The modeled structure of hNKA was used as the receptor, and the conformations of 1 –11 generated by LigPrep (Schrödinger Release 2018-2: LigPrep, Schrödinger, LLC., New York, NY, USA) were used in molecular docking against the receptor by AutoDock Vina, following our previous procedure [14 (link)]. In brief, the 3D structures of 1 –11 were built in Maestro (Schrödinger Release 2018-2: Maestro, Schrödinger, LLC., New York, NY, USA) and prepared by LigPrep from Schrodinger Suite 2018-2 (Schrödinger Release 2018-2: LigPrep, Schrödinger, LLC., New York, NY, USA). The geometric optimization was performed using the OPLS3 force field with all possible ionization states at pH 7.4 ± 0.1 created by Epik.
Ligprep
Manufactured by Schrödinger
Sourced in United States
LigPrep is a software tool designed to prepare chemical structures for computational modeling and analysis. It performs a variety of structure preparation tasks, including generating 3D molecular structures, adding hydrogen atoms, and adjusting ionization states. LigPrep is a useful tool for preparing chemical compounds for further computational studies.
Automatically generated - may contain errors
Lab products found in correlation
Maestro, by Schrödinger (50 mentions)
Protein preparation wizard, by Schrödinger (47 mentions)
Ligprep module, by Schrödinger (16 mentions)
Macromodel, by Schrödinger (15 mentions)
Qikprop, by Schrödinger (10 mentions)
Glide xp, by Schrödinger (7 mentions)
Prepwizard, by Schrödinger (6 mentions)
Glide module, by Schrödinger (5 mentions)
Maestro 10, by Schrödinger (5 mentions)
Glide software, by Schrödinger (5 mentions)
Maestro suite, by Schrödinger (4 mentions)
Glide sp, by Schrödinger (4 mentions)
Ligprep 2.3 module, by Schrödinger (4 mentions)
Maestro 11, by Schrödinger (4 mentions)
Glide program, by Schrödinger (3 mentions)
Tools, by AutoDock (3 mentions)
Ligprep software, by Schrödinger (3 mentions)
Induced fit docking, by Schrödinger (3 mentions)
Sitemap, by Schrödinger (3 mentions)
Ms jaguar, by Schrödinger (3 mentions)
410 protocols using ligprep
1
Molecular Docking of Ligand Conformations
2
Preparing Ligands for Simulation Studies
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The LigPrep module from Schrodinger suite was employed to prepare all the downloaded ligands for simulation studies (Schrodinger 2017: LigPrep, Schrodinger, LLC). This is an efficient module and prepares one ligand in approximately one second for downstream computational processes. The PubChem compound identity of all the downloaded ligands (DEHP, DINCH, ATBC, and DEHA) are mentioned in Table 1 . This module produces energy-minimized, accurate three-dimensional structures for the ligands and also applies filters to remove those compounds which fail to meet the user-specified criteria. The LigPrep module eliminate mistakes in ligands and corrects Lewis structures. Furthermore, it also produces structural and chemical diversity, such as various stereoisomers, ring conformations, tautomeric and ionization states, etc. from a given input ligand structure. The two-dimensional structures of all the indicated ligands are presented in Figure 1 .
3
Docking of AI-2 to TqsA Protein
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Substrate docking of AI‐2 to the TqsA structure was performed with Glide (Friesner et al, 2004 (link)). The initial coordinates of AI‐2 were taken in the cyclic form from the LsrB structure (PDB ID 6DSP) and an ensemble of conformations was generated using Ligprep (Schrödinger Release 2020‐1: Ligprep, Schrödinger, LLC, New York, NY, 2020). Docking was carried out over a search space of the entire protein volume with 75 runs. In each run, a box size of 15 × 15 × 15 Å for the inner box and 45 × 45 × 45 Å for the outer box were used. All the binding poses obtained from all docking runs (n = 3,950) were pooled together and then clustered using the cluster module of Gromacs program (v2020.2) with the Jarvis–Patrick method and a cut‐off of 4 Å (Abraham et al, 2015 (link)). Clustering resulted in 23 clusters and the most populated cluster comprises of more than 25% of all the binding poses (n = 1,091). This cluster is describing the binding modes near HP1, H5, and H6 in the protomer and has been discussed in the text and the representative binding pose is shown in Fig 5 A. The second (n = 635) and third (n = 553) most populated clusters are located near the loop between H6 and HP2 and the space H6 and HP1, respectively (not shown in the manuscript).
