Periodic Reporting for period 1 - DeCoDe (Deciphering Cellular Networks for Membrane Protein Quality Control Decisions)
Période du rapport: 2023-06-01 au 2025-11-30
Objective 1: Define signatures of intramembrane quality control decisions
For this Objective, our goal is to develop an understanding of what molecular quality control machineries see in their client proteins. How do they dinstinguish between a membrane protein that can still be salvaged from ones that needs to be degraded to avoid harm to the cell? Using systematic cell biological studies combined with machine learning and MD tools we have provided new insights into the "salvage" part of the process. Currently, we are delineating molecular signatures than lead to the degaradation of faulty membrane proteins if they cannot be salvaged.
Objective 2: Dissect chaperone synergies in membrane protein biogenesis
Membrane protein chaperones do not work in isolation, but it is increasingly becoming clear that they form super-complexes to increase effciciency and fidelity of membrane protein biogenesis. Here, we could solve the cryo-EM structure of an ER membrane protein complex centered multi-component chaperone machinery that will inspire how we think about membrane protein biogenesis in eukaryotic cells. We could show that molecular machines with opposing biochemical functions synergistically work together in eukaryotic cells to faciliate and control different steps in membrane protein biogenesis.
Objective 3: Identify novel membrane protein chaperones by functionally validated interactome analyses
Surprisingly, we are still lacking insights into the full arsenal of human membrane protein chaperones and quality control factors. Using well-defined model systems of disease-causing human membrane proteins combined with mass spectrometry, our goal is to define and characterize novel machineries for membrane protein biogenesis and quality control.Here we have completed all the proposed systematic interaction analyses and are currently performing in-depth mechanistic studies on our most promising hits.
2. we have developed a machine learning tool for predicting clients of this chaperone function (published in Nature Communications)
3. we have solved the cryo-EM structure of an ER membrane protein complex-centered multi-subunit molecular machine that establishes a new paradigm in membrane protein quality control (published on biorxive, currently in revision at a leading journal)