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1,410 results for “Protein Binding”
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: 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>
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>
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>
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>
Data for UV Plasmon-Enhanced Chiroptical Spectroscopy of Membrane-Binding Proteins, June 2024
<p>Extinction spectra of arrays of aluminum nanoparticles with diameters between 40 - 100 nm.</p> <p>Circular dichroism spectra of Tol-BINAP films on Al nanoparticle arrays before and after annealing of the films.</p> <p>Electromagnetic simulations of phase, electric (Eenh) field and magnetic (Henh) field enhancements as well as optical chirality density (Cenh) enhancement around flat aluminum hexagonal pyramid at specified wavelength. The simulations were performed with FDTD using Ansys Lumerical.</p>
A molecular dynamics study of adenylyl cyclase: the impact of ATP and G-protein binding
<p>Adenylyl cyclases (ACs) catalyze the biosynthesis of cyclic adenosine monophosphate (cAMP) from adenosine triphosphate (ATP) and play an important role in many signal transduction pathways. The enzymatic activity of ACs is carefully controlled by a variety of molecules, including G-protein subunits that can both stimulate and inhibit cAMP production. Using homology models developed from existing structural data, we have carried out all-atom, microsecond-scale molecular dynamics simulations on the AC5 isoform of adenylyl cyclase and on its complexes with ATP and with the stimulatory G-protein subunit Gsα. The results show that both ATP and Gsα binding have significant effects on the structure and flexibility of adenylyl cyclase. New data on ATP bound to AC5 in the absence of Gsα notably help to explain how Gsα binding enhances enzyme activity and could aid product release. Simulations also suggest a possible coupling between ATP binding and interactions with the inhibitory G-protein subunit Gαi.</p> <p>All-atom molecular dynamics simulations were performed with the GROMACS 5 package. The simulations were carried out in an NTP ensemble at a temperature of 310 K and a pressure of 1 bar using a Bussi velocity-rescaling thermostat (t<sub>T</sub> = 1 ps) and a Parrinello-Rahman barostat (t<sub>P</sub> = 1 ps). We provide the atomistic trajectories of the following 6 systems after 400 ns of equilibration:</p> <ul> <li>AC5</li> <li>AC5+ATP</li> <li>AC5+Gsα</li> <li>AC5+ATP+Gsα</li> <li>AC5+FOK</li> <li>AC5+ATP+FOK</li> </ul> <p>In each trajectory, the frames are saved each 20 ps.</p>
Predictive modeling of moonlighting DNA-binding proteins
<p>This repository contains the codes used for the prediction of moonlighting proteins in the paper "Predictive modeling of moonlighting DNA binding proteins".</p> <p>The repository is organized as the following:</p> <p>1. The DNA binding protein identifiers and their features that were used to train the models for the prediction of DNA binding Moonlighting proteins.</p> <p>2. Five feature sets were used to create Catboost models that make predictions. The source code for generating predictions based on all the features and predictions based on particular features is supplied. In addition, the source code for generating maximum and average ensemble predictions has been made available. A detailed explanation is given in README file.</p>
DATASET: Protein Binding Leads to Reduced Stability and Solvated Disorder in the Polystyrene Nanoparticle Corona
<p>This dataset contains the DLS, CD, fluorescence, ITC, TEM, and ANS raw data used for the manuscript.</p>
Data for the article: "Molecular Modelling Reveals Eight Novel Druggable Binding Sites in SARS-CoV-2's Spike Protein" by Ilke Ugur and Antoine Marion
