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292 results for “docks”
DockGame: Cooperative Games for Multimeric Rigid Protein Docking
<p>Raw datasets used for DB5.5 dataset and DIPS dataset used in DockGame, with associated code at https://github.com/vsomnath/dockgame </p> <p>These datasets can also be downloaded from https://zlab.umassmed.edu/benchmark (DB5.5) and https://www.dropbox.com/s/sqknqofy58nlosh/DIPS.zip?dl=0 (DIPS). Note that our organization would be slightly different from these downloaded versions.</p> <p>We follow EquiDock (https://github.com/octavian-ganea/equidock_public) for the DB5 dataset splits.</p> <p><strong>Paper Abstract</strong></p> <p>Protein interactions and assembly formation are fundamental to most biological processes. Predicting the assembly structure from constituent proteins -- referred to as the protein docking task -- is thus a crucial step in protein design applications. Most traditional and deep learning methods for docking have focused mainly on binary docking, following either a search-based, regression-based, or generative modeling paradigm. In this paper, we focus on the less-studied multimeric (i.e., two or more proteins) docking problem. We introduce DockGame, a novel game-theoretic framework for docking -- we view protein docking as a cooperative game between proteins, where the final assembly structure(s) constitute stable equilibria w.r.t. the underlying game potential. Since we do not have access to the true potential, we consider two approaches - i) learning a surrogate game potential guided by physics-based energy functions and computing equilibria by simultaneous gradient updates, and ii) sampling from the Gibbs distribution of the true potential by learning a diffusion generative model over the action spaces (rotations and translations) of all proteins. Empirically, on the Docking Benchmark 5.5 (DB5.5) dataset, DockGame has much faster runtimes than traditional docking methods, can generate multiple plausible assembly structures, and achieves comparable performance to existing binary docking baselines, despite solving the harder task of coordinating multiple protein chains.</p>
Supplementary Data for "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"
<p>Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation".</p><p>Includes:</p><ul><li>All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models.</li><li>Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes.</li><li>Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).</li></ul>
Dock crane
This is another fun game asset that I made for you. I would love to see what you do with it, send me a render at aiden_davenport@yahoo.com. Thanks! Source: Objaverse 1.0 / Sketchfab
St Katharine Docks Cannon
A 36-pounder cannon at the entrance to St Katharine Docks, London. Date: 18th Century 369 photos taken in October 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
FTDMP docking results for protein-protein, protein-DNA, protein-RNA benchmarks
<p><strong>FTDMP docking results for protein-protein, protein-DNA, protein-RNA benchmarks. </strong></p> <p>FTDMP is a software system for running docking experiments and scoring/ranking multimeric models. This dataset contains FTDMP docking results for protein-protein, protein-DNA, protein-RNA benchmarks. The FTDMP framework itself is available at https://github.com/kliment-olechnovic/ftdmp. </p> <p>Every *.tar.gz file in this dataset contains two folders: results for unbound-unbound and bound-bound docking. These folders contain results for the benchmark cases:</p> <p>252 folders with results for the protein-protein docking benchmark cases [1].<br>47 folders with results for the protein-DNA docking benchmark cases [2].<br>42 folders with results for the protein-RNA docking benchmark cases [3-6]. </p> <p>Every folder is named according to the PDB ID of the complex. The folders contain:</p> <p>1. A subfolder named <em>relaxed_top_complexes</em>. This subfolder contains 200 pdb files of relaxed [7] top docking models.<br>2. A text file named <em>scoring_results-ranks.txt</em>. It contains the names of the models (that are in the relaxed_top_complexes folder) in the ranked order. This means that the first model in the file is considered to be the best prediction by the FTDMP framework.