquid helium cryostat (Oxford Inc.); B) a Bruker ELEXSYS E580 X-band spectrometer equipped with an Oxford ESR 900 continuous-flow helium cryostat and a Bruker ER 4122 SHQ resonator.</li> <li>Enzyme activity was measured polarographically following the release of O2 by using a Clark-type oxygen electrode (Oxygraph Plus; Hansatech Instruments, Norfolk, U.K.).</li> <li>Differential scanning calorimetry experiments were performed on a Micro-Cal PEAQ-DSC Automated instrument (Malvern Panalytical Ltd., Malvern, U.K.) equipped with an autosampler for 96-well plates and controlled by the MicroCal PEAQ-DSC software.</li> <li>Crystallization experiments were performed using the sitting drop vapor diffusion method in SWISSCI MRC three-well crystallization plates (Molecular Dimensions, Newmarket, U.K.). Crystallization drops were set up using a mosquito crystallization robot (TTP Labtech). Commercially available crystallization screens were used for further screening. Crystallization plates were stored in a Formulatrix RI-1000 imaging device at 22 °C. Data were collected at 100 K using an Eiger2 XE 16 M detector at the beamline i04 at the Diamond Light Source (DLS, Didcot, United Kingdom). Further data was collected at 100 K at beamline ID30A-3 using a Eiger X 4 M detector, at beamline ID-23-1 using a Pilatus 6 M detector and at beamline ID23-2 using a PILATUS3 X 2 M detector at the of European Synchrotron Radiation Facility (ESRF, Grenoble, France). Data sets were processed with XDS, and symmetry equivalent reflections merged with XDSCONV.</li> <li>Molecular dynamics simulations were performed using the GROMOS11 molecular simulation package and GROMOS force field 54A8. Analyses of the coordinate trajectories was done with Gromos++ programs hbond, rdf and mdf. Coordinates at specific time point were generated using Gromos++ program frameout and analysed using PyMOL Molecular Graphics System.</li> <li> <ul> <li>Files in <strong>PARACAT_WP3_20211216_01_EPR </strong>folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in <strong>spc</strong>/<strong>par </strong>or<strong> DTA/DSC</strong> formats; files in <strong>m</strong> format were used to process the data.</li> <li>Files in <strong>PARACAT_WP3_20211216_02_UV-vis </strong>folder includes UV-Vis spectroscopic measurements of pH-titration in <strong>csv</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20211216_03_ECD </strong>folder includes ECD measurements of the far UV (180–260 nm) and visible (260–500 nm) area, as well as unfolding curves in <strong>dsx</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20211216_04_activity </strong>folder includes Clark electrode/activity measurements in<strong> csv</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20211216_05_DSC </strong>folder includes DSC measurements in <strong>csv </strong>format.</li> <li>Files in <strong>PARACAT_WP3_20211216_06_Xray </strong>folder includes pre-processed (from the beamline pipeline) data sets in <strong>mtz</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20211216_MD-Simulations </strong>folder includes pre-processed molecular dynamics data in<strong> xlsx</strong> format.</li> </ul> </li> </ul> </li> </ul> <p>NB. See the “READ ME” text file in each subfolder for more detailed information on files organization.</p> <ul> <li><strong>Information on</strong>: <ul> <li>Abbreviations:<strong> Cld</strong>, chlorite dismutase; <strong><em>C</em>Cld</strong>, chlorite dismutase from Cyanothece sp. PCC7425; <strong>CW</strong>, continuous wave; <strong><em>D</em></strong>, tetragonal zero-field splitting; <strong>DSC</strong>, differential circular calorimetry; <strong><em>E</em></strong>, rhombic zero-field splitting; <strong>ECD</strong>, electronic circular dichroism; <strong>EPR</strong>, electron paramagnetic resonance; <strong>HS</strong>, high-spin; <strong>LS</strong>, lowspin; <strong><em>Nd</em>Cld</strong>, chlorite dismutase from “Candidatus Nitrospira defluvii”; <strong>WT</strong>, wild type; <strong>ZFS</strong>, zero-field splitting.</li> </ul> </li> </ul> <p> </p> <ul> <li>Units of measurement: <ul> <li>Concentration: <strong>mM</strong> (millimolar), <strong>µM</strong> (micromolar), <strong>mg/mL</strong> (milligrams per milliliter), <strong>w/v %</strong> (weigth/volume) <strong>v/v %</strong> (volume/volume)</li> <li>Absorptivity: <strong>M<sup>-1</sup></strong> <strong>cm<sup>-1</sup></strong></li> <li>Volume: <strong>mL</strong> (milliliters), <strong>µL </strong>(microliters), <strong>nL</strong> (nanoliters)</li> <li>Wavelength:<strong> nm</strong> (nanometers)</li> <li>Temperature:<strong> °C</strong> (Celsius degrees), <strong>K</strong> (Kelvin degrees)</li> <li>Time:<strong> min</strong> (minutes), <strong>h</strong> (hours), <strong>ns</strong> (nanoseconds)</li> <li>Ellipticity: millidegrees</li> <li>Frequency: <strong>GHz</strong> (gigahertz), <strong>kHz</strong> (kilohertz)</li> <li>Power: <strong>mW</strong> (milliwatt)</li> <li>Pressure: <strong>atm</strong> (atmosphere)</li> </ul> </li> </ul>
