Deciphering Cellular Networks for Membrane Protein Quality Control Decisions

Informacje na temat projektu

DeCoDe

Identyfikator umowy o grant: 101088970

DOI

10.3030/101088970

Data podpisania przez KE 8 Marca 2023

Data rozpoczęcia 1 Czerwca 2023

Data zakończenia 31 Maja 2028

Finansowanie w ramach

• European Research Council (ERC)

Koszt całkowity

€ 1 975 000,00

Wkład UE

€ 1 975 000,00

Koordynowany przez

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

Periodic Reporting for period 1 - DeCoDe (Deciphering Cellular Networks for Membrane Protein Quality Control Decisions)

Okres sprawozdawczy: 2023-06-01 do 2025-11-30

Podsumowanie kontekstu i ogólnych celów projektu

Cells need to permanently communicate with each other in order to build complex organisms. Membrane proteins that span the multiple lipid bilayers of any eukaryotic cell, allow such cellular interactions to occur. They mediate direct cell:cell contacts and transport matter and information through biological membranes. In order to function properly, membrane proteins need to adopt well-defined structures with atomic precision. This process is complex and error-prone and failures in this process give rise to numerous human diseases from neurological disorders to cancer. The overarching goal of this project is to develop a molecular, mechanistic understanding of the cellular machineries that support and control membrane protein biogenesis in human cells. To achieve this, we use state-of-the-art approaches in cell biology, biochemistry, structural and computational biology. Insights from our project will have a major influence on our understanding of how mammalian cells function at a molecular level and are expected to inspire novel treatment options when it comes to human diseases caused by incorrect structure formation of membrane proteins.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

Our project is divided into three Objectives:

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.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

1. we have for the first time described and characterized a chaperone function for the ER membrane protein complex (published in Nature Communications)
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)