4
Compound Characterization and Antiviral Evaluation
5
Identifying RORγt Inverse Agonists
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RORγt inverse agonists used in the NIB model training with BR-NiB were acquired from the ChEMBL database [46 (link)] (obtained 10 June 2019; http://dude.docking.org ). Only those full orthosteric inverse agonists with reported IC50 values of traceable data origin were included. If several activity measurements existed for the same compound, the most potent IC50 value was selected. The set comprised 191 active compounds. The SPECS 10 mg compound library, consisting of approximately 170,000 compounds (SPECS, The Netherlands; www.specs.net ; obtained 28 June 2019), was used as decoy compounds in VS model optimization (25%) and in actual VS to identify RORγt inverse agonists (100%). This SPECS library was selected for VS due to its suitable size for testing various methods, reasonable pricing, and variability of chemistry. Both ChEMBL and SPECS molecules were processed with LIGPREP (Schrödinger Release 2018-2: LIGPREP, Schrödinger, LLC, New York, NY, 2018) to generate possible tautomers and enantiomers with OPLS3 charges [54 (link)] at pH 7.4 ± 0.0.
6
Ligand Preparation for Molecular Docking
7
Optimizing Antiviral Molecule Structures
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In this study, antiviral, anti-inflammatory, and immunomodulatory molecules were selected from the literature based on their mechanism of action, as provided in Table 1 . The ligands’ structures were downloaded and incorporated into Maestro. The ligands were then optimized using the LigPrep tool of Schrödinger (LigPrep, Schrödinger, LLC, NY, USA, 2020) to obtain appropriate geometry-optimized stable structures with the lowest energy at neutral pH 7.0 [20 (link)].
8
Molecular Docking of J. adhatoda Phytochemicals
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The list of phytochemicals present in J. adhatoda was prepared from the LC-QTOF-MS data. A total of 81 compounds were identified, and from them, 5 most active compounds were selected for docking study. The three-dimensional (3D) conformers of these compounds were downloaded from the PubChem database in SDF format. Similarly, the 3D structure of protein NF-κB (PDB ID: 1LE5) was acquired from the protein data bank (https://www.rcsb.org/ , (accessed on 15 June 2022)). The 3D structures of the ligands were optimized with LigPrep (Schrödinger Release 2022-2: LigPrep, Schrödinger, LLC, New York, NY, USA, 2021), and receptor structure was optimized and prepared for docking using Protein Preparation Wizard. The active site of the proteins was evaluated by SiteMap, and the grid was generated through the Receptor Grid generator. Further, the optimized ligand and the receptor were docked using Ligand Docking in Glide-V9.2. The probable poses were observed through Pose Viewer. The Docking analysis was performed in Schrödinger Release 2022-2: Maestro, Schrödinger, LLC, New York, NY, USA, 2021.
9
Protein and Ligand Preparation for Docking
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Preparation of the compounds for docking included the generation of tautomeric and ionisation states at pH 6–8 and the creation of 3D structures using LigPrep (Schrödinger Release 2021–3: LigPrep, Schrödinger, LLC, New York, NY, 2021). Proteins were prepared by adding H-atoms, protonating side chains at pH = 7, and refining H-bond network with the default settings of Protein Preparation Wizard (Schrödinger Release 2021–3: Protein Preparation Wizard, Schrödinger, LLC, New York, NY, 2021). Ser460 was mutated to Gly for noncovalent docking performed by Glide (Schrödinger Release 2021–3: Glide, Schrödinger, LLC, New York, NY, 2021). Covalent docking was performed by CovDock46 (link).
10
Ursolic acid and uvaol docking
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