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292 results for “docks”
Greenland Dock Winch
An old cast iron winch at Swedish Quay, Greenland Dock, Rotherhithe, London. Date: unknown. 275 photos taken in November 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Gallions Reach Dock Structure
A set of concrete structures at the former entrance to the Royal Albert Dock (now City Airport), London. EDOBID: e40611 This twitter thread contains some excellent info on why there is no evidence for it being a pillbox: https://twitter.com/AntonyFjrth/status/1521960288219811840?s=20&t=XfL5-QDOS6Dk4uV4XobX7g 1434 photos taken in May 2022 with a Sony a7R III and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
The docked poses from the docking screens against the AlphaFold2 models of the σ2 and 5HT2A receptors
<p>Though AlphaFold2 (AF2) models have had wide impact, they have had mixed success in retrospective ligand recognition. Here, we prospectively docked large libraries against unrefined AF2 models of the σ2 and 5-HT2A receptors, testing hundreds of new molecules and comparing results to docking against the experimental structures. Hit rates were high and similar for the experimental and the AF2 structures, as were affinities. The success of docking against the AF2 models was achieved despite differences in orthosteric residue conformations versus the experimental structures. Determination of the cryoEM structure for one of the more potent 5HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based ligand discovery. This repository contains docked poses shown in this study.</p> <p>The data contain:</p> <p>1) AlphaFold2 models of the σ2 and 5HT2A receptors used to generate these two docked poses:</p> <ul> <li>AF-Q5BJF2-F1-model_v1_prep.pdb is the AlphaFold2 model used for docking against the σ2 receptor.</li> <li>AF-P28223-F1-model_v1_prep.pdb is the AlphaFold2 model used for docking against the 5HT2A receptor.</li> </ul> <p>2) Docked poses of ZINC866533340 in the σ2 model and Z7757 (ZINClB000002x4yW) in the 5HT2A model:</p> <ul> <li>ZINC000866533340_af.mol2 is the docked pose of ZINC866533340 in the σ2 model.</li> <li>7757_af.mol2 is the docked pose of Z7757 (ZINClB000002x4yW) in the 5HT2A model.</li> </ul>
Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics
<p>Supplementary material to the article: Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer’s Disease Based on Bioinformatics, Molecular Docking and Molecular Dynamics,These data are available to researchers.</p>
Figure 6 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire
Figure 6. Structures of Ellagic Acid, Gallic Acid, α and β Punicalagins.
Molecular Dynamics Simulation and Docking Studies Reveals Inhibition of NF-kB signaling as a Promising Therapeutic Drug Target for reduction in Cytokines Storms
<p><span>The complexes of the top identified molecules with NF-kB-kB site, as well as all the designed molecules used in the screening process. </span></p>
Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations
<p>Dataset and supplementary files of the research: Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations (https://doi.org/10.1101/121681)</p> <p>- Supporting Information</p> <p>- Input: Parameters and initial structures</p> <p>- Output: Trajectories, Docking poses</p> <p>Gromacs (multi-core with CUDA) was used for the simulations.</p> <p>Autodock Vina, FlexAid and rDock were used for molecular docking.</p>
Protein-Protein Docking for PROTAC discovery
<p>My goal is to test whether structure-based approaches can guide the design of PROTACs. As a first step, I am evaluating whether protein-protein docking tools can accurately predict the interface between an E3 ligase and its target, a question recently explore by Drummond et al. This step is necessary to define the relative orientation of the chemical moiety binding the E3 ligase and the chemical moiety binding the target protein. Once we have this information, the second step will be to design PROTACs that are compatible with the relative orientation of these 2 chemical moieties. For this first protein-protein docking step, I will compare predicted structures with crystal structures of three complexes: the first bromodomain of BRD4 (BRD4<sup>BD1</sup>) bound to the E3 ligases CRBN [pdb codes 6boy and 6bnb], the second bromodomain of BRD4 (BRD4<sup>BD2</sup>) bound to the E3 ligase VHL [pdb code 5t35] and, the bromodomain of SMARCA2<sup>BD</sup> bound to the E3 ligase VHL [pdb code 6hay]. I will be using three different protein-protein docking tools: HADDOCK, Rosetta, and ICM, which all performed among the best at past CAPRI protein docking competitions.</p>
