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5,942 results for “binding”
data for "A disordered encounter complex is central to the yeast Abp1p SH3 domain binding pathway"
<p>Protein-protein interactions are involved in a wide range of cellular processes. These interactions often involve intrinsically disordered proteins (IDPs) and protein binding domains. However, the details of IDP binding pathways are hard to characterize using experimental approaches, which can rarely capture intermediate states present at low populations. SH3 domains are common protein interaction domains that typically bind proline-rich disordered segments and are involved in cell signaling, regulation, and assembly. We hypothesized, given the flexibility of SH3 binding peptides, that their binding pathways include multiple steps important for function. Molecular dynamics simulations were used to characterize the steps of binding between the yeast Abp1p SH3 domain (AbpSH3) and a proline-rich IDP, ArkA. Before binding, the N-terminal segment 1 of ArkA is pre-structured and adopts a polyproline II helix, while segment 2 of ArkA (C-terminal) adopts a 310 helix, but is far less structured than segment 1. As segment 2 interacts with AbpSH3, it becomes more structured, but retains flexibility even in the fully engaged state. Binding simulations reveal that ArkA enters a flexible encounter complex before forming the fully engaged bound complex. In the encounter complex, transient nonspecific hydrophobic and long- range electrostatic contacts form between ArkA and the binding surface of SH3. The encounter complex ensemble includes conformations with segment 1 in both the forward and reverse orientation, suggesting that segment 2 may play a role in stabilizing the correct binding orientation. While the encounter complex forms quickly, the slow step of binding is the transition from the disordered encounter ensemble to the fully engaged state. In this transition, ArkA makes specific contacts with AbpSH3 and buries more hydrophobic surface. Simulating the binding between ApbSH3 and ArkA provides insight into the role of encounter complex intermediates and nonnative hydrophobic interactions for other SH3 domains and IDPs in general.</p>
Drug molecules binding to Covid-19 main protease
<p>A comparative look at where drug molecules bind to the main protease of Covid-19 depicted by interactive raytracing in the UnityMol software. The visualization is inspired by the animation of small molecules in 92 protein databank structures (<a href="https://www.rbvi.ucsf.edu/chimerax/data/sars-protease-may2020/">https://www.rbvi.ucsf.edu/chimerax/data/sars-protease-may2020/</a>) prepared by the ChimeraX team.</p>
Dataset for identification of peptidomimetics and FDA approved drugs binding to novel allosteric pocket of the IRE1 RNase domain.
<p>Input files, protein-peptide and ligand docking datasets, and simulation trajectories, compressed in "rar" format. All calculations performed using the Schrödinger 2020-2 / 2020-3 software (modules Glide, Phase, Desmond). </p> <p>SI includes folders:</p> <p>1- "Peptide" folder contains the best peptide "Docking" complexes and "Pharmacophore" models </p> <p>2-"Quercitrin" folder contains the molecular "Docking", "MMGBSA" calculations, and "MD" simulation</p> <p>3-"Pemetrexed" folder contains the molecular "Docking", "MMGBSA" calculations, and "MD" simulation</p> <p> </p>
Kymograph of Cas9 binding to λ-DNA under tension
<p>Kymograph measurement of force-stretched λ-DNA in the presence of dCas9 with Cy3-labelled crRNA between 5 and 50 pN.</p>
Data from: Populus euphratica WRKY1 binds the promoter of PeHA1 to enhance gene expression and salt tolerance
<p>Plasma membrane proton pumps play a crucial role in maintaining ionic homeostasis in salt-resistant <i><span>Populus euphratica</span></i> under saline conditions<i><span>. </span></i>High levels of NaCl (200 mM) induced <i><span>PeHA1</span></i> expression in <i><span>P. euphratica</span></i> roots and leaves. We isolated a 2022-bp promoter fragment upstream of the translational start of <i><span>PeHA1 </span></i>from<i><span> P. euphratica</span></i>. The promoter-reporter construct <i><span>PeHA1-pro</span></i>::<i><span>GUS</span></i> was transferred to tobacco plants, demonstrating that β-glucuronidase activities increased in root, leaf, and stem tissues under salt stress. DNA affinity purification sequencing revealed that PeWRKY1 protein targeted the<i><span> PeHA1</span></i> gene. We assessed the salt-induced