Data: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites
<p>Complementary data for the paper: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites.</p>
Ligand binding remodels protein side chain conformational heterogeneity
<p>While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decrease and relative hydrophobicity increases. Across a series of 13 inhibitor bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from NMR studies suggesting that residual side chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.</p>
SIRAH-CoV2 initiative: RNA binding domain of nucleocapsid phosphoprotein (PDB id:6VYO)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 RNA binding domain of the nucleocapsid phosphoprotein in its APO form with Zn ions bound (PDB id:6VYO, Bioassembly 1). Simulations were performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The file 6VYO_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com. Additionally, the file 6VYO_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6VYO_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6VYO_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6VYO_SIRAHcg_prot_10us_skip10ns.prmtop 6VYO_SIRAHcg_prot_10us_skip10ns.ncrst 6VYO_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: NSP9 RNA binding protein (PDBid:6W4B)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 NSP9 RNA binding protein (PDB id: 6W4B, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6W4B_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W4B_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W4B_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W4B_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6M3M_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M3M_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6M3M_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M3M_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: S1 Receptor Binding Domain in complex with human antibody CR3022 (PDBid: 6W41)
<p>This dataset contains the trajectory of a 12 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV-2 receptor binding domain in complex with a human antibody CR3022 (PDB id: 6W41). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycans have been removed from the structures.</p> <p>The file 6W41_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W41_SIRAHcg_12us_prot.tar contains only the protein coordinates, while 6W41_SIRAHcg_12us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W41_SIRAHcg_12us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w41_SIRAHcg_prot.prmtop 6w41_SIRAHcg_prot.ncrst 6w41_SIRAHcg_prot_12us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
All-atom 500-nano seconds Molecular Dynamics Simulations of SARS-CoV-2 Spike Receptor-binding Domain bound with ACE2
<p>Data includes all of the trajectories (1000) of classical all-atom molecular dynamics (MD) simulations of of SARS-CoV2 Spike Protein/ACE2 complex (PDB ID: 6M0J). In order to decrease the size of the file only protein rajectories were provided. Simulation has been performed with Desmond. Protein was placed in the cubic boxes with explicit TIP3P water models that have 10.0 Å thickness from surfaces of protein. The system is neutralized by adding counter ions, and salt solution of 0.15M NaCl was also used to adjust the concentration of the systems. The long-range electrostatic interactions were calculated by the particle mesh Ewald method. A cutoff radius of 9.0 Å was used for both van der Waals and Coulombic interactions. The temperature was set as 310K initially, and Nose–Hoover thermostat was used for adjustment. Martyna–Tobias–Klein protocol was employed to control the pressure, which was set at 1.01325 bar. The time-step was assigned as 2.0 fs. The default values were used for minimization and equilibration steps, and finally 500 nano-seconds (ns) production run was performed for the simulation.</p>
Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).