A Deep-Learning Approach for Visual Detection of an AUV Docking Station - Dataset
<div>This dataset was used to train the models from the paper "A Deep-Learning Approach for Visual Detection of an AUV Docking Station" published by Ahmad et al. at Oceans 2024 Conference in Halifax.</div> <div> </div> <div># Dataset</div> <div> </div> <div>The dataset contains:</div> <div> </div> <div>1. images from Abisko Lake in Sweden[1]</div> <div>2. images recorded in the Maritime Hall basin at DFKI</div> <div> </div> <div># File contents</div> <div>Each of these datasets are put into seperate directories. The images were annotated using CVAT[2].</div> <div> </div> <div>The dataset has been exported into the following formats:</div> <div> </div> <div>1. YOLO</div> <div>2. PascalVOC</div> <div>3. COCO</div> <div> </div> <div>The exported datasets does not contain raw images, rather they are places into a seperate zip folder.</div> <div> </div> <div> </div> <div> </div> <div># References</div> <div>[1]: <a href="https://zenodo.org/records/7035132">https://zenodo.org/record/7035132#.ZDfKE5FBzJU</a></div> <div>[2]: https://www.cvat.ai/</div>
Docking data for "The evolution of the SARS-CoV-2 spike protein for differential usage of the host transmembrane serine proteases entry pathway"
<p><br>The dataset includes predicted complexes of the SARS-CoV-2 Spike protein (specifically at the S2' cleavage site) with Hepsin and TMPRSS2 proteins. It contains data on three variants: Wuhan, Delta, and Omicron BA.1.</p> <p><strong>Compressed folders:</strong></p> <p>-357596-DeltaHepsin.tgz</p> <p>-357597-DeltaTMPRSS2.tgz</p> <p>-360039-WuhanHepsin.tgz</p> <p>-360042-TMPRSSWuhan.tgz</p> <p>-392981-TMPRSS-BA1_all.tgz</p> <p>-392982-Hepsin-BA-all.tgz</p> <p><strong>Each compressed folder contains the following:</strong></p> <p>-Initial structures in pdb format</p> <p>-Output complexes in pdb format</p> <p>-Clusters in pdb format</p> <p>-Protocols</p> <p>-Parameters</p> <p>-Scoring files</p> <p> </p> <p><strong>Protein-protein docking </strong><br>Molecular docking between the SARS-CoV-2 S protein of Wuhan, Delta (PDB: 7W92, [DOI: 10.1038/s41467-022-28528-w]), and BA.1 (PDB: 7XO5, [DOI: 10.1038/s41422-022-00672-4]) and the human proteases TMPRSS2 (PDB: 8HD8, [DOI: 10.1038/s41467-023-42527-5]) and Hepsin (PDB: 1Z8G, [DOI: 10.1042/BJ20041955]) was performed using the HADDOCK v2.5-2024.03 webserver ([DOI: 10.1021/ja026939x], [DOI: 10.1016/j.jmb.2015.09.014]). Missing loops in the protein structures were reconstructed using Modeller v10.5 ([DOI: 10.1006/jmbi.1993.1626]). Every heteroatom was removed from the reference structures. The relaxed atomistic coordinates for each S protein variant were derived via all-atom molecular dynamics (MD) simulations. These simulations were performed using AMBER22 with the FF19SB force fields and the pmemd.cuda module for enhanced performance ([DOI: 10.1021/acs.jcim.3c01153], [DOI: 10.1021/jz501780a], [DOI:10.1021/ct400314y]). For the Wuhan variant the S protein was retrieved from our previous modeling study [DOI: 10.1039/D0NR03969A] where for Delta and BA.1, ecah S protein was placed in a dodecahedral box, extending 20 Å beyond the solute in every cartesian direction, and solvated with the four-site OPC water model ([DOI: 10.1021/jz501780a]). The systems were neutralized with counterions, specifically one Cl− ion for the Delta variant and three Cl- ions for the BA.1 variant. To remove local clashes, a geometric optimization was performed using the steepest descent algorithm for 5000 cycles. The MD equilibration process consisted of several stages. First, temperature equilibration in the NVT ensemble was performed by gradually increasing the temperature through steps of 150, 200, 250, 300, and finally 310 K, each lasting 200 ps. During this phase, position restraints were applied to the heavy atoms of the proteins, with progressively decreasing spring constants of 5.0, 4.0, 3.0, and 1.0 kcal mol−1 Å−2, facilitating gradual relaxation. This was followed by a 1 ns equilibration at 310 K in the NPT ensemble without restraints. For production MD, the simulations were run in the NPT ensemble with periodic boundary conditions and Particle Mesh Ewald (PME) method ([DOI: 