New approaches to data-driven analysis and enhanced sampling simulations of G protein-coupled receptors
Time: Wed 2021-06-02 13.00
Subject area: Biological Physics
Doctoral student: Oliver Fleetwood , Biofysik, Science for Life Laboratory, SciLifeLab
Opponent: Professor Ulrich Zachariae, University of Dundee
Supervisor: Universitetslektor Lucie Delemotte, Biofysik, Science for Life Laboratory, SciLifeLab; Professor Erik Lindahl, SeRC - Swedish e-Science Research Centre, Fysik, Science for Life Laboratory, SciLifeLab, Biofysik
Proteins are large biomolecules that carry out specific functions within living organisms. Understanding how proteins function is a massive scientific challenge with a wide area of applications. In particular, by controlling protein function we may develop therapies for many diseases. To understand a protein’s function, we need to consider its full conformational ensemble, and not only a single snapshot of a structure. Allosteric signaling is a factor often driving protein conformation change, where the binding of a molecule to one site triggers a response in another part of the protein. G protein-coupled receptors (GPCRs) are transmembrane proteins that bind molecules outside the membrane, which enables coupling to a G protein in their intracellular domain. Understanding the complex allosteric process governing this mechanism could have a significant impact on the development of novel drugs.
Molecular dynamics (MD) is a computational method that can capture protein conformational change at an atomistic level. However, MD is a computationally expensive approach to simulating proteins, and is thus infeasible for many applications. Enhanced sampling techniques have emerged to reduce the computational cost of standard MD. Another challenge with MD is to extract useful information and distinguish signal from noise in an MD trajectory. Data-driven methods can streamline analysis of protein simulations and improve our understanding of biomolecular systems.
Paper 1 and 2 contain methodological developments to analyze the results of MD in a data-driven manner. We provide methods that create interpretable maps of important molecular features from protein simulations (Paper 1) and identify allosteric communication pathways in biological systems (Paper 2). As a result, more insights can be extracted from MD trajectories. Our approach is generalizable and can become useful to analyze complex simulations of various biomolecular systems.
In Paper 3 and 4, we combine the aforementioned methodological advancements with enhanced sampling techniques to study a prototypical GPCR, the β2 adrenergic receptor. First, we make improvements to the string method with swarms of trajectories and derive the conformational change and free energy along the receptor’s activation pathway. Next, we identify key molecular microswitches directly or allosterically controlled by orthosteric ligands and show how these couple to a shift in probability of the receptor’s active state. In Paper 4, we also find that ligands induce ligand-specific states, and the molecular basis governing these states.
These new approaches generate insights compatible with previous simulation and experimental studies at a relatively low computational cost. Our work also provides new insights into the molecular basis of allosteric communication in membrane proteins, and might become a useful tool in the design of novel GPCR drugs.