<p>Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).</p> <p>This folder contains the files needed to start each of the models described in the paper. The files were created using MOE 2020 software made by Chemical Computing Group and run on NAMD2.</p> <p>The models identifiers in the paper correspond to the following terms in the code:</p> <p>Substrate: "13_5_coa"</p> <p>Product: "13_5_coa_desat_fe3"</p> <p>Apoprotein: "13_5_no_ligand"</p> <p>Saturated Lipid: "13_5_nocoa"</p> <p>Desaturated Lipid: "13_5_nocoa_desat_fe3"</p> <p>CoA model: "13_5_coa_nolipid"</p> <p>Substrate-waterbox model: "13_5_coa_waterbox"</p> <p>Saturated Lipid-waterbox: "13_5_nocoa_waterbox"</p>
Simulation results for Sars-CoV2 3C-like main protease: TRAPP analysis of the binding site flexibility and results of the docking study
<p>Collection of data and scripts related to the paper:</p> <p>Jonas Gossen et al. "A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics" </p> <p>https://www.biorxiv.org/content/10.1101/2020.12.14.422634v2 doi: https://doi.org/10.1101/2020.12.14.422634</p> <p>ACS Pharmacology and Translational Science 2021 DOI: 10.1021/acsptsci.0c00215</p> <p> </p> <p> </p> <p><strong>1. TRAPP simulation results for Sars-CoV2 3C-like main protease:</strong></p> <p>include simulation of the binding pocket druggability, physical-chemical properties, and the binding site composition</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Protease_clean.ipynb">Protease_clean.ipynb</a> - Jupyter Notebook containing analysis of the generated data</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/allTables.zip">allTables.zip</a> - results of TRAPP simulations of the binding site flexibility using LRIP and tConcoord methods</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Every10-ligand_6LU7_R3.5.zip">Every10-ligand_6LU7_R3.5.zip</a> - results of TRAPP pocket analysis on the MD frames</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/PDB-Giulia.zip">PDB-Giulia.zip</a> - TRAPP pocket analysis of 40 PDB complexes of main protease</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/TRAPP_properties_PDB.xlsx">TRAPP_properties_PDB.xlsx</a> - binding pocket properties for 40 PDB complexes of main protease summarized in a table</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/DrugPDB_3structures.xlsx">DrugPDB_3structures.xlsx</a> - binding pocket properties for 3 PDB structures </p> <p><strong>2. Docking & Screening Results</strong></p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/TRAPP_secondSelection_VS.csv">TRAPP_secondSelection_VS.csv</a> - docking/screening of selected structures from TRAPP analysis</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Fred_VS.csv">Fred_VS.csv</a> - docking of PDB structures using Fred</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Glide_VS.csv">Glide_VS.csv</a> - docking of PDB structures using Glide</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS1.xlsx">TableS1.xlsx</a> - Available structures of SARS-CoV-2 Mpro selected for binding site analyses. </p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2A.xlsx">TableS2A.xlsx</a> - SiteScore analysis of all the deposited X-ray crystal structures for the Mpro.</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2B.xlsx">TableS2B.xlsx</a> - SiteScore analysis of the MSM ensemble (4-macrostates).</p>
Associated Data: RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features
<p>Additional digital data to "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features" (ChemRxiv preprint:<a href="https://doi.org/10.26434/chemrxiv.12636704.v1">https://doi.org/10.26434/chemrxiv.12636704</a>).</p> <p>Associated code can be found at: <a href="https://github.com/HITS-MCM/RASPDplus">https://github.com/HITS-MCM/RASPDplus</a></p> <p>Files:</p> <ul> <li>weights.tar.gz: contains the model weights of one random dataset split and its associated crossvalidation folds. Used for standard RASPD+ evaluation.</li> <li>additional_model_replicates.tar.gz: contains the remaining models trained on the full set of descriptors.</li> <li>external_test_sets.tar.gz: contains the descriptor tables for all external test sets used</li> <li>dude.tar.gz: contains the descriptor tables for and several identifier lists for evaluation on the Directory of Useful Decoys - Enhanced (DUD-E)</li> <li>run_outputs.tar.gz: Performance metric data and predicted values created during the model training and evaluation runs. Basis for the figures and metrics in the manuscript.</li> </ul> <p> </p>