10.1063/5.0040966], [DOI: 10.1021/ct9001015]) using a grid spacing of 1.0 Å for long-range electrostatics. Non-bonded interactions were modeled with a Lennard-Jones potential using a 9Å cutoff. Temperature control was maintained using Langevin dynamics ([DOI: 10.1021/ct800573m]) with a collision frequency of 4.0 ps−1, and pressure control was managed by the Monte Carlo barostat ([DOI: 10.1016/j.cplett.2003.12.039]) with a 2.0 ps relaxation time at 1 bar. Bond constraints on hydrogen atoms were applied using the SHAKE algorithm ([DOI: 10.1016/0021-9991(77)90098-5]), and the hydrogen mass repartitioning scheme was applied via ParmEd ([DOI: 10.1371/journal.pcbi.1005659]), enabling a 4 fs integration time step ([DOI: 10.1021/ct5010406]). Each protein complex was simulated for a total of 20 ns. For the Wuhan variant, the 3D coordinates were retrieved from [DOI: 10.5281/zenodo.3817446].<br>The active interaction region on the spike protein was defined as the cleavage site (residues P809-R815). For TMPRSS2 and Hepsin, the active sites were defined based on their catalytic residues: H296, D345, D435, S441, S460, and G462 for TMPRSS2, and H203, D257, D347, A348, and S353 for Hepsin. These specific regions were selected to guide the docking process and maximize biologically relevant interactions. Docking clusters were analyzed by selecting those with the lowest interaction energies for further structural analysis. To evaluate binding accuracy, native contacts between the S protein and proteases were computed using the contact map analysis based on the OV+rCSU method ([DOI: 10.12693/APhysPolA.145.S9, 10.1021/acs.jctc.6b00986]), which allows for a precise identification of critical stabilizing interactions, both specific and non-specifics. High-frequency contacts, defined as those appearing in over 70% of the generated models, were highlighted as key determinants of protein-protein recognition, providing insight into the most stable and consistent interactions across docking configurations.</p>
Hull Springs Dock YSI Station 2021-05-03 to 2021-06-09
<pre># General Metadata for Hull Springs Dock-YSI Sampling Station ## Files Specific metadata for each deployment can be found as text files with the file format of: HS_YSI_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. ## File Created * 2021-07-14 by KF ## File Modified ## Description These data are from the YSI EXO3 sampling station in Aimes Creek near the lab dock (38.125367, -76.659537). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The sensors are sampled every 15 minutes ### Measurements Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * date - the date that the record was collected ( * time - the time that the record was collected ( * site_name - the ID name provided by the KOR software. * unit_ID - the sonde identification number. * user_ID - the user that created the sampling template. * Temp_dC - the water temperature in degrees C. * DO_perc - the dissolved oxygen percent saturation (%). * DO_percL - the "local" dissolved oxygen percent saturation where the calibration is locally always 100% saturated independent of the barometric pressure (%). * DO_mg-L - the concentration of dissolved oxygen (mg/L). * SPC_uS-cm - the specific conductance (uS/cm). * C_uS-cm - the conductivity (uS/cm). * nLFC_uS-cm * TDS_mg-L - the total dissolved solids (mg/L). * SAL_PSU - the salinity of the water (PSU). * pH - the pH of the water. * pH_mV - the millivolt measurement of the pH meter (mV). * FNU - the turbidity of the water (FNU). * TSS_mg-L - the total suspended solids (mg/L). * BGA_PC_RFU - the phycocyanin fluorescence level which is an indicator of blue-green algae (RFU). * BGC_PC_ug-L - the concentration of phycocyanin in the water, which is an indicator of blue-green algae (ug/L). * Chl_RFU - the chlorophyll fluorescence level, which is an indicator of phytoplankton biomass (RFU). * Chl_ug-L - the concentration of chlorophyll in the water, which is an indicator of phytoplankton biomass (ug/L). * fDOM_RFU - the fluorescent DOM fluorescence level (RFU). * fDOM_QSU - the standardized fluorescent DOM concentration (QSU). * Wiper_V - the voltage output of the wiper (V). * Cabel_V - the voltage output of the cable (V). * Batt_V - the voltage output of the internal batteries (V). </pre>
FIGURE 4 in Like A Glove: Do The Dimensions Of Male Adanal Suckers And Tritonymphal Female Docking Papillae Correlate In The Proctophyllodidae (Astigmata: Analgoidea)?