transcriptional response of PeWRKY1 and its interaction with <i><span>PeHA1</span></i> in <i><span>P. euphratica</span></i>. PeWRKY1 binding to the <i><span>PeHA1 </span></i>W-box in promoter region was verified by a yeast one-hybrid assay, electrophoretic mobility shift assay, luciferase reporter assay, and virus-induced gene silencing. Transgenic tobacco plants overexpressing <i><span>PeWRKY1</span></i> had improved expression of <i><span>NtHA4, </span></i>which has a cis-acting W-box in the regulatory region, and H<sup><span>+</span></sup> pumping activity in both in vivo and in vitro assays. We conclude that salt stress upregulated <i><span>PeHA1</span></i> transcription due to the binding of PeWRKY1 to the W-box in the promoter region of <i><span>Pe</span></i><i><span>HA1</span></i>. Thus, we conclude that enhanced H<sup><span>+</span></sup> pumping activity enabled salt-stressed plants to retain Na<sup><span>+</span></sup> homeostasis.</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>
All-atom 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 100 ns production run was performed for the simulation.</p>
Gaussian polarizable-ion tight binding (Supplemental Data)
<p><strong>This is a collection of input and output files for the publication below.</strong></p> <p>Abstract:</p> <p>To interpret Ultrafast Dynamics experiments on large molecules, computer simulation is required due to the complex response to the laser field. We present a method capable of efficiently computing the static electronic response of large systems to external electric fields. This is achieved by extending the density-functional tight binding method to include larger basis sets and by multipole expansion of the charge density into electrostatically interacting Gaussian distributions. Polarizabilities for a range of hydrocarbon molecules are computed for a multipole expansion up to quadrupole order, giving excellent agreement with experimental values, with average errors similar to those from density functional theory, but at a small fraction of the cost. We apply the model in conjunction with the polarizable-point-dipoles model to estimate the internal fields in amorphous Poly(3-hexylthiophene-2,5-diyl).</p>
The organophosphate pesticide methamidophos opens the blood-testis barrier and covalently binds to ZO-2 in mice
<p>We studied biological effects and post-translational modifications of proteins after treating mice with the pesticide methamidophos.</p> <p>This data set provides evidence for the modification of ZO-2, indicating that the blood-testis barrier in mouse was crossed.</p>
Molecular dynamic trajectory of magnesium binding wild type for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for wild type of the beta subunit binding magnesium:</p> <p>50ns trajectory (ATPsynth_woh2o_Mg_wt.dcd) and corresponding psf file (ATPsynth_mg_wt.psf)</p>
Molecular dynamic trajectory of calcium binding T163S mutant for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for T163S mutants of the beta subunit binding calcium:</p> <p>50ns trajectory (ATPsynth_woh2o_Ca_mut.dcd) and corresponding psf file (ATPsynth_ca_mut.psf)</p>
Molecular dynamic trajectory of calcium binding wild type for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for wild type of the beta subunit binding calcium:</p> <p>50ns trajectory (ATPsynth_woh2o_Ca_wt.dcd) and corresponding psf file (ATPsynth_ca_wt.psf)</p> <p> </p>
Molecular dynamic trajectory of magnesium binding T163S mutant for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for T163S mutants of the beta subunit binding magnesium:</p> <p>50ns trajectory (ATPsynth_woh2o_Mg_mut.dcd) and corresponding psf file (ATPsynth_mg_mut.psf)</p> <p> </p>
Characterization and effect of biomimetic surfaces based on the topography of a self-cleaning leaf on bacterial binding
<p>Four self-cleaning leaves (Tenderheart, Cauliflower, White cabbage, and Leek) and the corresponding biomimetic surfaces were analyzed for their properties (water contact angle, surface hydrophobicity and roughness). The antifouling behavior was assessed by bacterial attachment, adhesion, and retention assays.</p>
Elucidating molecular mechanisms of protoxin-2 state-specific binding to the human NaV1.7 channel
<p>Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-2 (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD and the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.</p>
DoubleChEC program to identify transcription factor binding sites from mapped ChEC-seq data