<br>3. A text file named <em>cad_scores.txt</em>. It contains interface CAD-score and binding site CAD-score [8] results for every model.<br>4. A text file named <em>rmsd_results.txt</em>, which is available only for protein-DNA and protein-RNA cases. The file contains ligand-RMSD values for the models, where the DNA/RNA is considered as the ligand.<br>5. A text file named <em>DockQ_results.txt</em>, which is available only for the protein-protein docking cases. The file contains DockQ [9] results for every model, as well as model accuracy based on CAPRI criteria (Incorrect, Acceptable, Medium, High)<br>6. A text file named <em>binding_site_CAD-scores.txt</em>, which contains the binding site CAD-score from the <strong>protein</strong> side for RNA and DNA docking. This binding site CAD-score shows how accurately the ligand (DNA/RNA) was docked to the protein without taking the orientation of the ligand into consideration. In the case of protein-protein docking the binding site CAD-score file is available only for antibody-antigen docking targets and contains the binding site (epitope) CAD-score for the antigen. </p> <p>The ligand-RMSD, CAD-scores, and DockQ scores were all calculated by comparing the models to the corresponding targets. The target structures are available at <span>https://zenodo.org/records/10517524</span>. These target structures have the same residue numbering as the models available here. </p> <p>REFERENCES </p> <p>[1] Guest, J. D., et al. (2021). An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure, 29(6), 606–621.e5.<br>[2] van Dijk, M., Bonvin, A.M. (2008). A protein-DNA docking benchmark. Nucleic Acids Res, 36, e88. <br>[3] Perez-Cano, L., et. Al. (2012). A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data. Proteins, 80(7): 1872-1882. <br>[4] Huang, S.Y., Zou, X. (2013). A nonredundant structure dataset for benchmarking protein-RNA computational docking. J Comput Chem, 34(4): 311-318. <br>[5] Nithin, C., et. al. (2017). A non-redundant protein-RNA docking benchmark version 2.0. Proteins, 85(2) :256-267. <br>[6] Zheng, J., et al. (2020). P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking. Bioinformatics, 36(1), 96–103. <br>[7] Eastman, P., et al.(2017). OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLOS Comp. Biol., 13(7): e1005659. <br>[8] Olechnovic, K., Venclovas, C. (2020). Contact area-based structural analysis of proteins and their complexes using CAD-score. Methods Mol Biol, 2112, 75.<br>[9] Basu, S., Wallner, B. (2016). DockQ: A Quality Measure for Protein-Protein Docking Models. PLoS ONE 11(8): e0161879. </p>
Data from: Benzyl alcohol synergistic effect with deltamethrin against Musca domestica with molecular docking of potential modes of action
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Data from: Synthesis, antimicrobial evaluation, ADMET prediction, molecular docking and dynamics studies of pyridine and thiophene moieties containing chalcones
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Data from: Compromise docking power evaluation of liganded crystal structures of Mpro SARS-CoV-2
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Fragment and torsion biasing algorithms for construction of small organic molecules in proteins using DOCK
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Data from: A maximum of two readily releasable vesicles per docking site at a cerebellar single active zone synapse
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Data from: The herpes simplex virus pUL16 and pUL21 proteins prevent capsids from docking at nuclear pore complexes
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A plastome phylogeny of Rumex (Polygonaceae) illuminates the divergent evolutionary histories of docks and sorrels
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The docked poses from the docking screens against the AlphaFold2 models of the σ2 and 5HT2A receptors
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Data for: Advances and critical assessment of machine learning techniques for prediction of docking scores
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Inputs for Galaxy tutorial on molecular docking on SARS-CoV-2 MPro
<p>Inputs for Galaxy tutorial on molecular docking on SARS-CoV-2 main protease.</p>