FIGURE 4: Size distributions of Proctophyllodes troncatus adult females, adult males and nymphs with and without docking papillae. Body size was measured as the length of the idiosoma (µm) from the anterior margin of the prodorsum to the posterior region of the body excluding the terminal hyaline appendages in adult females and the opisthosomal lamellae in males. Nymphs were catego- rized as having docking papillae or lacking docking papillae. Adult females were significantly larger than adult males as indicated by an unpaired t-test (t38 = 22.6, P <0.001). Similarly, nymphs with docking papillae were larger on average than were those without docking papillae (t194 = 20.6, P <0.001).
FIGURE 3 in Like A Glove: Do The Dimensions Of Male Adanal Suckers And Tritonymphal Female Docking Papillae Correlate In The Proctophyllodidae (Astigmata: Analgoidea)?
FIGURE 3: Correlations in morphology between adult male and tritonymphal female Neodectes spp., Proctophyllodes spp., and Proterothrix spp. feather mites (Astigmata: Proctophyllodidae). Correlations are illustrated between widths (A-B) and lengths (C-D) of the male adanal suckers and the female docking papillae. R2 values indicate how well variation in one variable is predicted by that of the other variable.
FIGURE 2 in Like A Glove: Do The Dimensions Of Male Adanal Suckers And Tritonymphal Female Docking Papillae Correlate In The Proctophyllodidae (Astigmata: Analgoidea)?
FIGURE 2: Correlation between adult male and tritonymphal female body length (µm) in Neodectes spp., Proctophyllodes spp., and Proterothrix spp. (Astigmata: Proctophyllodidae). Length of the idiosoma was measured from the anterior margin of the prodorsum to the posterior region of the body excluding the opisthosomal lamellae in males. Body size was positively correlated between the sexes (rs = 0.66, n = 32, P <0.01) whereby larger tritonymphal females were paired with larger conspecific males.
FIGURE 1 in Like A Glove: Do The Dimensions Of Male Adanal Suckers And Tritonymphal Female Docking Papillae Correlate In The Proctophyllodidae (Astigmata: Analgoidea)?
FIGURE 1: Length of the idiosoma in Proctophyllodes spp. in A. female tritonymphs, B. adult females and C. adult males. Measurements (dashed line) were taken from the margin of the prodorsum to the posterior margin of the body. In female tritonymphs we measured the lateral length (A), medial length (B), and width (C) of the docking papillae. In adult males (D; ventral), we measured the distal (D) and basal (E) widths of the adanal suckers as well as sucker depth (F). Line drawings are modeled after Proctophyllodes glandarinus for adults and from Proctophyllodes pari for tritonymphs (Atyeo and Braasch, 1996). Diagrams of the docking papillae and adanal suckers are drawn after scanning electron images published in Witali´nski et al. (1992).
Data storage_FcPsomes_AOs_Docking_Undocking
<p>This dataset contains raw and processed data related to the paper "Crosslinked and multi-responsive polymeric vesicles as a platform to study enzyme-mediated undocking behavior: Towards future artificial organelle communication" in 2023.</p>
Data for: Advances and critical assessment of machine learning techniques for prediction of docking scores
<p class="MsoNormal"><span>Semi-flexible docking was performed using AutoDock Vina 1.2.2 software on the SARS-CoV-2 main protease M<sup>pro</sup> (PDB ID: 6WQF). <br></span></p> <p class="MsoNormal"><span>Two data sets are provided in the xyz format containing the AutoDock Vina docking scores. These files were used as input and/or reference in the machine learning models using TensorFlow, XGBoost, and SchNetPack to study their docking scores prediction capability. The first data set originally contained 60,411 in-vivo labeled compounds selected for the training of ML models. The second data set,denoted as in-vitro-only, originally contained 175,696 compounds active or assumed to be active at 10 μM or less in a direct binding assay. These sets were downloaded on the 10th of December 2021 from the ZINC15 database. Four compounds in the in-vivo set and 12 in the in-vitro-only set were left out of consideration due to presence of Si atoms. Compounds with no charges assigned in mol2 files were excluded as well (523 compounds in the in-vivo and 1,666 in the in-vitro-only set). Gasteiger charges were reassigned to the remaining compounds using OpenBabel. In addition, four in-vitro-only compounds with docking scores greater than 1 kcal/mol have been rejected.