Three-dimensional dynamic views of proteomes as a novel readout for physiological and pathological alterations

Informazioni relative al progetto

Proteomes-in-3D

ID dell’accordo di sovvenzione: 866004

DOI

10.3030/866004

Progetto chiuso

Data della firma CE 19 Febbraio 2020

Data di avvio 1 Marzo 2020

Data di completamento 28 Febbraio 2025

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 2 000 000,00

Contributo UE

€ 2 000 000,00

Coordinato da

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Switzerland

Periodic Reporting for period 4 - Proteomes-in-3D (Three-dimensional dynamic views of proteomes as a novel readout for physiological and pathological alterations)

Periodo di rendicontazione: 2024-09-01 al 2025-02-28

Sintesi del contesto e degli obiettivi generali del progetto

Systems biology of proteins classically uses a technique called proteomics, which measures thousands of proteins in a biological system at once. This is very powerful, but it does not look at the structures (i.e. shapes) of these proteins, but only at their amounts. However, protein structures are critically important to understand how proteins function in health or mis-function in disease. In this project, the Picotti lab has addressed this problem by developing an approach to study the structure and function of thousands of proteins at the same time, within their native biological context. The group applied these methods to study protein aggregation (a type of protein structural change), in particular in diseases that are linked to aggregation such as the neurodegenerative Parkinson’s disease (PD). The overall goal of the project was to study protein aggregates or assemblies in healthy physiology and in disease and their modulating factors, develop new approaches to the discovery of disease biomarkers and extend the functional and mechanistic knowledge obtainable from systems biology. The approach provided the community with new methods for global analyses of proteins in cells and tissues, allowed the discovery of a new type of disease biomarkers for neurodegenerative and other diseases and shed light on regulators and mechanisms of protein aggregation.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

We have shown that global protein structural information (ie structures of thousands of proteins) detects multiple functional molecular events in the cell, for example enzyme catalysis and protein-protein interaction (among others). So for the first time, with a single analysis, we can monitor multiple different molecular processes going on in the cell and watch what happens when the cell changes. We have also shown that our global structural readout can be used to help identify drug targets and off-targets in mammalian cells, which is of interest in biopharma. We have pioneered the new concept of structural biomarkers and applied it to the discovery of 70 new candidate biomarkers for Parkinson's disease. Further, we have identified new regulators of protein conformational changes and protein aggregation and are building an open access database providing the community with an unprecedented data on protein dyamics for more than 10,000 proteins. These achievements resulted in a number of awards to the PI leading the projects and to group members and in two patents that have been licensed to an ETH spin-off company.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

Our work demonstrates the new concept that global protein structural analysis is more powerful than classical abundance-based proteomics for learning functional information, at the molecular level, both when studying organisms like yeast cells in the laboratory and in diagnosis and study of human disease. The new approach improve substantially our ability to marry molecular and global understanding of biological systems.We have used the detailed molecular information that we could gain with our approach to characterize a number of biological processes in health and disease.

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