Online events hosted by Chemical Computing Group are free but pre-registration is required. Early registration is recommended as space is limited. No previous MOE software experience is required to participate.
In Silico Fragment-Based Drug Design: Approaches and Applications
Scaffold Replacement / Fragment Linking / Ligand Growing / R-Group Screening / Medicinal Chemistry Transformations / Combinatorial Fragment Libraries / Pharmacophore Models / Fragment Databases
Fragment-based drug design (FBDD) is a key approach in the discovery of high-quality drug candidates. As a compliment to biophysical FBDD methods, insilico FBDD uses structure-based approaches to rapidly design and screen large libraries of virtual compounds, allowing for the exploration of a much larger chemical space. The webinar will describe insilico FBDD methods ranging from combinatorial fragment design to scaffold-hopping in the receptor active site, along with approaches for fragment linking and growing. A method for generating a series of closely related derivatives through the reaction-based Combinatorial Library and Medicinal Chemistry Transformations applications in the MOE software will be presented. The use of pharmacophore models and 2D/3D descriptors to guide insilico FBDD processes will also be discussed.
The Application of Docking and Fragment Replacement to Structure-Based Drug Design
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.
Biologics: Protein Alignments, Modeling and Docking
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.
Practical Guide: Set Up MOE High-Performance Computing on AWS Cloud
Running MOE on the AWS cloud might appear intimidating. It involves: installing the server and software, configuring a workload manager, data preparation, data transfer, job submission and data retrieval. In this webinar we will start from a newly created AWS account and show how easy it is to install an elastic compute cluster ready for MOE. Next we will connect the AWS cluster to the MOE HPC infrastructure to submit jobs from different applications (Dock, Protein Design, Ensemble Protein Properties, etc.) right from the graphical user interface of your local MOE installation. We will monitor the jobs and retrieve the results once the calculations have been completed. In the last part of the webinar we then install a single instance of MOE on AWS and use the GUI via the browser.
Quantitative Predictions of Protein Solubility Using a QSPR Approach
Homology Modeling / Protein Properties Calculations / QSAR/QSPR Modeling / Protein Patch Analysis
The course will cover a typical protein/biologics solubility prediction workflow. First, homology models will be generated for a series of Adnectin sequences. Protein properties will then be calculated for the modeled Adnectins, and the correlation between different properties and experimental solubility data will be investigated. Next, QPSR models will be generated to predict solubility based on the calculated protein properties. Finally, Protein Patch Analysis will be used visualize the differences between structures with varying solubility.