<p>This upload contains data related to the article<br> published as a preprint on ChemRxiv with DOI<br> https://doi.org/10.26434/chemrxiv.13292768</p> <p>"Molecular Modelling Reveals Eight Novel Druggable Binding Sites in SARS-CoV-2's Spike Protein"<br> by Ilke Ugur and Antoine Marion (2020)<br> Department of Chemistry, Middle East Technical University, Ankara, Turkey.</p> <p>For further information, please contact:<br> ilkeugur@metu.edu.tr ; amarion@metu.edu.tr</p> <p>The manuscript is currently under peer-review.</p> <p>Content:</p> <p>Library of molecules derived from DrugBank v 5.1.5:<br> - DrugBank_2020_5.1.5/ # All necessary files for the docking and refinement of the library of molecules.<br> -- DB_5.1.5_pH7.4_pdbqt/ ## PDBQT readily usable for docking with AutoDock Vina.<br> -- DB_5.1.5_pH7.4_mol2amber/ ## mol2 files containing assigned GAFF atom types and Gasteiger atomic charges.<br> -- DB_5.1.5_pH7.4_frcmod/ ## frcmod files containing missing molecular mechanics parameters<br> -- dbID_name.dat ## DrugBank ID to generic name dictionary</p> <p>Note: The files were prepared automatically via a series of operations handling openbabel and antechamber.<br> The protonation state of ionizable groups as well as Gasteiger atomic charges were assigned by openbabel for a pH of 7.4<br> mol2 and frcmod files can be used readily via the tleap module of AmberTools to produce topology files.</p> <p><br> Receptor structures:<br> - receptors/ # PDB files for the four structures of the spike protein considered in this work<br> -- CS00ns.pdb ## Closed state after the remodelling of missing loops (PDB ID 6vxx)<br> -- OS00ns.pdb ## Open state after the remodelling of missing loops (PDB ID 6vyb)<br> -- CS25ns.pdb ## Closed state after 25 ns of molecular dynamics in explicit water<br> -- OS25ns.pdb ## Open state after 25 ns of molecular dynamics in explicit water</p> <p>Note: All structures are aligned to CS00ns.pdb and can be converted to pdbqt for docking with AutoDock Vina</p> <p><br> Docking grid centers:<br> - dockingCenters/ # XYZ files containing the coordinates of each docking grid center considered in this work</p> <p>Note: The coordinates are given in the same frame as that of the four structures of the receptor.</p> <p><br> Binding sites:<br> - bindingSites/ # XYZ files with the coordinates of the representative atomic centres<br> # of each binding site identified in this work (A-H).</p> <p>Note: These files can be used to get a clearer picture of the binding sites within the structures<br> of the spike protein shared in the receptors directory.</p> <p><br> Final modelling results:<br> - allData.txt # data for all molecules in the set (approved and investigational)<br> - appData.txt # data for approved molecules only<br> - data.xlsx # data for all molecules in the set (approved and investigational)<br> # as a formatted excel spreadsheet</p> <p>Note: The columns are delimited with semi-colons ";".<br> The files contain the results for the best pose of all approved molecules for which<br> molecular mechanics-based geometry optimization succeeded, regardless of their score.<br> For other molecules, the result of their best pose is reported only for those complexes<br> having MM interaction energy lower or equal to -22.00 kcal/mol.</p> <p><br> Visualization:<br> - bs.pse # pymol session representing the binding sites within the<br> # closed state structure of the spike protein (CS00ns)<br> - pt.pse # pymol session representing the docking grid centres within<br> # closed statestructure of the spike protein (CS00ns)</p> <p>Note: the PSE files should be compatible with version 7.0 of pymol and later</p>
Double-stranded RNA structural elements holding the key to translational regulation in cancer: the case of editing in RNA Binding Motif Protein 8A
<p>Raw data supporting the manuscript</p> <p>Abukar, A.;Wipplinger, M.;<br> Hariharan, A.; Sun, S.; Ronner, M.;<br> Sculco, M.; Okonska, A.;<br> Kresoja-Rakic, J.; Rehrauer, H.; Qi, W.;<br> et al. Double-Stranded RNA<br> Structural Elements Holding the Key<br> to Translational Regulation in Cancer:<br> The Case of Editing in RNA-Binding<br> Motif Protein 8A. Cells 2021, 10, 3543.<br> https://doi.org/10.3390/<br> cells10123543</p>
Predictions of the SARS-CoV-2 B.1.1.529 Variant Spike Protein Receptor Binding Domain Structure and Neutralizing Antibody Interactions
<p>Using AlphaFold2 and HADDOCK, we have generated a predicted structure for the SARS-CoV-2 B.1.1.529 variant's Spike receptor binding domain and then predicted the binding interaction with neutralizing antibodies. This was performed to understand the potential structural changes in the receptor binding domain of B.1.1.529 and how this may affect vaccine efficacy through antibody interaction.</p>
Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?
