Building stable and accurate FEP models for agonist affinity for GPCRs
Free energy perturbation (FEP) has become the gold standard in binding affinity prediction, and target enablement for the use of this physics-based approach is highly desirable in structure-based drug design. G protein-coupled receptors (GPCRs), which are the target of approximately 35% of approved drugs, represent complex systems for FEP simulations. The rich conformational space of these receptors or the absence of structural waters in many cryo-EM structures are just a few factors that complicate FEP enablement of GPCRs and result in highly sensitive models. Additionally, modeling agonism requires cost-effective solutions to incorporate the effects of G-proteins without resulting in computationally prohibitive simulations.
In this webinar, Ferran Planas, Ph.D., Research Scientist at Lundbeck, will discuss how his team routinely used FEP+ to predict binding affinities for GPCR agonists. He will describe how they built a stable system, which required a combination of careful manual work, the use of Schrödinger tools, and the application of solutions from the literature. The work at Lundbeck highlights the challenges in modeling GPCR agonism but demonstrates that creating a stable FEP+ model is achievable with limited resources.
Webinar Highlights
- Overview of the challenges associated with enabling structure-based design for complex systems
- Case study: Lundbeck’s application of FEP+ to predict binding affinities for GPCR agonists
Who Should Attend
- Scientists looking to enable structure-based design on complex systems
- Scientists looking for strategies to maximize their FEP+ resources
Our Speakers
Ferran Planas
Research Scientist, Molecular Modelling & Structural Biology, Lundbeck
Ferran Planas is a research scientist at Lundbeck in Denmark. He holds a Ph.D. in organic chemistry from Stockholm University, and his research has focused on molecular modeling of proteins. During his Ph.D., he investigated the reaction mechanisms of ThDP-dependent enzymes using DFT. After completing his dissertation, he moved to Denmark for a postdoctoral position at the Technical University of Denmark (DTU). In 2022, he joined the Molecular Modeling and Structural Biology team at Lundbeck, where he supports early- to late-stage pre-clinical pipeline activities. Ferran’s career has been driven by multidisciplinary research, and he has contributed to enabling molecular modeling for medicinal chemists.
Steven Jerome
Executive Director, Life Science Software, Schrödinger
Steven Jerome, Executive Director, Life Science Software at Schrödinger, earned his Ph.D. in Chemistry from Columbia University under the mentorship of Richard Friesner. During his time at Columbia, he contributed to the development of tools for molecular docking and protein structure refinement. Following his doctoral studies, he joined Schrödinger as a scientific developer on the Glide team. Over the years, he has progressed through several roles within the company and now serves as Executive Director. In his current role as the Scientific Lead of Hit Discovery, he oversees the development of a cutting-edge portfolio of computational tools for small molecule hit identification. Additionally, he leads the EU+ Application Science team, directing advanced scientific support for Schrödinger’s commercial customers across Europe.