Bioinformatics Bioinformatics Software Molecular Docking


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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. The last years bioinformatics has experienced a great evolution due to the development of specialized software and to the increasing computer power. The development of sophisticated Docking methodologies also allows a more accurate prediction of the biological activity of molecules. Moreover, through this type of computational techniques and theoretical approaches, it is possible to develop explanatory hypotheses 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.

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, fragment-based design, pharmacophore discovery, medicinal chemistry applications, biologics applications, protein and antibody modeling, molecular modeling and simulations, cheminformatics & QSAR. The Scientific Vector Language (SVL) is the built-in command, scripting and application development language of MOE.

The main window of MOE shows the default interface, the top menu bar provides access to a range of applications and tools, below the top menu there is a command line interface that can be used to input SVL commands. The buttons to the right of the rendering area are shortcuts to commands that are often buried several levels down in the top menu system, these are also available by pressing the alt key. These include many of the rendering and labeling options, angle and distance measurements, and access to the molecular builder. At the foot of the window are dials used for rotating, translating and zooming. These actions can also be accessed using the mouse by pressing combinations of the alt, ctrl and shift keys, or using the middle button of a tree-button mouse.

Perhaps one of the most unusual features of MOE is the ability to customize the interface using SVL, either to introduce custom features or to modify the interface for particular users.

To import a structure use the File: Open Menu, MOE can read most common file formats (sdf, SMILES, pdb, mol2) as well as the internal .moe file type. A variety of structure builders are also supplied that can be used to build or edit systems of varying complexity, from small molecules and carbohydrates to proteins and crystals. The molecule builder is limited but functional but does allow the user to enter structures or fragments as SMILES strings.

Once built the structure can be minimized using a variety of force fields including MMFF94, AMBER, CHARMM and semi-empirical energy functions. Conformational analysis using either a systematic or a stochastic search using random rotation of bonds is available.
The MOE molecular databases also provide access to a variety of other tools including sorting and structure based searching and the calculation of a vast range of molecular descriptors. The plot functionality can be used to provide a simple visual comparison of the properties.

Alternatively the calculated descriptors can then be used in a principal component analysis, the results can be displayed as a 3D graph in the rendering window. Clicking a data point on the graph highlights the molecule in the database. The data can also be used to construct a QSAR model including cross-validation and activity prediction. Tools are then available for screening real or virtual libraries to select likely novel active compounds using either binary tree or linear regression models.

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