Can native peripheral membrane proteins exhibit favorable interactions with liquid ordered phases?

• Applicant: Professor Herre Jelger Risselada, Ph.D.
• Subject Area: Biophysics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 549166051

The discourse surrounding the potential existence of liquid-ordered (Lo) phases within plasma membranes has been a focal point of debate. These Lo phases, characterized by a high concentration of cholesterol and increased rigidity, have been subjects of various theories and arguments, including speculations that they might be model artifacts without much biological relevance. Our team has advanced the field by developing the first peptide sequence that selectively binds to the Lo phase, a discovery corroborated by direct validation through fluorescent microscopy experiments on phase-separated Giant unilamellar vesicles. This peptide was designed to optimize its affinity for cholesterol, showcasing that binding to even the liquid-ordered (Lo) phase becomes preferential if its affinity for cholesterol is sufficiently high. This fundamental discovery suggests that protein-lipid interactions may play an active role in the formation of cholesterol-enriched membrane phases, zones or domains, challenging the prevailing view that their formation is exclusively due to lipid-lipid interactions. This breakthrough implies that similar motifs may exist in naturally occurring proteins. The identification and characterization of liquid-ordered phase favoring sequences within native proteins could have profound implications for understanding protein functionality in living cells. Our primary goal is to determine whether similar motifs can indeed be found in native proteins. To achieve this, we are developing a transformer neural network model to predict liquid-ordered phase binding by quantifying cholesterol affinity. This computational approach will be applied to analyze existing protein databases, identify motifs associated with binding or attraction to cholesterol-enriched phases, and enhance our public webserver for Protein Membrane Interaction prediction (PMIpred).

• DFG Programme: Research Grants