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5,942 results for “Binding”
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>
Band structure of KTaO3 two dimensional electron gas: ARPES data and tight binding fits
<p>The dataset contains the angle resolved photoemission spectroscopy measurements of the band structure of the two dimensional electron gas generated at the KTaO3/Al interface. Both dispersion and constant energy maps near the Fermi level are provided.</p> <p>The experimental data are complemented with tight binding fits (eight bands).</p>
ATP binding facilitates target search of SWR1 chromatin remodeler by promoting one-dimensional diffusion on DNA
<p>One-dimensional (1D) target search is a well-characterized phenomenon for many DNA-binding proteins but is poorly understood for chromatin remodelers. Herein, we characterize the 1D scanning properties of SWR1, a conserved yeast chromatin remodeler that performs histone exchange on +1 nucleosomes adjacent to a nucleosome-depleted region (NDR) at gene promoters. We demonstrate that SWR1 has a kinetic binding preference for DNA of NDR length as opposed to gene-body linker length DNA. Using single and dual color single-particle tracking on DNA stretched with optical tweezers, we directly observe SWR1 diffusion on DNA. We found that various factors impact SWR1 scanning, including ATP which promotes diffusion through nucleotide binding rather than ATP hydrolysis. A DNA-binding subunit, Swc2, plays an important role in the overall diffusive behavior of the complex, as the subunit in isolation retains similar, although faster, scanning properties as the whole remodeler. ATP-bound SWR1 slides until it encounters a protein roadblock, of which we tested dCas9 and nucleosomes. The median diffusion coefficient, 0.024 μm2/s, in the regime of helical sliding, would mediate rapid encounter of NDR-flanking nucleosomes at length scales found in cellular chromatin.</p>
The structural basis for the self-inhibition of DNA binding by apo-σ70 - smFRET raw data and analyses pipeline
<p>This dataset includes all raw data of nsALEX smFRET measurements of doubly-labeled sigma70 reported in Joron et al. ("The structural basis for the self-inhibition of DNA binding by apo-σ70"), as well as Jupyter Notebooks documenting the analysis pipeline that takes us from the raw data to dual channel burst search and filtered bursts, and to the analyses of within-burst dynamics in the system</p>
NMR titration experiments that study binding of a NIR emitting osmium polypyridyl probe to cMYC and hTel G-quadruplex DNA
<p>1D and 2D NMR spectra of cMYC and hTel G-quadruplex DNA and their complexes with Λ-<strong> </strong>and Δ<strong>-</strong>enantiomers of the osmium polypyridyl probe [Os(TAP)<sub>2</sub>(dppz)]<sup>2+</sup>. Spectra were recorded on a 600 MHz NMR spectrometer with 70 mM KCl, 20 or 25 mM K-phosphate buffer, pH 7, 298 K, in 90% H<sub>2</sub>O and 10% D<sub>2</sub>O at 25 °C.</p>
Fast kinetics data of binding of azide to myoglobin, training data -- MetBio practicals
<p>These data files were acquired during the course of the 3rd <a href="http://frenchbic.cnrs.fr/">FrenchBIC</a> <a href="http://frenchbic.cnrs.fr/2021/01/25/3rd-frenchbic-summer-school-on-methods-for-studying-metals-in-biology/">MetBio summer school</a> taking place in and around Marseille. The data were acquired during the “stopped flow” practicals.</p> <p>The data correspond to the reaction of Myoglobin with various concentrations of azide, triggered using a stopped-flow apparatus and followed by UV/Visible spectroscopy.</p> <p>The concentrations of azide can be deduced from the names of the files and are expressed in millimolar. The concentrations of myoglobin are variable but always much lower than that of azide.</p> <p>The purpose of this dataset is to be used as training data for analysing multiwavelength kinetic data. It will be the subject of a data analysis tutorial using the free software <a href="https://bip.cnrs.fr/groups/bip06/software/">QSoas</a>, to be published <a href="https://vince-debian.blogspot.com/">there</a>.</p> <p>With the exception of the <code>WTV-Azide-0.5mm_3.txt</code>, all the files are in a “matrix” format, in which the first column gives the time and each column after the first corresponds to the absorbances over time of a single wavelength.</p> <p>The <code>WTV-Azide-0.5mm_3.txt</code> uses a different format, in which each line corresponds to a <em>wavelength</em> <em>time</em> <em>absorbance</em> triplet.</p> <p>Useful background reading:</p> <ul> <li> <p>Coletta <em>et al</em>, <strong>1996</strong>, DOI: 10.1111/j.1432-1033.1996.00049.x</p> </li> <li> <p>De Sanctis <em>et al</em>, <strong>2007</strong>, DOI: 10.1529/biophysj.106.098764</p> </li> </ul>
Training data for ChIP-seq data analysis (Galaxy Training Material): Identification of the binding sites of the Estrogen receptor
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes ChIP-seq data from a study published by Ross-Inness et al., 2012 (DOI:10.1038/nature10730) to identify the binding sites of the Estrogen receptor, a transcription factor known to be associated with different types of breast cancer.</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>