Characteristics of human and viral RNA binding sites and site clusters recognized by SRSF1 and RNPS1
<p>This dataset was developed for the following article:</p> <p> Rogan PK, Mucaki EJ and Shirley BC. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections [version 1; peer review: awaiting peer review]. <em>F1000Research</em> 2020, <strong>9</strong>:943 (<a href="https://doi.org/10.12688/f1000research.25390.1">https://doi.org/10.12688/f1000research.25390.1</a>)</p> <p><strong>Section 1. Extended Data Tables</strong></p> <p>This archive contains the extended data tables for the research article "A proposed mechanism for molecular pathogenesis of severe RNA-viral pulmonary infections". These tables provide SRSF1, RNPS1 and hnRNP A1 binding site and information-dense cluster counts across various RNA viral genomes [including multiple SARS-CoV-2 and influenza strains] and the human transcriptome, the estimated SARS-CoV-2 doubling time necessary for viral genome SRSF1 binding site availability to exceed sites within the host transcriptome, and an analysis of influenza, dengue, and aplastic anemia patients misdiagnosed as irradiated by established radiation gene signatures.These tables are:</p> <p><strong>Section 1 - Table 1.</strong> RNPS1 and hnRNPA1 binding sites and Information-Dense Clusters for RNPS1 and<br> hnRNPA1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2A.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 1) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2B.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 2) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2C.</strong> Detailed Analysis of Information-Dense Clusters for RNPS1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2D.</strong> Detailed Analysis of Information-Dense Clusters for hnRNP A1 in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 3.</strong> Binding Site Analysis of Multiple Coronavirus Strains (Both Strands)<br> <strong>Section 1 - Table 4A.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Negative Strand Only)<br> <strong>Section 1 - Table 4B.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Both Strands)<br> <strong>Section 1 - Table 5.</strong> SRSF1, RNPS1 and hnRNPA1 Binding Sites and Information-Dense Clusters by Gene<br> <strong>Section 1 - Table 6A.</strong> Transcriptome-Wide Information Dense Clusters Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6B. </strong>Exome-Wide Information Dense Clusters within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 6C.</strong> Transcriptome-Wide Scan of Strong Binding Sites Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6D. </strong>Exome-Wide Scan of Strong Binding Sites within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 7.</strong> Rate of False Positives for Influenza, Dengue Virus and Aplastic Anemia Using<br> Radiation Signatures<br> <strong>Section 1 - Table 8.</strong> Radiation Model Genes Contributing to False Positives for Patients with Influenza A,<br> Dengue Virus, and Aplastic Anemia<br> <strong>Section 1 - Table 9A.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Positive-Strand Sites Only)<br> <strong>Section 1 - Table 9B.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Both Strands Considered)</p> <p><strong>Section 2. All SRSF1, hnRNPA1 and RNPS1 binding site tracks for human and viral genomes</strong></p> <p>We provide bedgraph tracks which provide the location and strength of binding sites (and binding site clusters) for SRSF1, RNPS1 and hnRNPA1 across the human transcriptome (GRCh37), the human exome (including +/-300nt surrounding the exon; non-intergenic only), and for all viral genome investigated in this study (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [two strains]). Note that if no clusters were found for a particular viral genome, a file for said genome will not be present in the Zenodo archive.