Parametrization of ligands (NQ 4 and doxorubicin) and DENV-2 prefusion envelope protein (PDB:1OKE) was done using the AutoDock Tools suite [54 (link)]. Hydrogen atoms were added to the polar side chains and partial charges were calculated through the Gasteiger methodology. Then, a grid box was delimited in a binding site previously reported with some studied inhibitors [16 (link)]. Molecular docking was run with a modified version of AutoDock Vina that includes a scoring function parameterized also for halogen interactions [55 (link)]. We used an exhaustiveness (number of internal repetitions) of 20 for each protein-compound pair. The interactions (hydrogen bonds and hydrophobic interactions) and the predicted free energy scores in kcal/mol were obtained. Visualization of the docking results was generated using the Discovery Studio package.
Tools suite
Manufactured by AutoDock
AutoDock Tools is a suite of software programs for modeling the interactions between a small molecule and a receptor of known 3D structure. The core function of the software is to predict the preferred orientation and binding affinity of ligands to target proteins.
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Lab products found in correlation
4 protocols using tools suite
1
Molecular Docking of DENV-2 Envelope Protein
2
Docking Simulation of CNTs and TLRs
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The docking simulation of CNTs to TLRs was performed using the AutoDock Vina software75 . The 3D structures of model nanotubes, i.e., zigzag nanotubes with chirality parameters (14,0) and a corresponding diameter of 1.1 nm were generated using the TubeGen 3.4 tool76 . The length of each CNT was 8 nm. For oxidized CNTs, carboxylated groups were added on both ends (until saturation) and on the surface of the nanotube (randomly) using the Molfacture plugin in VMD visualization software77 (link). The target structure was taken from the x-ray crystal structure of TLR4 (PDB code: 3fxi, chain A) and a 1 Å-spaced grid map with 200 × 200 × 200 points was built around the protein to search for possible binding sites over the entire surface of the target. Gasteiger partial atom charges in both protein and nanotube structures were assigned by using the AutoDock Tools suite program78 (link). Docking simulations were carried out with a maximum number of 1000 generated binding modes. The top orientations were selected with a maximum energy difference of 10 kcal/mol between the best and worst retained binding modes.
3
Molecular Docking of Protein-Ligand Complexes
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Molecular docking runs were performed using the AutoDock4.0 program [47 (link)]. The AutoDock tools suite was used to prepare the proteins and ligands. During the preparation, polar hydrogens were incorporated into the rLiD1 and rHNC models, and Kollman United atom charges and atomic salvation parameters were assigned. SM and LPC structures were extracted from the Pub chem database [48 (link)], and Gasteiger atomic charges were appointed.
Electron affinity and electrostatic potential were calculated with the Autogrid program [47 (link)]. The grid map comprised 85 × 100 × 120 points, with a grid spacing of 0.375 Åusing distance-dependent dielectric constants. Molecular docking was performed using 200 runs of the GA-LS method with a maximum number of energy evaluations of 27,000,000 for each run. After docking, all the generated structures were assigned to clusters based on a tolerance of 2.0Åall-atom root-mean-square deviation (RMSD) from the lowest-energy structure. Specific interactions between the proteins and the possible binding modes were analyzed using UCSF Chimera [49 (link)] and Discovery Studio Visualizer [50 ].
Electron affinity and electrostatic potential were calculated with the Autogrid program [47 (link)]. The grid map comprised 85 × 100 × 120 points, with a grid spacing of 0.375 Åusing distance-dependent dielectric constants. Molecular docking was performed using 200 runs of the GA-LS method with a maximum number of energy evaluations of 27,000,000 for each run. After docking, all the generated structures were assigned to clusters based on a tolerance of 2.0Åall-atom root-mean-square deviation (RMSD) from the lowest-energy structure. Specific interactions between the proteins and the possible binding modes were analyzed using UCSF Chimera [49 (link)] and Discovery Studio Visualizer [50 ].
4
Comprehensive Computational Exploration of DHA Targets
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Several online databases, including TCMSP, DrugBank, David, and STRING were taken to carry out the network pharmacology study. The three-dimensional structures of total 122 potential targets were obtained from the Protein Data Bank (PDB) or AlphaFold Protein Structure Database (Table S8† ), and the coordinates of proteins and essential metal ions only were reserved. All of the structures were obtained by the prepared receptor program from the AutoDockTools suite, and a 60 × 60 × 60 nm3 docking box was set with the center of Top-1 pocket predicted by CavityPlus software. The virtual screening for all targets of DHA was performed by using the high accuracy docking program FIPSDock combined with the iterative anisotropic network model (iterANM)-based docking approach, which would be a significant benefit for increasing the prediction accuracy. In addition, the AI-based target prediction tool MolDesigner package and Daylight-AAC model would verify the potential targets from another perspective. Based on the simulation results from three different angles and also the prediction result from pHDPP, it could identify the potential targets of DHA with higher accuracy.
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