Structure preparation / Interaction analysis / Patch analysis / Protein contacts / Protein properties / Virtual mutagenesis

The webinar covers methods for analyzing and optimizing peptide-protein interactions in the active site. Using a model system consisting of MDMX protein and a peptide ligand modelled from p53, key contacts and interactions will be identified. Properties of the peptide ligand will be calculated, and opportunities for optimisation of the peptide will be derived by calculating protein patches and other properties for the receptor. We will then see how to mutate the residues in the peptide ligand to optimise binding, both by editing one residue at a time, and by "residue scanning" / virtual mutagenesis, which allows us to investigate the effects of multiple mutations together, thereby identifying beneficial changes.

Protein-Protein Interaction Analysis / Molecular Surfaces / Protein Patch Analysis / Protein Properties / Protein Engineering / Antibody Homology Modeling / Antibody Database / Developability Analysis

Protein Alignments and Superposition / Loop and Linker Modeling / Homology Modeling / Protein-Protein Docking / Epitope Analysis / Protein Properties / Protein Solubility Prediction / Protein Patches (2D and 3D) / Biologics QSAR/QSPR Modeling

Structure preparation / Sidechain rotamer exploration / Electron density maps / Solvent analysis with 3D-RISM

The webinar will cover methods for evaluating, analyzing and refining protein models derived from X -ray crystallographic data. Topics related to protein structure preparation and side-chain conformational analysis and placement will be discussed. Visualization and interpretation of electron density maps for assessing protein models will be described. The 3D-RISM method for predicting and refining the placement of solvent molecules in protein models will also be presented.

Targeted Protein Degradation, Heterobifunctional Degraders / Molecular Glues / Ternary Complex Modeling / Protein-Protein Docking

Targeted protein degradation has emerged in recent years as a new modality to control protein levels in vivo. Among the many competing degradation technologies, most research to date has focused on heterobifunctional protein degraders (such as PROTACs), although inherent concerns about the molecular properties of these “large small molecules” has sparked intense interest in the development of smaller degraders, such as molecular glues. As a rule, molecular glues are smaller and are thus more attractive as potential therapeutics – but because a single molecule is responsible for simultaneously binding to two disparate proteins, the rational discovery of molecular glues to date has proven difficult. In this work, we will discuss multiple computational techniques implemented in MOE for modeling ternary complexes containing molecular glues. Special focus will be paid to lessons learned from our well-established PROTAC modeling toolset. In particular, it will be shown that the most effective molecular glue models result from treating molecular glues as “linkerless PROTACs.” Finally, recent work in developing techniques to prioritize prospective molecular glue designs based on their predicted degradation efficacy will be presented.

SAR Analysis / Pharmacophores / Psychedelics / Virtual Screening

Psychedelic compounds are powerful substances that alter cognitive and sensory functions. Psilocybin (magic mushrooms), mescaline (peyote cactus) and Lysergic acid diethylamide (LSD) are tryptamine- and phenethylamine-based classical psychedelic compounds which primarily act as agonists and partial agonists against the serotonin 5-HT2A receptors. The overall hallucinogenic properties of psychedelic compounds are highly sensitive to structural features of the compounds and subject to cross-reactivity with monoaminergic receptors. To probe the SAR space of serotonergic hallucinogens, we have applied matched molecular pairs analysis in conjunction with conformational analysis to determine key pharmacophoric elements that contribute to agonist/partial agonist behavior.