Pharmacophore Modeling / Pharmacophore Consensus / Flexible Alignments / Pharmacophore Searching / Protein-Ligand Interaction Fingerprints (PLIF)

The course describes the application of pharmacophores in drug discovery projects and encompasses a range of topics from pharmacophore query generation to pharmacophore refinement and searching of structural databases. A new approach based on Extended Hückel Theory (EHT) for producing pharmacophore models with encoded interaction energies in the context of ligand-based and structure-based projects will be described. The generation and analysis of protein-ligand interaction fingerprints (PLIF) will be presented along with the application of PLIF for producing pharmacophore queries.

Protein Alignments and Superposition / Loop and Linker Modeling / Homology Modeling / Protein- Protein Docking

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.

Docking / Pharmacophore Modeling / Fragment-Based Design / Scaffold Replacement / Protein-Ligand Interaction Fingerprints (PLIF)

This workshop will cover some advanced workflows for structure-based design in drug discovery projects. The application of pharmacophores in the context of protein-ligand docking and scaffold replacement will be included, as will the generation and analysis of protein-ligand interaction fingerprints (PLIF). Participants will therefore experience a broad range of computational tools that can be brought to bear for lead generation and optimization.

High-Throughput Docking / HPC / SAR Analysis / NMR Spectral Modeling / QM/MM / Molecular Dynamics

The latest release of MOE contains some key upgrades to its comprehensive package of applications for computational small molecule drug discovery. MOE’s docking application has been speeded up and efficiently parallelized, and is now able to take full advantage of HPC and Cloud architectures for high-throughput experiments. MOEsaic, for analyzing and mining small molecule SAR data, now enjoys enhanced prediction capabilities, suggesting compounds that may have more desirable properties given the available data, and sharing MOEsaic projects among teams has been made easier. These new developments, along with others involving MOE’s NMR spectral modeling application, molecular dynamics, a new QM/MM capability, and some user interface enhancements, will be described in this webinar.

Antibody Modeling / Antibody Properties / Developability / Protein Liability / HPC / Protein Family Databases

Improvements in structure and property predictions of proteins and antibodies continue in the new release of MOE. The IMGT scheme for antibody sequence annotation has been implemented, and the antibody modeler application has been enhanced in a number of ways, including its ability to use HPC resources. Conformational ensemble averaging in antibody structures is now used to calculate more accurate descriptors, which may be used to derive more robust models for protein liability and other developability-related properties for biological therapeutics. This webinar will cover these capabilities, along with introducing some new protein family databases.

Inputting Multiple Sequence Data / Mutation Frequencies / Targeted Mutant Libraries / Protein Engineering

Computational techniques for optimizing the affinity of a potential therapeutic protein or antibody are described. Experimental results from phage display may be read into MOE and analyzed to identify promising mutation sites and specific mutations, which can then be explored further individually and in correlation to yield potential new leads with enhanced efficacy. Workflows for these procedures will be described and demonstrated.