<p>Datasets related to publication: </p> <p>Shevchenko E, Pantsar T: Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?. <em><em>Biochem Soc Trans</em></em> 28 February 2022; 50 (1): 633–648</p> <p>https://doi.org/10.1042/bst20210837</p> <p> </p> <p> </p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (1/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>Cneg</td> <td>RNASEQ-AVF1</td> <td>RNASEQ-AVF1_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF1_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF2</td> <td>RNASEQ-AVF2_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF2_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF3</td> <td>RNASEQ-AVF3_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF3_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF4</td> <td>RNASEQ-AVF4_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF4_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF5</td> <td>RNASEQ-AVF5_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF5_S5_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA A</td> <td>RNASEQ-AVF6</td> <td>RNASEQ-AVF6_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF6_S6_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA B</td> <td>RNASEQ-AVF7</td> <td>RNASEQ-AVF7_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF7_S7_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA C</td> <td>RNASEQ-AVF8</td> <td>RNASEQ-AVF8_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF8_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (2/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>TDP-43 siRNA D</td> <td>RNASEQ-AVF9</td> <td>RNASEQ-AVF9_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF9_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF10</td> <td>RNASEQ-AVF10_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF10_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF11</td> <td>RNASEQ-AVF11_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF11_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF12</td> <td>RNASEQ-AVF12_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF12_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF13</td> <td>RNASEQ-AVF13_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF13_S5_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF14</td> <td>RNASEQ-AVF14_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF14_S6_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos B+C</td> <td>RNASEQ-AVF15</td> <td>RNASEQ-AVF15_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF15_S7_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos A+B+C</td> <td>RNASEQ-AVF16</td> <td>RNASEQ-AVF16_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF16_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
Simulation of Receptor Binding Domain of SARS-CoV-2 spike protein (WT and variants) in complex with neutralizing antibodies.
<p>This repository contains the molecular dynamics trajectories of the SARS-CoV-2 Spike RBD bound to BD23 and B38 monoclonal antibodies. The simulations for the RBD only systems are also provided. The trajectories are available for the WT spike protein as well as for four different variants (alpha, beta, kappa and delta). The simulations of the RBD only system are propagated for 300 ns and for the RBD-Antibody complex for 500 ns. The trajectories are saved at 100 ps interval. The Steered MD simulation trajectories (WT_RBD_B38_SMD_1.dcd etc.) and collective variables files are also included (WT_RBD_B38_SMD_1.colvars.traj etc.). There are 5 SMD trajectories for each RBD antibody pair. The details of the simulation can be obtained from the preprint: https://doi.org/10.1101/2021.08.13.456317</p>
Screening routine for integrative dynamic structural biology using SAXS and intramolecular FRET and DEER-EPR on hGBP1 (human guanalyte binding protein 1)
<p>Initial and selected ensemble for major and minor species of the human guanalyte binding protein 1 with scripts for the reading routine to combine and analyse jointly SAXS, EPR and FRET data.</p>
PSnpBind: A database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow
<p>A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is freely available at <a href="https://psnpbind.org">https://psnpbind.org</a>.<strong> </strong>The source code of the tools used in constructing PSnpBind is available on <a href="https://github.com/ammar257ammar/PSnpBind-Build">GitHub</a>.</p>
Depletion of cap-binding protein eIF4E dysregulates amino acid metabolic gene expression
<p><span>Protein synthesis is </span><span>metabolically costly </span><span>and</span><span> must be tightly coordinated with </span><span>changing </span><span>cellular needs and nutrient availability. T</span><span>he cap-binding protein eIF4E</span><span> makes </span><span>the earliest contact between mRNAs and the translation machinery</span><span>, offering a key regulatory nexus</span><span>. </span><span>W</span><span>e acute</span><span>ly</span><span> deplet</span><span>ed</span> <span>this essential protein </span><span>and </span><span>found </span><span>s</span><span>urprisingly modest effects on cell growth and </span><span>recovery of </span><span>protein synthesis.</span><span> Paradoxically, </span><span>impaired protein biosynthesis upregulated </span><span>genes involved in catabolism of aromatic amino acids</span><span>simultaneously with the </span><span>induction of the </span><span>amino acid</span><span> biosynthetic regulon</span> <span>driven</span> <span>by </span><span>the integrated stress response factor</span> <span>GCN4</span><span>. </span><span>W</span><span>e</span><span> further</span><span> identified translation</span><span>al </span><span>control</span><span> of </span><span>PCL5</span><span>, </span><span>a negative regulator of Gcn4, that provides a consistent protein-to-mRNA ratio under varied translation environments. </span><span>This</span> <span>regulation </span><span>depende</span><span>d in part</span><span> on a uniquely long poly-(A) tract in the </span><span>PCL5</span><span> 5´ UTR and poly-(A) binding protein. Collectively, these results highlight</span> <span>how eIF4E connects</span> <span>protein synthesis </span><span>to</span> <span>metabolic gene regulation</span><span>,</span><span>uncover</span><span>ing</span><span> new mechanisms control</span><span>ling</span> <span>translation</span><span> during environmental challenges.</span></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.