Free energy landscapes of membrane proteins: modulation of GPCRs, sugar porters, and voltage-gated ion channels

SNIC 2022/3-12


SNIC Large Compute

Principal Investigator:

Lucie Delemotte


Kungliga Tekniska högskolan

Start Date:


End Date:


Primary Classification:

10603: Biophysics

Secondary Classification:

10407: Theoretical Chemistry



Membrane proteins enable the communication between cells and their environment. To do so, they cycle through a series of conformational states. G-protein coupled receptors bind extracellular ligands to trigger downstream cellular responses to G-protein binding. This is done by cycling between inactive and active states. The state populations and rate of interconversion between states depend on environmental conditions and are modulated by factors such as presence of agonist, lipid environment, amino-acid mutation, etc... Sugar porters import hexoses into the cell, where they can serve as energy source. They do so by assuming an alternative access mechanism: in the outward facing conformation, they can bind the hexose, which catalyzes a transition to the inward facing state. The sugar unbinds towards the intracellular medium and the transporter spontaneously resets to its outward facing state. This transport cycle is modulated by factors such as the lipid composition of the membrane or the identity of the substrate. Voltage-sensor domains (VSDs) confer the ability to sense changes in membrane voltage to membrane proteins. They are crucial for the propagation of electrical signals in excitable cells. We have developed enhanced sampling methodologies that enable us to characterize the conformational landscape using molecular dynamics simulations, and to project these ensembles down to important molecular determinants of the transition. In addition, we have developed analysis methodologies to guide us in our understanding of the molecular basis of these transitions and their modulation. In this proposal, we propose to use these methodologies to study various aspects of modulation of GPCR activation, of the sugar transport cycle and of VSD activation. In particular, we plan on relating sequence to function directly by gathering free energy landscapes of protein activation for entire protein families and relating differences in the free energy maps to sequence determinants using machine learning. All of these projects are important for our understanding of membrane protein function but also pave the way for technological development, namely the development of synthetic proteins with specific activity profiles.