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The search for new compounds with a given biological activity requires enormous effort in terms of manpower and cost. This effort arises from the large number of compounds that need to be synthesized and subsequently biologically evaluated. For this reason the pharmaceutical industry has shown great interest in theoretical methods that enable the rational design of pharmaceutical agents. In the last years bioinformatics has experienced a great evolution due to the development of specialized software and to the increasing computer power. The codification of the structural information of molecules through molecular descriptors and the subsequent data analysis allow establishing QSAR models (Quantitative Structure-Activity Relationship) that can be applied to the design and the virtual screening of new drugs. The development of sophisticated Docking methodologies also allows a more accurate predict of the biological activity of molecules. Moreover, through this type of computational techniques and theoretical approaches, it is possible to develop explanatory hypothesis on the mechanism of action of drugs. This work provides a brief description of a series of studies implemented in the software MOE (Molecular Operating Environment) with particular attention to the medicinal chemistry aspects.
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Molecular Operating Environment (MOE) is a drug discovery software platform that integrates visualization, modeling and simulations, as well as methodology development, in one package. MOE scientific applications are used by biologists, medicinal chemists and computational chemists in pharmaceutical, biotechnology and academic research. MOE runs on Windows, Linux, Unix, and macOS. Main application areas in MOE include structure-based design,[1] fragment-based design,[2] ligand-based design, pharmacophore discovery, medicinal chemistry applications, biologics applications, structural biology and bioinformatics, protein and antibody modeling, molecular modeling and simulations, virtual screening, cheminformatics & QSAR. The Scientific Vector Language (SVL) is the built-in command, scripting and application development language of MOE.
The Molecular Operating Environment was developed by the Chemical Computing Group under the supervision of President/CEO Paul Labute.[3] Founded in 1994[4] and based in Montreal, Quebec, Canada, this private company is dedicated to developing computation software that will challenge, revolutionize, and aid in the scientific methodology. The Chemical Computing Group contains a team of mathematicians, scientists, and software engineers constantly altering and updating MOE in order to improve the fields of theoretical/computational chemistry and biology, molecular modeling, and computer-driven molecular design.[5] Researchers specializing in pharmaceutics (drug-discovery); computational chemistry; biotechnology; bioinformatics; cheminformatics; molecular dynamics, simulations, and modeling are the main clients of the Chemical Computing Group.
As discussed before, MOE is a versatile software with main applications in 3D molecular visualization; structure-based protein-ligand design; antibody and biologics design, structure-based protein engineering; SAR and SPR visualization; ligand-based design; protein, DNA/RNA modeling; virtual screening; 3D pharmacophore screening; fragment-based discovery; structural bioinformatics; molecular mechanics and dynamics; peptide modeling; structural biology; cheminformatics and QSAR.[5]
The protein structure file is downloaded from the PDB and opened in a molecular docking software. There are many programs that can facilitate molecular docking such as AutoDock, DOCK, FlexX, HYDRO, LIGPLOT, SPROUT, STALK,[15] and Molegro Virtual Docker.[16] Alternatively, some protein structures have not been experimentally determined through the use of X-ray crystallography and therefore, are not found on the PDB. In order to produce a protein molecule that can be used for docking, scientists can use the amino acid sequence of a protein and a program named UniProt to find protein structures in the PDB that have similar amino acid sequences.[17] The amino acid sequence of the protein that is being constructed is then used in combination with the protein structure found in the PDB with the highest percent similarity (template protein) in order to create the target protein used in docking. Although this method does not produce an exact model of the target protein, it allows scientists to produce the closest possible structure in order to conduct computational methods and gain some insight into the behavior of a protein. After constructing the necessary molecules for docking, they are imported into a computational docking software such as MOE. In this program, proteins can be visualized and certain parts of the molecule can be isolated in order to obtain more precise data for a region of interest. A cavity, or region where the molecular docking will take place, is set around the binding site, which is the region in the receptor protein where the ligand attaches to. After specifying the cavity, molecular docking settings are configured and the program is run in order to determine the binding energy of the complex.
