Molecular transport in nanoporous materials

Introduction

This project deals with molecular conformational dynamics, that is, the capacity of molecules, in particular of proteins, to change their shape, that is, their conformation, in time. In the case of proteins, different conformations (or states) trigger different signals at the molecular, cellular, and ultimately physiological level, so that the ability to understand, track, and even manipulate the conformational dynamics of proteins represents a very active field of research. More so in the case of G-proteins and G-protein coupled receptors (GPCRs), where it is intimately related with the discovery and development of medicinal drugs, given that about a quarter of FDA approved drugs target GPCRs [1].

In this project, which resulted in two complementary publications [2,3], we were able to track the conformational life-cycle of a G-protein—GPCR system, namely of the beta-2-adrenergic receptor, an adrenaline-binding receptor, coupled to the stimulatory Gs protein. We did so by combining extensive molecular dynamics (MD) simulations running on the NHR-FAU infrastructure with novel biochemical and biophysical experiments carried out by our collaborators. Thorough analysis of these simulations allowed to predict -and then experimentally validate- key structure-function relationships.

Method

MD simulations are a powerful tool to investigate conformational dynamics with atomic-level detail, simulating molecular behavior at conditions and timescales not accessible to most experiments. While highly CPU and GPU intensive, these the simulations are physically straightforward: the atoms of the molecules are modelled as charged spheres connected to each other with strings, and an equation of motion (Newton’s second, in most cases) is integrated discretely -with other physics-enforcing refinements- by so-called simulation engines, like GROMACS [4], which we used for this project. This produces a trajectory of the system, that represents a plausible way in which a protein would react from a given starting condition, representing different experimental setups. What is more, most of these simulation engines are specifically designed to exploit massively parallel architectures, so that trajectories can be computed much efficiently and robustly at NHR-FAU.

During controlled cell death, the structure of cell membranes, which are mainly made up of a thin layer of lipids and various proteins, is disrupted. This allows inflammation-controlling molecules to escape. The process can be reversed because small pores form in the membrane, surrounded by specific proteins, such as gasdermins.

Gasdermin D and gasdermin A3 are both proteins that can form pores in cell membranes, but they work with different types of lipids. Although scientists have recently determined the structures of gasdermin aggregates that create these membrane pores, the exact process of how the pores form is still not fully understood. So far, two main ideas have been proposed about how gasdermins create these pores. One idea suggests that after being activated, individual gasdermins come together in the cell or on the membrane surface and then insert themselves into the membrane at the same time. The other idea proposes that gasdermins insert into the membrane one by one and then assemble into a functional pore. Using advanced computer simulations that require high-performance computing, we closely study and compare how gasdermin D and A3 interact with a lipid membrane that resembles natural conditions. All-atom molecular dynamics (MD) simulations are a computational technique used to study the behavior of molecules over time by describing the movement of individual atoms by classical physics. In MD simulations, every atom in a molecule is represented, allowing researchers to observe how molecules interact, change shape, and respond to different conditions.

Even though gasdermin D and gasdermin A3 have similar structures, they behave very differently. Our research shows that it takes much less energy for gasdermin D to insert into a membrane—only 2.0 kcal/mol—compared to 5.6 kcal/mol for gasdermin A3. This difference is due to the specific building blocks (amino acids) in each protein, which allow gasdermin D to pull more water into the membrane, form stronger connections, and draw lipid molecules, especially from phosphatidylethanolamine (PE), into the area where it inserts. As a result, gasdermin D is more likely to stay inserted in the membrane before forming pores. In contrast, gasdermin A3 tends to stay on the surface of the membrane rather than inserting itself, making it more likely to follow a different pathway before forming pores. Interestingly, the situation changes dramatically when gasdermin D is studied in a membrane with a different lipid composition, one that lacks PE lipids and has a lot of cholesterol. In this case, individual gasdermin D molecules slip out of the membrane, much like gasdermin A3 does in a natural lipid mixture.

Our findings indicate that both the type of gasdermin and the specific lipids present play important roles in how gasdermin pores form. This, in turn, affects the inflammation process and how it spreads throughout the body.

References of our publications resulting from this computer time project:

1. Vastly different energy landscapes of the membrane insertions of monomeric gasdermin D and A3,  Korn, V. and Pluhackova, K.@ accepted in Communications Chemistry, 2024. DOI: 10.1038/s42004-024-01400-2

2. Entropic barrier of water permeation through single-file channels Wachlmayr, J., Flaeschner, G., Pluhackova, K., Sandtner, W., Siligan, C., Horner, A.@ Communications Chemistry, 2023, 6 (1), 135. https://www.nature.com/articles/s42004-023-00919-0#Sec11

3. Linker-cluster cooperativity in confinement of proline-functionalized Zr-based metal-organic frameworks and its effect on the organocatalytic aldol reaction Dilruba, Z., Yeganeh, A.D., Sofia, K., Noor, S., Shatla, H., Wieland, C., Yu, B.-H., Gugeler, K., Zens, A., K ̈astner, J., Estes, D., Pluhackova, K.@, Krause, S.@ and Laschat, S.@ ChemRxiv, 2024. https://chemrxiv.org/engage/chemrxiv/article-details/671fba9b1fb27ce124dba7ec

4. ART-SM: Boosting Fragment-based Backmapping by Machine Learning C Pfaendner, V Korn, P Gogoi, B Unger, K Pluhackova, ChemRxiv 2024 https://chemrxiv.org/engage/chemrxiv/article-details/662933e421291e5d1d9ab879