Activity Data Mining / SAR / Matched Molecular Pairs / R-group Analysis / R-group Profiling / Free-Wilson Analysis / Compound Suggestions

Matched Molecular Pair analysis on an activity data set enables finding activity cliffs and bioisosteres, providing insight on parts of a molecule that must remain fixed or which can be varied to advance a campaign. If activity against multiple related targets is present, selectivity information can also be gleaned. Analyzing and profiling the functional groups present in a data set can point up useful routes to improving properties such as those relevant for DMPK. This webinar will demonstrate MOEsaic, which enables these analyses in a browser interface which is easy to use and interpret, and which takes analysis one step further by suggesting potential new compounds, not present in the original data sets, which are most likely to display desirable activity and property profiles.

Molecular Surfaces and Maps / Ligand Interactions / Docking / Ligand Optimization / Ligand Selectivity / Protein Alignments and Superposition

The course covers MOE applications for interactive structure based design. Examples include active site visualization, protein-ligand contact analysis and ligand modification/optimization in the receptor pocket. Use of the docking module and its application to assess ligand flexibility will be discussed. A protocol for aligning and superposing protein complexes in the context of protein selectivity will be studied.

Molecular Fingerprints / Pharmacophore Elucidation / 3D Query Generation / Ligand-Based Drug Design / Virtual Pocket, vHTS

Incorporating 3D information into small-molecule drug design can be challenging when structural data for the biological target is absent. Given a series of ligands and associated activity data, we show how reverse fingerprints can identify mportant molecular motifs and generate a consensus pharmacophore query that includes atom-centered and projected features. Ligand shape and excluded volumes features can be derived from the ligand ensemble, and physical properties such as charge and lipophilicity can be mapped from the ligand ensemble onto the ligand shape surface and onto a "pocket surface" created at the ligand shape/excluded volume interface. The resulting "virtual pocket" is a hypothesis of how the ligands appear in the bound state, and what the binding pocket looks like around the bound ligands. The virtual pocket can be used for retrospective analysis or converted to a 3D pharmacophore query that can be used to search databases for new active molecules in a vHTS context.

Protein Alignments and Superposition / Loop and Linker Modeling / Homology Modeling / Protein- Protein Docking / Protein Solubility Analysis / 2D Hot Spot Mapping / PLIF / Biologics QSAR Modeling

The course covers methods for aligning protein sequences, superposing structures, homology modeling fusion proteins and conducting protein-protein docking. In particular, an approach for aligning and superposing multiple structures will be described for determining structural and surface protein variations in relation to protein property modulation. A method for grafting and refining antibody CDR loops as well as using a knowledge-based approach to scFv fusion protein modeling using the MOE linker application will be described. An approach to generate homology models of a murine antigen structure from a human template as well as protein-protein docking of an antibody to an antigen will be discussed. A QSAR model for predicting and analyzing protein/biologics solubility will be described.

Protein Engineering / Protein Properties / Developability / Hot Spot Analysis / Antibody Modeling / Humanization / Molecular Surfaces

The course covers approaches for structure-based antibody design and includes protein-protein interactions analysis, in silico protein engineering, affinity modeling and antibody homology modeling. The interaction of a co-crystallized antibody-antigen complex will be studied by generating and examining the molecular surfaces and visualizing protein-protein interactions in 3D and 2D. Antibody properties will be evaluated using specialized calculated protein property descriptors and analyzing protein patches. The application of protein engineering tools for homology modeling and conducting property optimization of antibodies in the context of developability will be studied. Antibody optimization examples will include identification of glycosylation sites and analysis of correlated pairs using a specialized antibody database. An approach for humanizing antibody homology models will be discussed. All the steps necessary for producing and assessing antibody homology models will be described.

Structure Preparation / Protein Binding Site Visualization / Template-Based Docking / Covalent Docking / Pharmacophores / Protein-Ligand Interaction Fingerprints (PLIF) / Multiple Processor Calculations

Advanced docking workflows encountered in drug discovery projects will be described, which include a range of related topics from the application of pharmacophore constraints in docking to the analysis of protein-ligand interaction fingerprints (PLIF) generated from docked poses. Examples include: (1) general docking with applied pharmacophore constraints, (2) template-based docking using a scaffold or fragment to guide placement, and (3) covalent docking to generate, rank and score ligands that are covalently bound to a protein/receptor. Leveraging multiple processing capacity will also be presented.

Structure Preparation / Force Field / Solvation / AMBER / NAMD / Visualization / Player

Setting up a Molecular Dynamics simulation starting from a protein PDB structure can be challenging. It involves: data preparation, force field parameterization, running protocols and trajectory analysis/visualization. MOE provides a streamlined interface which allows for easy setup, execution and visualization of MD trajectories via AMBER, NAMD or the internal MD engine. In this webinar, the complete workflow to run MD simulations will be introduced.

Conformational Search / Molecular Dynamics / Macrocycles / Loop Modeling

We present a method for conformational search of complex molecular systems such as macrocycles and protein loops. The method is based on perturbing an existing conformation along a molecular dynamics trajectory using initial atomic velocities with kinetic energy concentrated on the low-frequency vibrational modes, followed by energy minimization. A novel Chebyshev polynomial filter is used to heavily dampen the high-frequency components of a randomly generated Maxwell−Boltzmann velocity vector. The method is very efficient, even for large systems; it is straightforward to implement and requires only standard force-field energy and gradient evaluations. The results of several computational experiments suggest that the method is capable of efficiently sampling low-strain energy conformations of complex systems with nontrivial nonbonded interaction networks.