An in silico docking simulation of SARS Coronavirus2 and Ivermectin
<p><strong>ABSTRACT</strong></p> <p> COVID-19 is spreading and infecting in the world. And that is occurring a death very much. Of course, I want to cooperate for save peoples. And I was simulating a docking about proteins of SARS Coronavirus 2 in silico. That a papain-like protease, a karyopherin importin α,β and RNA Polymerase. One hypothesis [1] said Ivermectin can destabilises about a bind to an importin and the virus cargo proteins. And I got a significantly results from in silico simulation that at catalytic center of RNA Polymerase. Remdesivir is docking at here. [2] I dedicate this report to a current patients and a future patients.</p> <p><strong>1. Introduction</strong></p> <p> The virus is called the bacteriophage. You know that the virus infect the bacteria. Therefore, the bacteria need a counter plan. I think, Ivermectin(Avermectin) is that. One hypothesis, the Coronavirus2 is<br> replicating by using a main protease [4], papain-like protease [4] and this RNA Polymerase. And Ivermectin destablishes about bind to an importin and the virus cargo proteins [1]. I checked for these case. But I<br> seems that Importin is too big for Ivermectin. And netxt, I checked for RNA Polymerase. and I report this result simply.</p> <p><strong>2. A software for docking simulation</strong></p> <p> This case is using a software that “Autodock vina” [5]. This software is better performance than other docking simulation softwares. But a simulation accuracy isn’t a high quality more than a real phenomenon<br> yet. This software is using an affinity score [kcal/mol]. See also vina web-site [5].</p> <p><strong>3. A docking parameters and Dataset</strong></p> <p> This case is using following parameters for docking simulations.</p> <p>(1).exhaustiveness: 8<br> (2).num modes: 10<br> (3).energy range: 1</p> <p>and I was using a protein data PDB:7bzf for RNA Polymerase. And PDB:IVM for Ivermectin. I chosen a chain A from PDB:7bzf by using pymol [8]. And I converted PDB:IVM from .sdf to .pdb by using “PDB<br> format-PDBx/mmCIF conversion service” web-site [9].</p> <p><strong>4. Contents</strong></p> <p> Figure 2: Front view of RNA Polymerase and Ivermectin I tried to a docking simulation during a four monthes about RNA Polymerase (PDB:7bzf) of SARS Coronavirus2 and Ivermectin [6]. And<br> I got a significantly results. I chosen a few higher score to following.</p> <p>mode | affinity | dist from best mode<br> | (kcal/mol) | rmsd l.b.| rmsd u.b.<br> -----+------------+----------+----------<br> 1 -11.5 0.000 0.000<br> 2 -11.1 1.975 3.233<br> 3 -11.0 1.895 2.482</p> <p>mode | affinity | dist from best mode<br> | (kcal/mol) | rmsd l.b.| rmsd u.b.<br> -----+------------+----------+----------<br> 1 -11.4 0.000 0.000<br> 2 -11.0 1.572 2.041<br> 3 -10.8 4.406 14.971</p> <p>mode | affinity | dist from best mode<br> | (kcal/mol) | rmsd l.b.| rmsd u.b.<br> -----+------------+----------+----------<br> 1 -11.4 0.000 0.000<br> 2 -10.8 1.550 2.430<br> 3 -10.6 1.648 2.197</p> <p>These are a near points. You can see a number of rmsd (Root Mean Square Deviation). These are a catalytic center of RNA Polymerase. One paper said [2], this point can combine Remdesivir too. Figure 1,2,3 are a point of best of affinity score that -11.5[kcal/mol]. Of course, If you want to know detail of results then you can download an all eleven data about this simulation from my web-site.</p> <p><strong>5. Conclusion</strong></p> <p> I seems that is very significantly result. Because, One, here are a higher score point (Figure 4). Second, here are a catalytic center. Third, Remdesivir can combine at same point [2]. I consider about an accuracy<br> of Autodoc Vina and a conformation will change, probably. You know, this is a computer simulation absolutely. I want to wait a result of cryo-EM and a crystal structure complex.</p> <p><strong>Acknowledge</strong></p> <p> Thank you for the NIG supercomputer at ROIS National Institute of Genetics. Because I’m using this computer system everyday.</p> <p> </p>
Data from: Molecular docking and dynamics studies to identify novel active compounds targeting potential breast cancer receptor proteins from an indigenous herb Euphorbia thymifolia Linn