</span></p> <p class="MsoNormal"><span>The provided in-vivo and the in-vitro-only sets contain 59,884 (in-vivo.xyz) and 174,014 (in-vitro-only.xyz) compounds, respectively. Compounds in both sets contain the following elements: H, C, N, O, F, P, S, Cl, Br, and I. The in-vivo compound set was used as the primary data set for the training of the ML models in the referencing study. <br></span></p> <p class="MsoNormal"><span>The file in-vivo-splits-data.csv contains the exact composition of all (random) 80-5-15 train-validation-test splits used in the study, labeled I, II, III, IV, and V. Eight additional random subsets in each of the in-vivo 80-5-15 splits were created to monitor the training process convergence. These subsets were constructed in such a manner, that each subset contains all compounds from the previous subset (starting with the 10-5-15 subset) and was enlarged by one eighth of the entire (80-5-15) train set of a given split. These subsets are further referred to as in_vivo_10_(I, II, ..., V), in_vivo_20_(I, II, ..., V),..., in_vivo_80_(I, II, ... V). </span></p>
A plastome phylogeny of Rumex (Polygonaceae) illuminates the divergent evolutionary histories of docks and sorrels
<p>The genus <em>Rumex</em> L. (Polygonaceae) provides a unique system for investigating the evolutionary development of sex determination and molecular rate evolution. Historically, <em>Rumex</em> has been divided, both taxonomically and colloquially into two groups: 'docks' and 'sorrels'. A well-resolved phylogeny can help evaluate a genetic basis for this division. Here we present a plastome phylogeny for 34 species of <em>Rumex</em>, inferred using maximum likelihood criteria. The historical 'docks' (<em>Rumex</em> subgenus <em>Rumex</em>) were resolved as monophyletic. The historical 'sorrels' (<em>Rumex</em> subgenera <em>Acetosa</em> and <em>Acetosella</em>) were resolved together, though not monophyletic due to the inclusion of <em>R. bucephalophorus</em> (<em>Rumex</em> subgenus <em>Platypodium</em>). <em>Emex</em> is supported as its own subgenus within <em>Rumex</em>, instead of resolved as sister taxa. We found remarkably low nucleotide diversity among the docks, consistent with recent diversification in that group, especially as compared to the sorrels. Fossil calibration of the phylogeny suggested that the common ancestor for <em>Rumex</em> (including <em>Emex</em>) has origins in the lower Miocene (22.13 MYA). The sorrels appear to have subsequently diversified at a relatively constant rate. The origin of the docks, however, was placed in the upper Miocene, but with most speciation occurring in the Plio-Pleistocene.</p>
Docking of chloroquine and piperaquine to PfCRT_Dd2 and to the PfCRT_Dd2 mutations conferring piperaquine resistance
<p>PDB files of the docking of chloroquine and piperaquine to the PfCRT_Dd2 variant and to the Dd2 mutants investigated</p> <p>reported in the manuscript</p> <p>"PfCRT mutations conferring piperaquine resistance in falciparum malaria shape the kinetics of quinoline drug binding and transport"</p> <p>by Guillermo Martin Gomez; Giulia D'Arrigo; Fiona Berger; Cecilia P. Sanchez; Rebecca C. Wade and Michael Lanzer</p> <p>submitted to PLOS Pathogens</p> <p> </p> <p>1. <strong>Dockings.zip </strong>contains <strong>a)</strong> the individual pdb files of the PfCRT_Dd2 variants models prior to preparation with maestro and <strong>b)</strong> sub-folders with the docking results for each PfCRT_Dd2 variant and for PfCRT_7G8</p> <p>2. <strong>Simulations.zip </strong>contains the input file for the simulated systems. Each sub-folder contains:</p> <ul> <li>assembly.pdb - structure of the complex in PDB format</li> <li>assembly.prmtop - topology file in AMBER</li> <li>assembly.inpcrd - coordinates file in AMBER</li> <li>final.pdb - structure after NPT production</li> </ul>
Identification of Potential JNK3 Inhibitors Through Virtual Screening, Molecular Docking And Molecular Dynamics Simulation as Therapeutics for Alzheimer's Disease
<p>Alzheimer's disease (AD) is a complex neurological disorder without effective treatment. One factor in its development is c-Jun N-terminal kinases (JNKs), a type of protein related to brain function. JNK3, found mainly in the brain, contributes to AD by promoting brain abnormalities. Current research aims to create new JNK3 inhibitors for AD treatment using a virtual screening method. A database of compounds was filtered, and five potential compounds were identified with better scores than a reference. These compounds underwent simulations and energy calculations, showing stability and potential as JNK3 inhibitors.</p>
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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)
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