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7 results for “docking poses”
TOP-100 DOCKING POSES OF FDA APPROVED DRUGS AND DRUGS IN CLINICAL INVESTIGATION AT SARS-CoV2 MAIN PROTEASE
<p>7922 compounds were downloaded from NPC database (https://tripod.nih.gov/npc/). In order<br> to eliminate the non-specific binders, some criteria including molecular weight, between 100 to<br> 1000 g/mol; number of rotatable bonds, <100; number of atoms, between 10 and 100; number<br> of aliphatic and aromatic rings, <10; number of hydrogen-bond acceptor and donors, <10 were<br> set and as a result the total number of compounds was decreased to 6654. These ligands were<br> prepared using LigPrep module of Maestro at neutral pH (LigPrep, Schrodinger v.2017). In<br> molecular docking, we used following protein structure: SARS-CoV2 Main Protease, (PDB, 6LU7). The protein<br> was prepared using Protein Preparation module of Maestro. PROPKA was used for<br> determination of protonation states of amino acid residues. Restrained minimization was<br> performed with OPLS3 force field for the protein using 0.3 Å heavy atom convergence.<br> Docking was performed with Glide/SP using default settings. Top-100 docking poses were provided.</p>
TOP-100 DOCKING POSES OF FDA APPROVED AND DRUGS IN CLINICAL INVESTIGATION AT SARS-CoV2 SPIKE/ACE2 INTERFACE
<p>7922 compounds were downloaded from NPC database (https://tripod.nih.gov/npc/). In order<br> to eliminate the non-specific binders, some criteria including molecular weight, between 100 to<br> 1000 g/mol; number of rotatable bonds, <100; number of atoms, between 10 and 100; number<br> of aliphatic and aromatic rings, <10; number of hydrogen-bond acceptor and donors, <10 were<br> set and as a result the total number of compounds was decreased to 6654. These ligands were<br> prepared using LigPrep module of Maestro at neutral pH (LigPrep, Schrodinger v.2017). In<br> molecular docking, we used following protein structure: Spike Protein/ACE-2, (PDB, 6M0J). The protein<br> was prepared using Protein Preparation module of Maestro. PROPKA was used for<br> determination of protonation states of amino acid residues. Restrained minimization was<br> performed with OPLS3 force field for the protein using 0.3 Å heavy atom convergence.<br> Docking was performed with Glide/SP using default settings. Top-100 docking poses were provided.</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 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>
The docked poses from the docking screens against the AlphaFold2 models of the σ2 and 5HT2A receptors
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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>
The impact of the cross-docked poses on the performance of machine learning classifier for protein-ligand binding pose prediction
<p>Datasets, features, and some representative scripts utilized in the paper "The impact of the cross-docked poses on the performance of machine learning classifier for protein-ligand binding pose prediction".</p>
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