Moe is free software: you can redistribute it and/or modify it under theterms of the GNU General Public License as published by the FreeSoftware Foundation, either version 2 of the License, or (at youroption) any later version.Valid HTML 4.01 Strict
Ligand-based pharmacophore modeling was used to identify the chemical features responsible for inhibiting tubulin polymerization. A set of 26 training compounds was used to generate hypothetical pharmacophores using the HypoGen algorithm. The structures were further validated using the test set, Fischer randomization method, leave-one-out method and a decoy set, and the best model was chosen to screen the Specs database. Hit compounds were subjected to molecular docking study using a Molecular Operating Environment (MOE) software and to biological evaluation in vitro.
In our study, we successfully used pharmacophore modeling, database screening, and molecular docking approaches to identify potential leads with antitumor activities. A high-correlation quantitative pharmacophore model was generated using the observed structure-activity relationship of known tubulin inhibitors. After validation, this pharmacophore model was used as a 3D structural search query to find new classes of compounds from Specs database. The hit compounds were subjected to molecular docking studies for refinement. The binding free energy and molecular interactions with the active site residues were considered important components when identifying the potential leads.
A Molecular Operating Environment (MOE) (Chemical Computing Group Inc, Montreal, Quebec, Canada) was used for molecular docking. A crystal structure of tubulin, which was obtained at 3.58 Å, was downloaded from the protein data bank (PDB ID: 1SA0). This structure was protonated in the Molecular Operating Environment (MOE)38. The active site was defined with a 6 Å radius around the bound inhibitor (colchicine) in the tubulin crystal structure. The triangle matcher algorithm of the MOE software packages was selected to dock the identified hit compounds into the protein active site. The scoring function must comply with the following parameters: (1) specifying ASE Scoring to rank the poses output by the placement stage; (2) specifying Forcefield Refinement to relax the poses; (3) specifying Affinity dG Scoring to rank the poses using the refinement stage39. The free energy of binding was calculated from the contributions of the hydrophobic, ionic, hydrogen bond, and van der Waals interactions between the protein and the ligand, intramolecular hydrogen bonds and strains of the ligand. We observed that the docking poses were ranked by the binding free energy calculation in the S field.
The steps used during the database screening are shown in Figure 5. First, the concord software was used to convert the two-dimensional structures of the tested compounds in the Specs database into three-dimensional structures with the addition of electric charges. Second, the preliminary screening of drug-like compounds was performed based on Lipinski's rule of five. Consequently, 145 307 drug-like compounds were selected for screening with Hypo1. The 952 compounds mapped on all of the pharmacophoric features present in Hypo1 were finally used in a molecular docking study.
To further refine the retrieved hits and remove the false positives, these 952 compounds, as well as the 26 training-set compounds, were docked into the colchicine-binding site of tubulin (PDB ID: 1SA0) using the Molecular Operating Environment (MOE) software. The binding free energy that distinguishes molecules based on their interacting ability was calculated for all 978 compounds. The highly active compounds in the training set had binding free energy values above -3.931 kcal/mol. Finally, 164 compounds were selected by restricting the binding free energy to
Inclusion complex of fluconazole with β-cyclodextrins (β-CD) were investigated by applying NMR and molecular modelling methods. The 1:1 stoichiometry of FLZ:β-CD complex was determined by continuous variation (Job's plot) method and the overall association constant was determined by using Scott's method. The association constant was determined to be 68.7 M-1 which is consistent with efficient FLZ:β-CD complexation. The shielding of cavity protons of β-CD and deshielding of aromatic protons of FLZ in various1H-NMR experiments show complexation between β-CD and FLZ. Based on spectral data obtained from 2D ROESY, a reasonable geometry for the complex could be proposed implicating the insertion of the m-difluorophenyl ring of FLZ into the wide end of the torus cavity of β-CD. Molecular modelling studies were conducted to further interpret the NMR data. Indeed the best docked complex in terms of binding free energy supports the model proposed from NMR experiments and the m-difluorophenyl ring of FLZ is observed to enter into the torus cavity of β-CD from the wider end. 2ff7e9595c
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