</p> <p>Folder “Cluster-to-DRIPseq-Intersection-Tracks” contain tracks which indicate where binding site clusters have been identified, intersected with DRIP-seq and DRIPc-seq intervals which indicate where there is evidence of R-Loop formation in the human genome. The DRIP-seq dataset (GSE68845) is not strand specific. DRIPc-seq (GSE70189) is strand specific, and has been taken into account in the intersection (e.g. tracks only list positive strand clusters found in positive-strand DRIPc-seq intervals).</p> <p>Due to sheer size, the human transcriptome and exome tracks which indicate the location of individual binding sites are split into two separate files (separated by strand). While the custom tracks containing human binding site information are designed to be uploaded to the UCSC Genome Browser, files containing transcriptome-wide binding site information may be too large to be uploaded and may require further filtering (i.e. by chromosome).</p> <p>To be classified as a cluster, binding sites on the same strand must have <em>Ri</em> values which sum to >50 bits, each binding site must have a neighboring site within 25nt, and all binding sites in the cluster must have <em>R<sub>i</sub></em> greater than a minimum bit threshold. For human transcriptomes and exomes, this bit minimum was set to <em>R<sub>sequence</sub></em>. The bit minimum for viral binding sites was set to 0.1 * <em>R<sub>sequence</sub></em>. The information density-based clustering algorithm utilized in this work is described in Lu and Rogan 2018 (<a href="https://f1000research.com/articles/7-1933/v2">https://f1000research.com/articles/7-1933/v2</a>) and archived source code is available through Zenodo (<a href="https://dx.doi.org/10.5281/zenodo.1892051">https://dx.doi.org/10.5281/zenodo.1892051</a>).</p> <p><strong>Section 3. Binding site clusters - lollipop plots</strong></p> <p>Lollipop plots present the genomic coordinates and information densities of clusters across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). The height of the "lollipop" corresponds to the information density of a cluster. Labels above "lollipops" present the start and end genomic coordinate (GRCh37) of the cluster followed by the number of sites in the cluster enclosed in brackets. Lollipop plots associated with human transcriptomes/exomes each contain a single gene. Influenza has 8 segments and each segment requires its own plot, other viral genomes examined are presented in a single plot.</p> <p>File naming convention for human plots:</p> <ul> <li>RBP_Gene.png</li> <li>e.g. RNPS1_ADK.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>Virus[.InfluenzaSegment].RiThreshold.Strand.RBP.png</li> <li>e.g. Wuhan-Hu-1.complete-genome.4.2-bits.PosStrand.hnRNPA1.png</li> </ul> <p>The specified Ri threshold indicates all binding sites which comprise a cluster have <em>R<sub>i</sub></em> greater-than or equal to the threshold.</p> <p><strong>Section 4. Ri(b,l) matrices for all binding sites scanned</strong></p> <p>The information theory-based position weight matrices for the following RNA binding proteins (RBP) used in this study: SRSF1, hnRNPA1 and RNPS1. We investigated binding using two different RNPS1 binding models. While similar, these two models contained binding site information on opposing sides of the binding site motif which is why we found it prudent to scan with both models.</p> <p>Structure of each file:</p> <p>Line #1: Start position, End position and<em> R<sub>sequence</sub></em> [average strength of sequences used to generate the model]</p> <p>Subsequent lines describe the information on each position of the binding site:</p> <ul> <li>First four columns: <em>R<sub>i</sub></em> contribution of nucleotide at this position of the matrix [A, C, G, T]</li> <li>Row #5: Position of the matrix</li> <li>Last four columns: Number of binding sites used to generate model with a particular nucleotide at this position of the matrix [A, C, G, T]</li> </ul> <p>Example:</p> <p>-2.965775 1.282153 0.034225 -4.906891 0 1 19 8 0</p> <p>At zero position of the matrix (first nucleotide), a ‘C’ would have a positive contribution to binding site strength, a ‘G’ would be relatively neutral, and an ‘A’ or ‘T’ would negatively contribute to binding site strength.