Engineering dynamic gates in binding pocket of penicillin G acylase to selectively degrade bacterial signaling molecules
<p>(01-mutants_design.tar.gz) Mutants design:</p> <ol> <li>Input structures of ecPGA from the PDB database (PDB IDs: 1GK9, 1GM7, and 1GM9), processed to resemble wild-type state, repaired by RepairPDB module of FoldX 4</li> <li>Double-point mutants preparation, analysis and filtering: <ol> <li>text files including configuration for FoldX 4</li> <li>inputs and outputs of CAVER 3.02 calculations on FoldX 4 PDB files of ecPGA double-point mutants</li> <li>input configuration file and TransportTools library 0.9.4 calculations outputs generated based on the inputs produced in step 01 and 02 above</li> <li>CSV files containing complete information about FoldX 4 stability prediction and geometrical properties from CAVER 3.02 and TransportTools library version 0.9.4 for ecPGA double-point mutants</li> </ol> </li> <li>Triple-point mutant preparation, analysis and filtering: <ol> <li>text files including configuration for FoldX 4</li> <li>inputs and outputs of CAVER 3.02 calculations on FoldX 4 PDB files of ecPGA triple-point mutants</li> <li>input configuration file and TransportTools library 0.9.4 calculations outputs generated based on the inputs produced in step 01 and 02 above</li> <li>CSV files containing complete information about FoldX 4 stability prediction and geometrical properties from CAVER 3.02 and TransportTools library version 0.9.4 for ecPGA triple-point mutants</li> </ol> </li> </ol> <p>(02-docking.tar.gz) Preparation of protein-ligand complexes using molecular docking for wild-type ecPGA and 6 best designed triple-point mutants with 6 various bacterial signaling molecules:</p> <ol> <li>PDB files of ligand, PDBQT files of the receptor and PDB files of the complexes selected from docking experiment:</li> </ol> <p>Full names of presented protein variants:<br>ecPGA_wt, wild-type Escherichia coli penicillin G acylase<br>LAF, Phe138αLeu & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_22<br>LSF, Phe138αLeu & Met142αSer & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_98<br>MAF, Phe138αMet & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_23<br>MSF, Phe138αMet & Met142αSer & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_99<br>VAF, Phe138αVal & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_30<br>YAF, Phe138αTyr & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_33<br>Full names of presented AHLs:<br>C06, N-hexanoyl-L-homoserine lactone;<br>C06-3O, N-3-oxo-hexanoyl-L-homoserine lactone;<br>C08, N-octanoyl-L-homoserine lactone;<br>C08-3O, N-3-oxo-octanoyl-L-homoserine lactone;<br>C10, N-decanoyl-L-homoserine lactone;<br>C12-3O, N-3-oxo-dodecanoyl-L-homoserine lactone</p> <p>(03-protein_ligand_MDs.tar.gz) Ligand-enzyme complexes molecular dynamics for wild-type ecPGA and 6 best designed triple-point mutants with 6 various bacterial signaling molecules:</p> <ol> <li>Force field parameters in Amber format</li> <li>Input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format</li> <li>Output files generated during post-processing of production runs trajectories in a form of text files generated by cpptraj</li> </ol> <p>(04-free_enzymes_MDs.tar.gz) Free enzymes molecular dynamics of 3 best triple-point ecPGA (VAF, YAF and MSF) mutants prioritized based on protein-ligand molecular dynamics simulations and experimental assays:</p> <ol> <li>Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber format</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format</li> <li>Post-processing analysis of generated trajectories: <ol> <li>Text files with distances, CSV files containing result of PCA and clustering, PNG files with clustered PCA results</li> <li>Inputs and outputs of MDpocket analysis and visualization of the pocket frequency grid as an isomesh</li> <li>CAVER input configuration files in text format, CAVER output data including parsed CSV and text files for visualization of entrance opening time evolution and cavity profiles inspection</li> <li>cpptraj generated text files including RMSD, distances and chi1 angles measurements</li> </ol> </li> </ol> <p>All plots were generated using matplotlib or seaborn Python libraries. Figures containing structural representations were generated using PyMOL 2.0.1.</p> <p>(05-ecPGA_VAF_YAF_MSF_penG_MDs.tar.gz) PenG-enzyme complexes molecular dynamics for wild-type ecPGA and 3 best designed triple-point mutants (VAF, YAF, MSF):</p> <ol> <li>PenG force field parameters in Amber (GAFF) format</li> <li>Input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format and analysis output files generated during post-processing of production runs trajectories in a form of text files</li> <li>Reactive Stabilization Score [RSS] statistics summarized in CSV files</li> </ol>
Data from: Residues neighboring an SH3-binding motif participate in determining affinity and specificity in vivo
