MOE™: Molecular Operating Environment

Structure-Based Design

Macromolecular crystallographic data, when available, can be a valuable source of information for discovering active ligands. MOE provides a collection of applications for visualizing and understanding details of receptor active sites and receptor-ligand interactions. These applications are used to suggest improvements to ligands or screen ligand databases for candidate binders.

Detect and score candidate protein-ligand and protein-protein binding sites using a fast α-shapes algorithm. The sites are scored for Ligand Binding Propensity [Soga 2007]. Visualize individual sites or populate them with “dummy atoms” for docking calculations or starting points for de novo ligand design efforts.

MOE - Structure-Based Design Visualize the residues in close contact with a ligand or series of ligands in diagram format [Clark 2007]. Identify hydrogen bonds, salt bridges, hydrophobic interactions, cation-π, sulfur-LP, halogen bonds and solvent exposure. Browse through a chemical series or receptor family series to identify conserved or non-conserved interactions for selectivity analysis.

CCG has developed (in collaboration with large pharmaceutical companies) a streamlined interface for active site visualization and ligand optimization. A button bar provides applications for structure preparation, active site analysis, molecular property/binding affinity calculations, potential R-group directions and ligand optimization in an active site. Modify ligands using the 3D builder or using 2D sketchers. Visualize and modify aligned complexes, toggle proteins on/off, browse docking poses, pharmacophore hits and adjust rendering with the System Manager.

Build Molecular Surfaces colored by properties. Predict Contact Preferences using statistical knowledge-based potentials and calculate Electrostatic Maps using the non-linear Poisson-Boltzmann equation. Identify steric limits, hot spots and regions for ligand modification and understand receptor recognition preferences.

Calculate water density and binding desolvation penalty maps using 3D-RISM [Kovalenko], a first principles theory of solvation based on the Density Functional Theory of liquids. Detect non-obvious hydrophobic regions of binding sites created by correlation and cavitation effects to prioritize ligand modifications. The method is orders of magnitude faster than alternative methods based on Molecular Dynamics or Monte Carlo.

Grow R-groups, link fragments, transform and replace ligand scaffolds in the context of a receptor active site.  Transform bound molecules using reaction-based bioisostere transformations.  Hybridize multiple molecules to generate new candidate binders.  Search standard 3D databases or special scaffold and linker databases to find novel chemical scaffolds that preserve substituents geometry.  Add additional pharmacophore features to preserve known scaffold interactions or volume constraints to satisfy shape requirements.  Generate custom linker databases with the sdfrag application or use the linker database derived from the CSD (distributed by CCDC).

Dock small molecules in a macromolecular binding site. Supply a database of conformations or generate conformations on the fly. Choose between various scoring functions [Corbeil 2012] and optionally constrain the generated poses to satisfy a pharmacophore query to bias the search towards known important interactions. Use the streamlined scenario-based interface for docking covalent ligands, running electron density guided docking or knowledge-based template guided docking. Refine the poses using a forcefield based method with MM/GBVI [Labute 2008] scoring or a fast grid based method. An interface to third party docking programs is provided for high throughput virtual screening. The docking architecture is parallelized using the MOE/smp technology.

Multi-Fragment Search is an ensemble-based method for mapping the preferred locations of specific chemical groups in a receptor structure [Miranker 1991].  An active site of a macromolecular structure is populated with a large number of chemical fragments, which are subjected to an energy minimization protocol. The resulting group locations are clustered, scored (including solvation effects) and written to a database for subsequent visualization and analysis.

Automatically correct many problems encountered in crystallographic data such as missing loops, empty residues, chain termini or breaks, missing disulfide bonds or atom names, picking alternate conformations, etc using the Structure Preparation application.  Optimize the hydrogen bonding network by navigating through different tautomer/protomer states or automatically by using Protonate3D [Labute 2008].  Protonate3D calculates optimal protonation states, including titration, rotamer and “flips” using a large-scale combinatorial search.


[Corbeil 2012] Corbeil, C.R., Williams, C.I., Labute, P.; Variability in Docking Success Rates Due to Dataset Preparation; J. Comp.-Aided Mol. Des. 26 (2012) 775–786.

[Clark 2007] Clark, A. M., Labute, P.; 2D Depiction of Protein-Ligand Complexes; J. Chem. Inf. Model. 47 (2007) 1933–1944.

[Labute 2005] Labute, P.; On the Perception of Molecules from 3D Atomic Coordinates; J. Chem. Inf. Model. 45 (2005) 215–221.

[Labute 2008] Labute, P.; The Generalized Born / Volume Integral (GB/VI) Implicit Solvent Model: Estimation of the Free Energy of Hydration Using London Dispersion Instead of Atomic Surface Area; J. Comput. Chem. 29 (2008) 1963–1968.

[Labute 2008] Labute, P.; Protonate3D: Assignment of Ionization States and Hydrogen Coordinates to Macromolecular Structures; Proteins 75 (2008) 187–205.

[Miranker 1991] Miranker, A., Karplus, M.; Functionality Maps of Binding Sites: A Multiple Copy Simultaneous Search Method;Proteins: Structure, Function, and Genetics 11 (1991) 29–34.

[Soga 2007] Soga, S., Shirai, H., Kobori, M., Hirayama, N.; Use of Amino Acid Composition to Predict Ligand-Binding Sites;J. Chem. Inf. Model. 47 (2007) 400–406.

[Kovalenko] Kovalenko, A., Hirata, F.; Self-consistent description of a metal--water interface by the Kohn--Sham density functional theory and the three-dimensional reference interaction site model; J. Chem. Physics 110 (1999) 10095-10112. Luchko, T., Gusarov, S. Roe, D. R., Simmerling, C., Case, D. A., Tuszynski, J., Kovalenko, A.; Three-Dimensional Molecular Theory of Solvation Coupled with Molecular Dynamics in Amber; J. Chem. Theory Comput. 6 (2010) 607-624.