<p>Breast cancer has become most prevalent disease and their incidence has doubled in Indian scenario. Targeted therapy with the novel compounds derived from plants could be the promising approach for the development of drugs. <em>Euphorbia thymifolia</em> L is a widely growing tropical herb which has been reported for its various ethnopharmacological properties, including anticancer properties. The aim of the present study was to identify the active phytocompounds present in the methanolic extract using an <em>I</em><em>n-silico</em> approach. The methanolic extract of <em>E. thymifolia</em> (ME.ET) was subjected to GC-MS analysis and the identified compounds were docked with potential protein targets implicated in breast cancer such as ERK1, AKT, EGFR/HER2, ER, MELK, PLK1, PTK6. Compounds with good docking score were further subjected to dynamics study to understand the protein ligand binding stability, ligand pathway calculation, molecular mechanics energies combined with Poisson-Boltzmann (MM/PBSA) calculation using Schrodinger suite. Out of 219 unique phytocompounds subjected to docking, two compounds namely, 3,6,9,12-tetraoxatetradecane-1,14-diyl dibenzoate (TTDB) and succinic acid, 2-(dimethylamino)ethyl 4-isopropylphenyl ester (SADPE) showed good docking score. Molecular dynamics study showed high affinity and low binding energy for TTDB with HER2, ERK1 and SADPE with ER. Hence this is the first study to identify and report active compounds from <em>E.thymifolia</em> linn. Further <em>invitro</em> and <em>invivo</em> anticancer studies can be performed to confirm these results and understand the molecular mechanism by which TTDB and SADPE exhibit anticancer activity against breast cancer.</p>
Docking of 8400 peptides into an experimental structure of bacterial peptide transporter DtpB
<p>Di- and tripeptides composed of proteinogenic amino acids were docked into an experimental structure of DtpB using Rosetta FlexPepDock protocol. Additional experimental structures of DtpB were used to train a classifier to select top docking poses. Average Rosetta energy terms for top 10 hits were then combined with experimental affinity measurements to train a binary classifier that predicts binders to DtpB.</p> <p>This upload includes input and output data as well as code to run and analyse the full pipeline. Some intermediate results are available upon request.</p> <p>For more details, please see:<br> <strong>Plasticity of the binding pocket in peptide transporters underpins promiscuous substrate recognition</strong></p> <p>https://dx.doi.org/10.1016/j.celrep.2023.112831</p>
Molecular docking and quantum-chemical characterization of inhibitory activity of procyanidins and flavonol glucosides from Graptopetalum paraguayense E. Walther against nonstructural proteins of SARS-Cov-2
<p>The dataset includes:</p> <ol> <li><span>Optimized geometries of all ligands, NSPs and their complexes in gas phase</span></li> <li><span>Total energies (a.u.) and interaction energies (a.u. and kcal mol-1) of the complexes, ligands and NSPs in gas phase and in argon</span></li> <li><span>Mass spectra (full ms) and product ion spectra (ms2) of standards and identified components from GP. </span></li> <li><span>Output files from quantum-chemical calculations.</span></li> <li><span>pdb files of NSPs</span></li> <li><span>mol files of all complexes</span></li> </ol>
Molecular docking analysis was performed to assess the affinity of onalespib for their targets LOX, elucidating binding poses, protein interactions, and associated binding energies.
<p>To analyze the binding affinities and interaction modes between the drug candidates and their targets, we employed the Autodock Vina software [21]. Molecular structures of the candidate drugs and targets of hub genes were retrieved from Pubchem (https://pubchem.ncbi.nlm.nih.gov/) and Protein Data Bank database (http://www.rcsb.org/), respectively. <span>In the analysis of docking, the files for all proteins and molecules were converted to PDBQT format. Water molecules were removed and polar hydrogen atoms were added. The grid box was positioned at the center to encompass the protein domain, allowing for unrestricted movement of molecules.</span></p>
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