<p>ChIP-seq (chromatin immunoprecipitation followed by sequencing) is commonly used to identify genome-wide protein-DNA interactions. However, ChIP-seq often gives a low yield, which is not ideal for quantitative outcomes. An alternative method to ChIP-seq is ChEC-seq (Chromatin endogenous cleavage with high-throughput sequencing). In this method, the endogenous TF (transcription factor) of interest is fused with MNase (micrococcal nuclease) that non-specifically cleaves DNA near binding sites. Compared to the <a href="https://www.nature.com/articles/ncomms9733" rel="nofollow">original ChEC-seq method</a>, the <a href="https://sites.northwestern.edu/bricknerlab/" rel="nofollow">modified version</a> requires far less amplification. Since <a href="https://github.com/macs3-project/MACS/tree/master#introduction">MACS3</a> failed to identify peaks in data generated from the modified ChEC-seq method, a new peak finder has been developed specifically for it.</p> <p>There are three functions in the <em><code>peak_finder/</code></em>. <code>callpeaks()</code> is used to identify peaks from BAM files. <code>goanalysis()</code> is used to make GO (Gene Ontology) term plots from peaks. <code>bedtomeme()</code> is a wrapper function to perform <a href="https://meme-suite.org/meme/tools/meme" rel="nofollow">MEME analysis</a> in R <strong>after <a href="https://meme-suite.org/meme/doc/download.html" rel="nofollow">MEME Suite</a> is installed locally</strong>.</p>
Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?
<p>Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?:</p> <p><br>This data set contains all the experimental raw data, analysis and source files for the final figures reported in the manuscript: "Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?". It is divided into five (1-5) zipped folders, named as the technique used to obtain the data. Each of them, where applicable, consists of three different subfolders (raw data, analysed data, final graph). Read below for more details. </p> <p>1) ConfocalMicroscopy</p> <p> 1a) Raw_Data: the raw images are reported as .dat and .tif formats, divided into folders (according to date first yymmdd, and within the same day according to composition). Each folder contains a .txt file reporting the experimental details </p> <p> 1b) GUVs_Statistics<br> - GUVs_Statistics.txt explains how we generated the bar plot shown in Fig. 1E</p> <p> 1c) Final_Graph<br> - Figure_1B_1D.png is the figure representing figure 1B and 1D<br> - Figure1E_%ofGUVswithCaMAdsorbptions.csv is the source file x-y of the bar plot shown in figure 1E (% of GUVs which showed adsorption of CaM over the total amount of measured GUVs) <br> - Where_To_Find_Representative_Images.txt states the folders where the raw images chosen for figure 1 can be found </p> <p>2) FCS<br> <br> 2a) Raw_Data: <br> - 1_points: .ptu files <br> - 2_points: .ht3 files <br> - Raw_Data_Description.docx which compositions and conditions correspond to which point in the two data sets<br> <br> 2b) Final_Graphs:<br> - Figure_2A.xlsx contains the x-y source file for figure 2A</p> <p> 2c) Analysis: <br> - FCS_Fits.xlsx outcome of the global fitting procedure described in the .docx below (each group of points represents a certain composition and calcium concentration, read the Raw_Data_Description.docx in the FCS > Raw_Data)<br> - Notes_for_FCS_Analysis.docx contains a brief description of the analysis of the autocorrelation curves</p> <p>3) GPLaurdan<br> <br> 3a) Raw Data: all the spectra are stored in folders named by date (yymmdd_lipidcomposition_Laurdan) and are in both .FS and .txt formats </p> <p> 3b) GP calculations: contains all the .xlsx files calculating the GP values from the raw emission and excitation spectra</p> <p> 3c) Final_Graphs<br> - Data_Processing_For_Fig_2D.csv contains the data processing from the GP values calculated from the spectra to the DeltaGP (GP with- GP without CaM) reported in fig. 2D<br> - Figure_2C_2D.xlsx contains the x-y source file for the figure 2C and 2D</p> <p>4) LiveCellsImaging </p> <p> 3a) Intensity_Protrusions_vs_Cell_Body: <br> - contains all the .xlsx files calculating the intensity of the various images. File renamed by date (yymmdd) <br> - All data in all excel sheets gathered in another Excel file to create a final graph </p> <p> 3b) Final_Graphs<br> - Figure_S2B.xlsx contains the x-y source file for the figure S2B</p> <p>5) LiveCellImaging_Raw_Data: it contains some of the images, which are given in .tif. They are divided by date (yymmdd) and each contains subfolders renamed by sample name, concentration of ionomycin. Within the subfolders, the images are divided into folders distinguishing the data acquired before and after the ionomycin treatment and the incubation time.