</p> <p>Generation of R<sub>i</sub>(b,l) matrices and computation of <em>R<sub>i</sub></em> values and can be accomplished by utilizing the Delila package (<a href="https://alum.mit.edu/www/toms/delila/delilaprograms.html">https://alum.mit.edu/www/toms/delila/delilaprograms.html</a>).</p> <p><strong>Section 5. Ri and intersite distance - histograms</strong></p> <p>Two sets of histograms present <em>R<sub>i</sub></em> distribution and intersite distance distribution across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). </p> <p>File naming convention for human plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Human-[DRIPc]-AllChrs-RBP[-RiThreshold].png</li> <li>e.g. IntersiteDistances500-Human-AllChrs-hnRNPA1-4.6-bits.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Strand-RBP-Virus[.InfluenzaSegment][-RiThreshold].png</li> <li>e.g. IntersideDistances1000-PosStrandOnly-SRSF1-top50000sitesReplicate1-HIV-1-Strain-B.png</li> </ul> <p>Intersite distance thresholds of 500 or 1000 were assigned for all intersite distance histograms. Any distances above the corresponding threshold were excluded from the plot. Plots presenting <em>R<sub>i</sub></em> distributions contain a dashed line indicating <em>R<sub>sequence</sub></em> if it is visible within the scope of the plot.</p> <p><strong>Section 6. Perl Scripts and Descriptions</strong></p> <p>This archive contains all Perl scripts discussed in this archive's associated manuscript and a document file which describes them ("Perl-Script-Descriptions-Page.docx"). The programs and their general functions are as follows:</p> <p>“ClusterToDRIPseqAnalysisProgram.pl” – reports which information-dense clusters are located within DRIPc- and/or DRIP-seq intervals (individually and by gene)</p> <p>“ClusterToDRIPseqAnalysisProgram.GeneDensityFinder.pl” – uses the output from script “ClusterToDRIPseqAnalysisProgram.pl” to determine the number and the density of information-dense clusters within a gene (total clusters within the gene and those within DRIPc-seq intervals)</p> <p>“calculateIntersiteDistance.pl” – determines the distance between all binding sites in the same gene from a list of genomic coordinates</p> <p>“removeOutliersHigherThanN.pl” – discards intersite distances computed by script “calculateIntersiteDistance.pl” that are greater than a specified threshold</p> <p>“getStatisticsOnCol.pl” – calculates the count, geometric mean, median, arithmetic mean, and standard deviation of values from the output of script “removeOutliersHigherThanN.pl”</p> <p>“ScanDataSummaryProgram.pl” – determines the number of binding sites (above a specified <em>R<sub>i</sub></em> threshold) found within known genes (the program also reports the total expression of those genes using external A549 and pneumocyte expression datasets) from binding site coordinate data</p> <p>“TotalBindingSitePerCellCalculator.pl” – estimates the number of binding sites expressed in a single A549 or pneumocyte cell at any given time.</p>
Roger Powell and the Development of the Non-Adhesive Binding
<p>This is the recording and transcript of a lecture given by Anthony Cains in 1988 at the Fifth Anniversary Conference of the Parker Library Conservation Project. A publication based upon the conference paper, supplemented with new material and updated, was released in 1994:</p> <p>Cains, Anthony G. 1994. ‘Roger Powell and His Early Irish Manuscripts in Dublin’. In <em>Conservation and Preservation in Small Libraries</em>, edited by Nicholas Hadgraft and Katherine Swift, 151–56. Cambridge: Parker Library Publications.</p>
Paramecium Polycomb Repressive Complex 2 physically interacts with the small RNA binding PIWI protein to repress transposable elements