<p>In signaling networks, many protein-protein interactions are mediated by modular domains that bind short linear motifs. The motifs' sequences modulate many factors, among them affinity and specificity, or the ability to bind strongly and to bind the appropriate partners. Previous studies have proposed a trade-off between affinity and specificity, suggesting that motifs with high affinity are less capable of differentiating between domains with similar sequences and structures. Using Deep Mutational Scanning to create a mutant library of a well characterized binding motif, and protein complementation assays to measure protein-protein interactions, we tested this trade-off in vivo for the first time. We measured the binding strength and specificity of a library of mutants of a binding motif on the MAP kinase kinase Pbs2, which binds the SH3 domain of the osmosensor protein Sho1 in Saccharomyces cerevisiae. We find that many mutations in the region surrounding the binding motif modify binding strength, but that few mutations have a strong impact on specificity. Moreover, we find no systematic relationship between affinity and specificity as measured in vivo. Interestingly, all Pbs2 mutations which increase affinity or specificity are situated outside of the Pbs2 residues that interact with the canonical SH3-binding pocket, suggesting that other surfaces on Sho1 contribute to binding. We use predicted structures to propose a model of binding which involves residues neighboring the core Pbs2 motif binding outside of the canonical SH3-binding pocket, allowing affinity and specificity to be determined by a broader range of sequences than what has previously been considered.</p>
Data set for "A Magnesium Binding Site And The Anomeric Effect Regulate The Abiotic Redox Chemistry Of Nicotinamide Nucleotides"
<p>Data associated with Sebastianelli L, Kaur H, Chen Z, Krishnamurthy R, Mansy SS (2024) A magnesium binding site and the anomeric effect regulate the abiotic redox chemistry of nicotinamide nucleotides. Chem Eur J. 30, e202400411. DOI: 10.1002/chem.202400411 [<a href="https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/chem.202400411">link</a>]</p>
Profiling phage-host interactions between Skunavirus receptor binding proteins and lactococcal cell wall polysaccharide structures
Open the record for dataset details and reuse information.
[Accompanying Dataset for PHIStruct] ColabFold-Predicted Structures of Receptor-Binding Proteins
<p><strong>This dataset contains protein structures, computationally predicted via <a href="https://doi.org/10.1038/s41592-022-01488-1">ColabFold</a>, of 19,081 non-redundant (i.e., with duplicates removed) receptor-binding proteins from 8,525 phages across 238 host genera</strong>. We identified these receptor-binding proteins based on GenBank annotations. For phage sequences without GenBank annotations, we employed a pipeline that uses the viral protein library <a href="https://doi.org/10.1093/nargab/lqab067">PHROG</a> and the machine learning model <a href="https://doi.org/10.3390/v14061329">PhageRBPdetect</a>. </p> <p>More details can be found in our paper <strong>"PHIStruct: Improving phage-host interaction prediction at low sequence similarity settings using structure-aware protein embeddings."</strong> The project page is <a href="https://github.com/bioinfodlsu/PHIStruct">https://github.com/bioinfodlsu/PHIStruct</a>. Our paper is published in <em>Bioinformatics:</em> <a href="https://doi.org/10.1093/bioinformatics/btaf016" rel="nofollow">https://doi.org/10.1093/bioinformatics/btaf016</a></p> <p>Our research was supported with Cloud TPUs from <a href="https://sites.research.google/trc/about/" rel="nofollow">Google's TPU Research Cloud (TRC)</a> and with computing resources from the <a href="https://docs.mlerp.cloud.edu.au/" rel="nofollow">Machine Learning eResearch Platform (MLeRP)</a> of Monash University, University of Queensland, and Queensland Cyber Infrastructure Foundation Ltd.</p>
Dataset 2 for UV Plasmon-Enhanced Chiroptical Spectroscopy of Membrane-Binding Proteins, June 2024
<p>Scanning electron microscopy images of Al nanostructures</p>
Pose Selector Workflow - Docking Poses, Absolute Binding Free Energy Estimates and Structure Input Files for Machine Learning
<p>The Pose Selector (PS) workflow calculates absolute binding free energies (ABFEs) for binding poses of protein-ligand complexes. First, it converts the binding poses (both docking poses as well as experimentally observed ligand binding poses), which are provided as a combination of protein PDB file and ligand MOL2 file, into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks and repair steps. Next, the PS workflow post-processes and analyses the last frame of the resulting eight 100 ps trajectories per binding pose with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the ABFE estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the PS workflow was run on docking poses generated for the PDBbind 2020 dataset (http://www.pdbbind.org.cn/index.php), shared in dockingPosesPDBBind2020.tar.gz. This entry and its partner entry 10.5281/zenodo.11397486 also share the intial coordinates used in the MD simulations of >800,000 docking poses of 4022 protein-ligand complexes (structureFiles_dockingPoses1.tar.gz in this entry and structureFiles_dockingPoses2.tar.gz in 10.5281/zenodo.11397486) and of the experimental ligand binding pose of 4549 complexes (structureFiles_experimentalStructures.tar.gz) as well as the corresponding ABFE estimates (absoluteBindingFreeEnergyEstimates.tar.gz). The MD simulations were run on the LUMI and MeluXina supercomputers while the implicit-solvent calculations were carried out on Galileo (Cineca).