</p> <p> </p> <p>6) 211124_BioCev_Imaging_1 folder has the .jpg files of the time laps, these are shown in fig 1A and S2.</p> <p>7) 211124_BioCev_Imaging_2 and 8) 211124_BioCev_Imaging_3 contain the images of HeLa cells expressing EGFP-CaM after treatment with ionomycin 200 nM (A1) and 1 uM (A2), respectively. </p> <p><br>9) SPR</p> <p> 9a) Raw Data: <br> - SPR_Raw_Data.xlsx x/y exported sensorgrams <br> - the .jpg files of the software are also reported and named by lipid composition</p> <p> 9b) Final_Graph: <br> - Fig.2B.xlsx contains the x-y source file for the figure 2B</p> <p> 9c) Analysis<br> - SPR_Analysis.xlsx: excel file containing step-by-step (sheet by sheet) how we processed the raw data to obtain the final figure (details explained in the .docx below)<br> - Analysis of SPR data_notes.docx: read me for detailed explanation</p>
Phenoxytacrine derivatives: Low-toxicity neuroprotectants exerting affinity to ifenprodil-binding site and cholinesterase inhibition
<p><a title="Learn more about Tacrine from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemistry/tacrine">Tacrine</a> (THA), a long withdrawn drug, is still a popular scaffold used in medicinal <a title="Learn more about chemistry from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemistry/chemistry">chemistry</a>, mainly for its good reactivity and multi-targeted effect. However, THA-associated hepatotoxicity is still an issue and must be considered in <a title="Learn more about drug discovery from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/drug-discovery">drug discovery</a> based on the THA scaffold. Following our previously identified hit compound 7-phenoxytacrine (7-PhO-THA), we systematically explored the chemical space with 30 novel derivatives, with a focus on low hepatotoxicity, <a title="Learn more about anticholinesterase from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/cholinesterase-inhibitor">anticholinesterase</a> action, and antagonism at the GluN1/GluN2B subtype of the <a title="Learn more about NMDA receptor from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/n-methyl-dextro-aspartic-acid-receptor">NMDA receptor</a>. Applying the down-selection process based on <em>in vitro</em> and <em>in vivo</em> <a title="Learn more about pharmacokinetic from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/pharmacokinetics">pharmacokinetic</a> data, two candidates, <strong>I-52</strong> and <strong>II-52,</strong> selective GluN1/GluN2B inhibitors thanks to the interaction with the ifenprodil-binding site, have entered <em>in vivo</em> <a title="Learn more about pharmacodynamic from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/pharmacodynamics">pharmacodynamic</a> studies. Finally, compound <strong>I-52,</strong> showing only minor affinity to <a title="Learn more about AChE from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/acetylcholinesterase">AChE</a>, was identified as a lead candidate with favorable behavioral and <a title="Learn more about neuroprotective from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/neuroprotective-agent">neuroprotective</a> effects using open-field and <a title="Learn more about prepulse inhibition from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/prepulse-inhibition">prepulse inhibition</a> tests, along with scopolamine-based behavioral and NMDA-induced hippocampal lesion models. Our data show that compound <strong>I-52</strong> exhibits low toxicity often associated with <a title="Learn more about NMDA receptor from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/nmda-receptor">NMDA receptor</a> ligands, and low hepatotoxicity, often related to THA-based compounds.</p>
Distinct binding hotspots for natural and synthetic agonists of FFA4 from in silico approaches
<p>Compressed trajectories files of natural and synthetic ligands (TUG-891, alpha-Linolenic acid, Linoleic acid and Oleic acid) as supporting information for a research article in Molecular Informatics exploring binding hotspots of FFA4.</p> <p>Each compressed trajectories comprise of a pdb file (xx_Rx.pdb) and its associated trajectory file (xx_Rx_TRJ.trr ) to be read using vmd.</p> <p> </p> <p> </p>
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>
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.