<p>Polycomb Repressive Complex 2 (PRC2) maintains transcriptionally silent genes in a repressed state via deposition of histone H3 K27 trimethyl (me3) marks. PRC2 has also been implicated in silencing transposable elements (TEs), yet how PRC2 is targeted to TEs remains unclear. To address this question, we identified proteins that physically interact with the <em>Paramecium</em> Enhancer-of-zeste Ezl1 enzyme, which catalyzes H3K9me3 and H3K27me3 deposition at TEs. We show that the <em>Paramecium</em> PRC2 core complex comprises four subunits, each required <em>in vivo</em> for catalytic activity. We also identify PRC2 cofactors, including the RNA interference (RNAi) effector Ptiwi09, which are necessary to target H3K9me3 and H3K27me3 to TEs. We find that the physical interaction between PRC2 and the RNAi pathway is mediated by a RING finger protein and that small RNA recruitment of PRC2 to TEs is analogous to the small RNA recruitment of H3K9 methylation SU(VAR)3-9 enzymes.</p>
Phase separation of hnRNP A1 upon specific RNA-binding observed by magnetic resonance
<p>Experimental data, <a href="https://mmmx.info">MMMx</a> restraint and ensemble analysis files (.mcx), restraint data, raw ensembles, and ensemble lists with populations (.ens) pertaining to the manuscript "Phase separation of hnRNP A1 upon specific RNA-binding observed by magnetic resonance" <a href="https://www.biorxiv.org/content/10.1101/2022.03.21.485092v1">available at bioRxiv</a> and submitted to a peer-reviewed journal.</p>
Neutron spin echo and intramolecular FRET and DEER-EPR measurements on hGBP1 (human guanylate binding protein 1)
<p>Neutron spin echo (NSE), double electron–electron resonance (<em>DEER</em>) <em>EPR</em>, ensemble time-correlated single photon counting (eTCSPC) fluorescence, and single-molecule detection (SMD) fluorescence spectroscopy data of the human guanylate binding protein 1 (hGBP1).</p> <p>CSH prepared samples for smFRET and performed protein activity assays. TV prepared sampled for EPR measurements. TOP, CSH, and AV performed the smFRET measurements under the supervision of CAMS. TOP analyzed the smFRET measurements. JPK performed and analyzed the EPR measurements.</p>
Simulation Parameters for Two Cooperative Binding Sites Sensitize PI(4,5)P2 Recognition by the Tubby Domain
<p>Dataset to perform the coarse-grained MD simulations presented in "Two cooperative binding sites sensitize PI(4,5)P2 recognition by the tubby domain". The dataset includes protein structures and GROMACS simulation files such as mdp, itp, gro, and index files for the tubby domain as well as PLC-delta1 PH domain.</p>
Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding
<p>TF binding models built by JAMS (https://github.com/csglab/JAMS), ChIP-seq peak files (from ENCODE, Najafabadi et al. 2015, Schmitges et al. 2016, and Imbeault et al. 2017; called by MACS 1.4v), ChIP-seq pulldown and control tags from said peaks, input data for JAMS, and RCADE2 motifs for C2H2 zinc finger proteins. </p>
Low dose rate radiation induced secretion of TGF-β3 together with an activator in small extracellular vesicles modifies low dose hyper-radiosensitivity through ALK1 binding
<p>This is a collection of results from all clonogenic assays on T-47D cells used in the manuscript "Low dose rate radiation induced secretion of TGF-β3 together with an activator in small extracellular vesicles modifies low dose hyper-radiosensitivity through ALK1 binding". </p> <p>T-47D cells were subjected to various pretreatments: low dose rate priming (0.1-0.3 Gy/h for 1 hour), small extracellular vesicles from irradiated or control cells, irradiated or control cell conditioned medium, MMP/ADAM inhibitor TAPI-2, recombinant TGF-B3, inhibitors of ALK1, ALK2, ALK5 or TGF-BRII, iNOS inhibitor 1400W, recombinant FKBP4, recombinant MMP14 and combinations of these. All pretreatments except low dose rate priming was administered for 24 hours. </p> <p>After pretreatments, cells were seeded to colonies and irradiated with 220 kV x-rays at a dose rate of 22.5 Gy/h or gamma rays from a Co-60 source at a dose rate of 20-25 Gy/h. </p> <p>Colonies were cultured for 2-3 weeks before fixation and manual counting. </p>
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