</p> <p>The README file describes the structure of the shared data in more detail and points out how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates as well as how to use the data provided in this entry to train a machine-learning model predicting the ABFE of binding poses of protein-ligand complexes. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Pose-Selector-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
The state-of-the-art machine learning model for Plasma Protein Binding Prediction: computational modeling with OCHEM and experimental validation
<p><span>Institute of Materia Medica, Chinese Academy of Medical Sciences purchased 10,000 ChemDiv databases.</span></p>
Fig. 4 in The Mad2 Spindle Checkpoint Protein Undergoes Similar Major Conformational Changes upon Binding to Either Mad1 or Cdc20
Fig. 4. Putative homologies of molar features among toothed monotremes, occlusal views; mesial is to the left in all the drawings. A. Monotrematum, the geologically oldest upper molar known for monotremes (left upper molar shown). Outline restoration for M2 (A1); the same in stipple drawing (A2). B. Steropodon, the geologically oldest lower molars known for monotremes (left lower molars shown). Diagrammatic drawing (B1); the same in stippled drawing (B2). C. Obdurodon dicksoni. Left upper molars (C1); left lower ultimate premolar and first molar (C2). D. Hypothetical occlusal relationship of the upper and lower molars for basal monotremes; hypothetical models of upper molar (reversed crown view of right M2 of Monotrematum) and lower molars (crown view of Steropodon). Hypothetical contacting relations between the upper and the lower structures at the beginning of occlusion (D1); contacting relations near the mid−point of occlusion (analogous to the centric occlusion in the boreosphenidan mammals with the "pestle−to−mortar" occlusion (D2); contacting relations near of the end of the occlusal cycle (D3). The matching of the upper and the lower molar models is based on the similarity in the lowers between Steropodon and Obdurodon dicksoni and the similarity in the uppers of Monotrematum and O. dicksoni. E. Three stages (E1–E3) showing the sequence of upper−to−lower occlusion, in correspondence with D1–D3, as the lower molars moved across the transversely wider upper molar. Arrows denote direction of movement of the lower molars. Relative positions of the overlapping upper and lower molars of E1, E2, and E3 correspond to the contact points of the upper and lower structures labelled in D1–D3. See text for explanation. All original drawings, based on: A, photos of Pascual et al. (1992a, b) reversed; B, a cast of the holotype, reversed; C, SEM photos of Archer et al. (1993), with premolar reversed from the right side to be consistent with the left m1; D and E originals.
Fig. 1 in The Mad2 Spindle Checkpoint Protein Undergoes Similar Major Conformational Changes upon Binding to Either Mad1 or Cdc20
Fig. 1. Phylogenetic relationships of all major Mesozoic mammal lineages (strict parsimony from unconstrained searches). Each of the 42 equally parsimonious trees has: TreeLength = 935; CI = 0.499; RI = 0.762. Multi−state characters unordered; PAUP4.0b5 heuristic search (stepwise addition) 1000 runs. Numbers in circles (1 and 2) denote the nodes of two unnamed clades, described on p. 20 and 21 respectively, (3) crown−group Mammalia. Shadowed areas denote: Australosphenida (upper shading) and Boreosphenida (lower shading).
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 20. Overview about which Binding Mechanisms Work at what Hierarchical Levels and Development Stages of the Brain
<p>As a result of our research, in [60], a solution to the binding problem for perception was suggested by<br> combining the already existing binding hypotheses in a conclusive way, supplementing them with<br> other insights about the perceptual system of the brain, and translating them into a technically<br> implementable model. It was demonstrated via computational simulations that different binding<br> mechanisms proposed in literature are not mutually inclusive. On the contrary! At different<br> hierarchical levels and in different development stages, different binding mechanisms are acting in<br> perception. An overview about these circumstances is given in Figure 20. A detailed description can<br> be found in.</p>
ScienceDex guides
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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.