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Suite 2020 3

Manufactured by Schrödinger

Suite 2020-3 is a comprehensive software suite designed for molecular modeling and simulation. It includes a collection of tools and algorithms for tasks such as structure preparation, property prediction, and computational chemistry analysis. The suite provides a versatile platform for researchers and scientists working in the field of drug discovery and materials science.

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19 protocols using suite 2020 3

1

Curating and Optimizing Medicinal Plant Ligands for Molecular Docking

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A ligand library of 7,832 compounds from 32 traditionally used medicinal plants of India was curated. To prepare the ligands for Molecular Docking and further studies, a series of activities were executed. The two-dimensional structures of the ligands were converted to their respective 3D counterparts using the LigPrep module of the Schrödinger Suite (Schrödinger, 2017 ). Further, to screen out the unfavourable molecules from the library, Lipinski’s rule of Drug Discovery was used as the criteria (Lipinski, 2004 (link)). Next, energy minimization and geometry optimization were performed for the 3D structures of the ligands, followed by desaltation and correction of chirality. The tautomeric and ionization states were generated between pH 6.8–7.2 using Epik module of Schrödinger suite 2020-3. The ligand structures were minimized using OPLS3e force field in Schrödinger suite 2020-3 until a Root Mean Square Deviation (RMSD) value of 2.0Å was attained. The optimized ligands were docked against the selected receptor proteins.
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2

Structural Insights into SARS-CoV-2 Proteases

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The crystal structure of SARS-CoV-2 main protease in complex with an inhibitor N3 having PDB ID 6LU7 [33 (link)] (resolution 2.16 Å), wild type SARS-CoV-2 papain-like protease (PLPro) with inhibitor GRL0617 having PDB ID 7JRN (resolution 2.48 Å) and ADP ribose phosphatase of NSP3 from SARS-CoV-2 in the complex with ADP ribose having PDB ID 6W02 (resolution 1.5 Å) were downloaded from RCSB Protein Data Bank [34 ] and same were processed through protein preparation wizard of Epic module [35 (link)] of Schrödinger suite 2020-3. The proteins were prepared by removing similar binding sites, unnecessary water molecules and also refining bond orders. Missing chain atoms were added by using the prime module of Schrödinger suite 2020-3. Energy minimization of the proteins was performed using optimized potentials for liquid simulations-3 (OPLS3e) molecular force field with root-mean-square difference (RMSD) of crystallographic heavy atoms kept at 0.3 Å. The ligand binding information of N3, GRL0617 and ligand binding pocket of ADP ribose were used for prediction of the active site of the proteins.
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3

Molecular Docking Analysis Using Glide-Ligand Docking

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The molecular docking analysis was carried out using the Glide-Ligand Docking panel of Maestro 12.5 on Schrödinger Suite 2020-3. The prepared ligands and the receptor grid file were imported into the work space of Maestro. Using standard precision (SP) docking, the ligands were docked into the binding pocket of the target protein. The vdW radius scaling factor was scaled at 0.80 with a partial charge cut-off of 0.15 for ligand atoms, and the ligand sampling method was set to be flexible (32 (link)).
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4

Comprehensive Profiling of Cymbopogon citratus Compounds

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Forty-five (45) bioactive compounds of Cymbopogon citratus were obtained from previous reports and Ethnobotanical Databases [10 (link),11 (link)]. Using the Ligprep panel of Maestro 12.5, Schrödinger Suite 2020-3, the compounds and the standard ligand (9-(3-Iodobenzylamino)-1,2,3,4-tetrahydroacridine or PDB: 1QON) were prepared to obtain low-energy 3D structures with suitable chiralities. The ionization state for each ligand structure was generated at a physiological pH of 7.2 ± 0.2. Stereoisomers of each ligand were computed by retaining specified chiralities while others were varied.
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5

Molecular Docking Analysis with Glide-Ligand

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The molecular docking analysis was carried out using the Glide-Ligand Docking panel of Maestro 12.5 on Schrödinger Suite 2020-3. The prepared ligands and the receptor grid file were imported into the workspace of Maestro, using standard precision (SP) docking, the ligands were docked into the binding pocket of the target protein. The vdW radius scaling factor was scaled at 0.80 with a partial charge cut-off of 0.15 for ligand atoms and the ligand sampling method was set to be flexible.
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6

MM/GBSA Analysis of Teicoplanin Binding

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Post-simulation MM/GBSA analysis was performed by using the thermal_MMGBSA.py script of the Prime/Desmond module of the Schrodinger suite 2020-3 [27 (link),28 (link)]. From each MD trajectory, every 10th frame was extracted from the last 50 ns of simulated trajectories, averaging over 50 frames, for binding free energy calculations of teicoplanin. The Prime MM/GBSA method uses rule of additivity wherein total binding free energy (Kcal/mol) represents a summation of individual energy modules like coulombic, covalent, hydrogen bond, van der Waals, self-contact, lipophilic, solvation, and π-π stackings of ligand and protein.[38] (link)
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7

Binding Energy Estimation Using MM/GBSA

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MM/GBSA technique was exploited as a post-docking validation protocol. The binding energy computed by Prime MM/GBSA of Schrödinger Suite 2020-3 [27 (link),28 (link)] demonstrates an adequate estimation of binding affinity. The MM/GBSA protocol implemented in Prime combines OPLS molecular mechanics energies, a VSGB solvation model for polar solvation (GSGB), and a nonpolar solvation expression (GNP) involving nonpolar solvent-accessible surface area (SASA) and van der Waals interactions [29] (link). For each docked teicoplanin-MPro complex, Prime MM/GBSA estimated the binding free energy (ΔGbind) of teicoplanin according to the equation [30] (link). ΔGbind=ΔEMM+ΔGsolv+ΔGSA
Where, ΔEMM is the difference in energy between the complex structure and the sum of the energies of the protein with and without teicoplanin, ΔGsolv is the difference in the GBSA solvation energy of the teicoplanin-protein complex and the sum of the solvation energies for the teicoplanin-bound and unbound protein, and ΔGSA is the difference in the energy of surface area for the teicoplanin-MPro complex and the sum of the surface area energies for the ligand and un-complexed protein.
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8

Molecular Docking for Lead Identification

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Molecular Docking was performed between the receptor and the optimized ligands in Glide module of Schrödinger Suite 2020-3 using the High Throughput Virtual Screening (HTVS) mode. Taking into consideration the Glide Docking Score, fifty ligands were shortlisted and further subjected to Extra Precision (XP) docking option of Glide (Wilmes et al., 2004 (link); Schrödinger, 2017 ). Prior to performing molecular docking, grid box of the receptor protein structure was defined on the basis of the information of the predicted active site.
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9

Automated Pharmacophore Modeling for Drug Discovery

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The PHASE module of the Schrödinger Suite 2020-3 was used to generate an auto/e-pharmacophore model as previously described (43 (link)). The process was set to generate a maximum of 7 features at a minimum feature–feature distance of 2.00, and the minimum distance between features of the same type was set at 4.00. Donors were set as vectors.
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10

Molecular Docking and Binding Analysis

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The molecular docking analyses were carried out using the Schrödinger Suite 2020-3. To determine the binding sites on the targeted proteins, we utilized the computed Atlas of Surface Topography of Proteins (CASTp) server and constructed a grid box around the identified binding pocket, maintaining the default size of 20 Å. Molecular mechanics generalized born surface area (MM-GBSA) method was used to calculate binding free energy in this study [46 (link)]. The interactions between the ligands and receptors were visualized using Discovery Studio Visualizer v.20.
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